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Wang, Beibei, Binyu Yan, Gwanggil Jeon, Xiaomin Yang, Changjun Liu, and Zhuoyue Zhang. "Lightweight Dual Mutual-Feedback Network for Artificial Intelligence in Medical Image Super-Resolution." Applied Sciences 12, no. 24 (December 13, 2022): 12794. http://dx.doi.org/10.3390/app122412794.
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As a result of hardware resource constraints, it is difficult to obtain medical images with a sufficient resolution to diagnose small lesions. Recently, super-resolution (SR) was introduced into the field of medicine to enhance and restore medical image details so as to help doctors make more accurate diagnoses of lesions. High-frequency information enhances the accuracy of the image reconstruction, which is demonstrated by deep SR networks. However, deep networks are not applicable to resource-constrained medical devices because they have too many parameters, which requires a lot of memory and higher processor computing power. For this reason, a lightweight SR network that demonstrates good performance is needed to improve the resolution of medical images. A feedback mechanism enables the previous layers to perceive high-frequency information of the latter layers, but no new parameters are introduced, which is rarely used in lightweight networks. Therefore, in this work, a lightweight dual mutual-feedback network (DMFN) is proposed for medical image super-resolution, which contains two back-projection units that operate in a dual mutual-feedback manner. The features generated by the up-projection unit are fed back into the down-projection unit and, simultaneously, the features generated by the down-projection unit are fed back into the up-projection unit. Moreover, a contrast-enhanced residual block (CRB) is proposed as each cell block used in projection units, which enhances the pixel contrast in the channel and spatial dimensions. Finally, we designed a unity feedback to down-sample the SR result as the inverse process of SR. Furthermore, we compared it with the input LR to narrow the solution space of the SR function. The final ablation studies and comparison results show that our DMFN performs well without utilizing a large amount of computing resources. Thus, it can be used in resource-constrained medical devices to obtain medical images with better resolutions.
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MOHAMMED ABDUL, AZEEM. "Designing of Medical processor unit for Intelligent network-based Medical usage." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 3 (December 18, 2016): 532. http://dx.doi.org/10.11591/ijeecs.v4.i3.pp532-537.
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<span>This medical design conventions of books and deductive method (MPU). the development of research and the success of many already, we have found the cause of architecture MPU. On the unique features of the processor in question it is coded in different areas of medicine (MOPC). working from a very close bilateral processor MPU. Each issue has a special feature code for the hardware supply chain on the steps and produce a special version of the code and the victim (s). Illness, Doctor MOPC mph dismounted and made a series of sub-processes, and to launch the second law of medical devices. If the computer system of a victim and has a specific digital for logic, and victims of medical devices that operate in the blood, tissues, operating theaters, medical staff, medical costs and variables, etc. We follow the process that the patient design of medical networks and overlapping and development computer.</span>
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V. Ahamed, Syed, and Syed M. Rahman. "Architecture and Design of Medical Processor Units for Medical Networks." International journal of Computer Networks & Communications 2, no. 6 (November 20, 2010): 13–29. http://dx.doi.org/10.5121/ijcnc.2010.2602.
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Yamasaki, Nobuyuki. "Responsive Multithreaded Processor for Distributed Real-Time Systems." Journal of Robotics and Mechatronics 17, no. 2 (April 20, 2005): 130–41. http://dx.doi.org/10.20965/jrm.2005.p0130.
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The Responsive MultiThreaded (RMT) Processor is a system LSI that integrates almost all functions for parallel/distributed real-time systems including robots, intelligent rooms/buildings, ubiquitous computing systems, and amusement systems. Concretely, the RMT Processor integrates real-time processing (RMT Processing Unit), real-time communication (Responsive Link II), computer I/O peripherals (DDR SDRAM I/Fs, DMAC, PCI-X, USB2.0, IEEE1394, etc.), and control I/O peripherals (PWM generators, pulse counters, etc.). The RMT Processor, with a design rule of 0.13<I>μ</I>m CMOS Cu 1P8M and a die size 10.0mm square, was fabricated by TSMC. The RMT Processing Unit (RMT PU) executes eight prioritized threads simultaneously using fine-grained multithreading based on priority, called the RMT architecture. Priority of real-time systems is introduced into all functional units, including cache, fetch, and execution, so the RMT PU guarantees real-time execution of prioritized threads. If resource conflicts occur at functional units, higher priority threads overtake lower priority threads. Flexible powerful vector operation units for multimedia processing are also designed. System designers use on-chip functions easily by connecting required I/Os to this chip and the designers realize distributed control by connecting several RMT Processors with their own functions via Responsive Link II.
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Song, Hong, Xiao Hui Zeng, and Wei Peng Zhou. "The Software Design for an Automation Intelligent Distribution Terminal Unit in Electric Power Systems Based on C and Assembly Language." Advanced Materials Research 676 (March 2013): 302–5. http://dx.doi.org/10.4028/www.scientific.net/amr.676.302.
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Electric power distribution systems play an important role in electric power systems, in which automation intelligent distribution terminal units are critical for the performance of power distribution systems. The software of an automation intelligent electric power distribution terminal unit based on digital signal processor is designed in the paper, a way of admixture programming with C language and assembly language. In this manner, real-time requirement on the electric power distribution system will be satisfied, the reliability and stability of the software are ensured as while. It has a broad application prospects in electric power systems.
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Fountas, Nikolaos A., Constantinos I. Stergiou, Nikolaos M. Vaxevanidis, and Redha Benhadj-Djilali. "A Generic Multi-Axis Post-Processor Engine for Optimal CNC Data Creation and Intelligent Surface Machining." Solid State Phenomena 261 (August 2017): 463–69. http://dx.doi.org/10.4028/www.scientific.net/ssp.261.463.
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This paper focuses on the development of a multi-axis post-processor engine with a curvature-based feed adaptation module, capable of extracting generic CNC data for high precision machining. The motivation of this work stems from the drawback of standard and commercial post-processors to modify their internal source codes so as to be implemented to newly-developed functions which integrate modern CNC units. The multi-axis post-processor proposed in this work operates as a stand-alone function of an artificial intelligent module that optimizes machining parameters for standard swept cut multi-axis surface tool-paths. The post-processor developed receives APT source files previously been optimized by means of a genetic algorithm that handles cutting tool selection; radial cut engagement; maximum discretization step; lead and tilt angles. The algorithm optimizes the aforementioned machining parameters towards the minimization of the number of cutter locations found in a specific APT source file as well as the surface machining error as a combined effect of chordal deviation and scallop height. The final APT output is then properly handled by the post-processor engine so as to extract the final ISO code for a double-pivoted head 5-axis CNC machine and compute optimal values for feed rate in each NC block considering the interpolation error and curvature analysis given the surface properties. To simulate and verify our proposals, the MAZAK Vortex 1000 gantry-type 5-axis CNC machine tool equipped with a Fanuc 15i CNC unit has been selected as the manufacturing resource corresponding to the final CNC output that the proposed post-processor computes. A benchmark sculptured part is created and used for the virtual material removal simulation in CATIA® V5 R18. For that part, both the proposed post-processor engine and a commercially available post-processor were employed to extract G-code data whilst it was shown that identical outputs were obtained.
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PAU, L. F. "AN INTELLIGENT CAMERA FOR ACTIVE VISION." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 01 (February 1996): 33–42. http://dx.doi.org/10.1142/s0218001496000049.
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Much research is currently going on about the processing of one or two-camera imagery, possibly combined with other sensors and actuators, in view of achieving attentive vision, i.e. processing selectively some parts of a scene possibly with another resolution. Attentive vision in turn is an element of active vision where the outcome of the image processing triggers changes in the image acquisition geometry and/or of the environment. Almost all this research is assuming classical imaging, scanning and conversion geometries, such as raster based scanning and processing of several digitized outputs on separate image processing units. A consortium of industrial companies comprising Digital Equipment Europe, Thomson CSF, and a few others, have taken a more radical view of this. To meet active vision requirements in industry, an intelligent camera is being designed and built, comprised of three basic elements: – a unique Thomson CSF CCD sensor architecture with random addressing – the DEC Alpha 21064 275MHz processor chip, sharing the same internal data bus as the digital sensor output – a generic library of basic image manipulation, control and image processing functions, executed right in the sensor-internal bus-processor unit, so that only higher level results or commands get exchanged with the processing environment. Extensions to color imaging (with lower spatial resolution), and to stereo imaging, are relatively straightforward. The basic sensor is 1024*1024 pixels with 2*10 bits addresses, and a 2.5 ms (400 frames/second) image data rate compatible with the Alpha bus and 64 bits addressing. For attentive vision, several connex fields of max 40 000 pixels, min 5*3 pixels, can be read and addressed within each 2.5 ms image frame. There is nondestructive readout, and the image processing addressing over 64 bits shall allow for 8 full pixel readouts in one single word. The main difficulties have been identified as the access and reading delays, the signal levels, and dimensioning of some buffer arrays in the processor. The commercial applications targeted initially will be in industrial inspection, traffic control and document imaging. In all of these fields, selective position dependent processing shall take place, followed by feature dependent processing. Very large savings are expected both in terms of solutions costs to the end users, development time, as well as major performance gains for the ultimate processes. The reader will appreciate that at this stage no further implementation details can be given.
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Huang, Ping, Li Zhu, Qi Wu, and Weishu Hu. "Safety Influencing Factors and Management Countermeasures of Patients Transferred from ICU in Transition Period Based on Intelligent Processor Three-Dimensional Quality Model." Journal of Healthcare Engineering 2022 (January 25, 2022): 1–11. http://dx.doi.org/10.1155/2022/1455830.
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With the development of science and technology of the times, the level of medical care is constantly improving. For patients transferred from ICU, the intelligent processor 3D quality model technology has gradually played an important role in clinical treatment and has become a new type of attention. In order to understand the implementation status of transitional care and the feelings of transitional care for patients transferred from ICU and understand the views of transitional care-related department doctors on transitional patient care and the role that the intelligent processor three-dimensional quality model can play, this article passed a review of the city ICU transferred patients from a hospital that conducted related investigations, reviewed related literature, conducted interviews with professionals, etc., collected relevant information, constructed case templates, and created a clinical research model using comprehensive quantitative and qualitative analysis methods. The results of the study found that, after treatment, patients transferred from the ICU based on the intelligent processor’s three-dimensional quality model have higher physical activity than patients treated by other methods, the ratio is more than 20%, and the postoperative recovery efficiency of patients is higher than 15% and more. This shows that the three-dimensional quality model based on the intelligent processor can improve the important role in the transition period of patients transferred from ICU.
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Saeed, Azzad Bader, and Sabah Abdul-Hassan Gitaffa. "FPGA Based Design of Artificial Neural Processor Used for Wireless Sensor Network." EMITTER International Journal of Engineering Technology 7, no. 1 (June 15, 2019): 200–222. http://dx.doi.org/10.24003/emitter.v7i1.346.
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In this paper, a simulation of artificial intelligent system has been designed for processing the incoming data of sensor units and then presenting proper decision. The Back-propagation Neural Network BPNN has been used as the proposed intelligent system for this work, whereas the BPNN is considered as a trained network in conjunction with an optimization method for changing the weights and biases of the overall network. The main two features of the BPNN are: high speed processing, and producing lowest Mean-Square-Error MSE ( cost function ) in few iterations. The proposed BPNN has used the linear activation functions 'Satlins' and 'Satline' for the hidden and output layer respectively, and has used the training function 'Traingda' ( which is gradient descent with adaptive learning rate) as a powerful learning method. It is worth to mention, that no previous research used these three functions together for such analysis. The MATLAB software package has been used for designing and testing the proposed system. An optimal result has been obtained in this work, where the value of Mean-Square-Error has reached to zero  in 87 epochs, and the real and desired outputs have been fitted. In fact, there is no previous work has reached to this optimal result. The proposed BPNN has been implemented in FPGA, which is fast, and low power tool.
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Wang, Bao Qiang, and Liang Liang Yang. "Design of Embedded Monitored Control System for Ultrasonic Reaction Kettle." Advanced Materials Research 339 (September 2011): 219–22. http://dx.doi.org/10.4028/www.scientific.net/amr.339.219.
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Ultrasonic effect is widely applied in biomaterial treatment and this process will happen in ultrasonic reaction kettle of which physical parameters directly affect the treatment effect. This paper introduces a kind of monitored control system of ultrasonic reaction kettle and mainly presents the realization method of key units. Depending on embedded technology, this system includes three layers control unit: ARM central control layer, PC control layer and bottom CAN intelligent node layer which has sensors and actuators. Linux operating system has been transplanted on ARM processor as the software platform. Through the practical hardware arrangement and software design, it can be proved that this system can effectively and accurately monitored control the parameters of ultrasonic reaction kettle.
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Fujioka, Yoshichika, and Nobuhiro Tomabechi. "Design of a WSI scale parallel processor for intelligent robot control based on a dynamic reconfiguration of multi-operand arithmetic units." Systems and Computers in Japan 31, no. 12 (2000): 33–42. http://dx.doi.org/10.1002/1520-684x(20001115)31:12<33::aid-scj4>3.0.co;2-3.
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Wang, Shiyu, Shengbing Zhang, Xiaoping Huang, and Libo Chang. "Single-chip multi-processing architecture for spaceborne SAR imaging and intelligent processing." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 3 (June 2021): 510–20. http://dx.doi.org/10.1051/jnwpu/20213930510.
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The satellite-borne SAR image intelligent processing system needs to process on-orbit real-time imaging and various tasks of applications, for which reason designing a dedicated high-efficient single-chip multi-processor is of prioritized necessity that can simultaneously satisfy requirements of real-time and low power consumption. Aiming at on-chip data organization and memory access structure, two typical models of SAR(synthetic aperture radar) imaging CSA (chirp scaling) and neural network VGG-11 are analyzed, and then a collaborative computing model for the intelligent processing on remote sensing is extracted. A strip Tile data processing scheme and a dedicated multi-processing architecture is not only proposed, but a data organization and a caching strategy of Tile space synchronization splicing is also presented. In addition, the designed data caching structure among the processing units greatly reduces off-chip access memory bandwidth while supporting parallel pipeline execution of multi-task model. The chip adopts 28 nm CMOS technology featuring with merely 1.83 W of the overall power consumption, whose throughput and energy efficiency reaches 9.89TOPS and 5.4 TOPS/W, respectively. The present architecture can improve real-time performance of the on-orbit remote sensing intelligent processing platform while reducing the complexity of system designing, which is highly adaptive to differentiated expansions according to different models of algorithm.
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Zhang, Kaiyuan, and Yanbin Long. "Research on Data Sharing of Medical Big Data." International Journal of Education and Humanities 2, no. 1 (March 16, 2022): 33–38. http://dx.doi.org/10.54097/ijeh.v2i1.252.
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The invention disclose a data sharing method of medical big data, which comprise that following steps of: obtaining a first rare case through rarity analysis of medical data in a first medical data share platform; Storing the first rare case and correspondingly generating a first retrieval feature; A first search feature, which obtains a first click amount and a first usage amount in a first preset period; According to the first cloud processor, constructing a first access trend prediction model; Inputting the first click amount and the first usage amount into the first access trend prediction model to obtain a first prediction index; Judging whether the first prediction index is in a preset prediction index; If so, data upload is completed on multiple related medical data sharing platforms. It solves the technical problem that the corresponding medical data sharing method is not intelligent enough based on the particularity of rare cases in the prior art, thus causing isolated circulation of rare cases.
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Gennart, B. A., B. Krummenacher, L. Landron, R. D. Hersch, B. Saugy, J. C. Hadorn, and D. Müller. "The Giga View Multiprocessor Multidisk Image Server." Scientific Programming 5, no. 1 (1996): 3–13. http://dx.doi.org/10.1155/1996/680239.
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Professionals in various fields such as medical imaging, biology, and civil engineering require rapid access to huge amounts of pixmap image data. Multimedia interfaces further increase the need for large image databases. To fulfill these requirements, the GigaView parallel image server architecture relies on arrays of intelligent disk nodes, each disk node being composed of one processor and one disk. This contribution reviews the design of the GigaView hardware and file system, compares it to other storage servers available on the market, and evaluates fields of applications for the architecture.
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SHOURAKI, SAEED BAGHERI, NAKAJI HONDA, and GO YUASA. "FUZZY INTERPRETATION OF HUMAN INTELLIGENCE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 04 (August 1999): 407–14. http://dx.doi.org/10.1142/s0218488599000362.
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In this paper, introducing a new hardware concept which has the ability of FUZZY computation in a non-exact naturally FUZZY method, outlines of human brain simulation in hardware level will be presented. It will be shown that a soft computer may be implemented by using unique hardware structures for processor and memory units. Considering the learning procedure of human being and simulating it, using the introduced soft computer, a total energy decreasing trend will be observed in the soft computer structure. The generalization of this trend will be regarded as an equivalent concept for human intelligence. Therefore, complicated concepts such as human intelligence and also imagination and inference procedures may appear as natural abilities of the proposed soft computer. It also will be shown, when starting with observing a phenomenon, the whole procedure, from modeling to controller designing, including the recognition of goal function and effective inputs, may be done automatically and in an intelligent way.
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Yuan, J. "SU-E-I-101: A Toolkit for Automatic 2D/3D Medical Image Registration Using Graphic Processor Units." Medical Physics 38, no. 6Part5 (June 2011): 3419. http://dx.doi.org/10.1118/1.3611675.
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Migal, Vasiliy, Shchasiana Arhun, Andrii Hnatov, Hanna Hnatova, and Pavlo Sokhin. "Intelligent diagnosics of vehicles." Vehicle and electronics. Innovative technologies, no. 22 (December 27, 2022): 72–80. http://dx.doi.org/10.30977/veit.2022.22.0.5.
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Problem. Diagnostics or troubleshooting is an integral part of the operation of automotive technology, and as automotive systems become more complex, the need for diagnostic skills increases, so diagnostic methods by the human senses should be considered an integral part of technical diagnostics at all stages of a vehicle life cycle. Methodology. Analytical methods are used to study the methods of diagnosing vehicles with the help of the intellectual abilities of the operator-diagnostician. Results. The paper shows that the intellectual abilities of the operator-diagnostician play an important role in diagnosing vehicles, the advantages and disadvantages of such diagnostics are presented. The list of basic knowledge necessary for the operator-diagnostician is described as well as the type of operational documentation which is necessary to improve the efficiency of intelligent diagnostics. Intelligent diagnostics of vehicles is divided into stages and shows the wide possibilities of diagnosing by the senses and knowledge of the diagnostician. It is shown that a highly qualified diagnostician can significantly reduce the complexity of diagnosis. With qualified training, experienced mechanics determine up to 70-90% of malfunctions and failures of vehicles and units using organoleptic methods and simple tests. Originality. The stages of intelligent diagnostics of vehicles are singled out and the wide possibilities of diagnosing by the human senses and knowledge of diagnostics at these stages are shown. Practical value. The results of this work are intended for wide use, for example, for drivers, maintenance services, developers of operational and technical documentation, developers involved in the improvement of technical diagnostic tools, machine learning, etc.
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Ibrahim, Atef, and Fayez Gebali. "Word-Based Systolic Processor for Field Multiplication and Squaring Suitable for Cryptographic Processors in Resource-Constrained IoT Systems." Electronics 10, no. 15 (July 25, 2021): 1777. http://dx.doi.org/10.3390/electronics10151777.
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Internet of things (IoT) technology provides practical solutions for a wide range of applications, including but not limited to, smart homes, smart cities, intelligent grid, intelligent transportation, and healthcare. Security and privacy issues in IoT are considered significant challenges that prohibit its utilization in most of these applications, especially relative to healthcare applications. Cryptographic protocols should be applied at the different layers of IoT framework, especially edge devices, to solve all security concerns. Finite-field arithmetic, particularly field multiplication and squaring, represents the core of most cryptographic protocols and their implementation primarily affects protocol performance. In this paper, we present a compact and combined two-dimensional word-based serial-in/serial-out systolic processor for field multiplication and squaring over GF(2m). The proposed structure features design flexibility to manage hardware utilization, execution time, and consumed energy. Application Specific Integrated Circuit (ASIC) Implementation results of the proposed word-serial design and the competitive ones at different embedded word-sizes show that the proposed structure realizes considerable saving in the area and consumed energy, up to 93.7% and 98.2%, respectively. The obtained results enable the implementation of restricted cryptographic primitives in resource-constrained IoT edge devices such as wearable and implantable medical devices, smart cards, and wireless sensor nodes.
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Hussain, Sarwat. "Artificial Intelligence, the Need of the Hour." esculapio 17, no. 1 (March 29, 2021): 1–2. http://dx.doi.org/10.51273/esc21.2517122.
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Fourth Industrial revolution is currently sweeping the high-income countries (HIC) with Artificial Intelli- gence (AI) based automation affecting virtually every aspect of life. The term AI was first coined by McCar- thy in 1956. It was not until 2000s that AI began to thrive. The evolution of AI into the current status occurred in the last decade owing to the enhanced computing power using Graphic Processing Units (GPU), development of high-powered computer languages, and the emergence of the Big Data. The latter is generated through wireless communication between ‘Smart’ sensors/devices and self-learning machines. The word ‘smart’ is applied to any device that has memory and is able to connect with data networks such as the internet and the processors. In the last few years, there has been exponential growth in AI applications. This can be judged by the projec- tion that the AI field will add $ 15 Trillion to global economy, by the year 2030, up from $ 600 Million in 2016. This will occur mostly in the HIC. The adoption of AI by low- and middle-income countries (LMIC) lags far behind that of HICs. The LMICs would miss out in the economic benefits, further widening the global inequalities. Machine Learning and Deep Learning are branches of AI that are beginning to form the basis of the automation of financial and business decisions, and are the tools of self-driving cars, industrial produc- tion, data analytics, quality improvement and health- care processes to name a few. In healthcare, some of the AI applications have shown to enhance patient care, reduce medical errors, support clinical and administrative decision making, automate equipment maintenance and help reduce operational cost. For instance, AI led cost reductions achieved up to 25 percent drop in the length of hospital stay and up to 91 per cent reduction in admissions to step down facili- ties. In the United States alone, by the year 2026, AI in healthcare is estimated to realize $150 billion in annual cost savings.
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Ltifi, Hela, Emna Benmohamed, Christophe Kolski, and Mounir Ben Ayed. "Adapted Visual Analytics Process for Intelligent Decision-Making: Application in a Medical Context." International Journal of Information Technology & Decision Making 19, no. 01 (January 2020): 241–82. http://dx.doi.org/10.1142/s0219622019500470.
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The theoretical and practical researches on Visual Analytics for intelligent decision-making tasks have remarkably advanced in the past few years. Intelligent Decision Support Systems (IDSS) introduce effective and efficient paths from raw data to decision by involving visualization and data mining technologies. Data mining-based DSS produces potentially interesting patterns from data. The transition from extracted patterns to knowledge is a delicate task. In this context, we propose to adapt a common visual analytics process for creating a path that enables the user (decision-maker) to automatically explore and visually extract insights by interacting with the patterns. This proposal is inspired from integrating traditional visual analytics concepts with the mental model of knowledge visualization. The idea is to combine an automatic and visual analysis of patterns to generate knowledge for the purpose of decision-making. To validate our proposal, we have applied it to a medical case study for the fight against Nosocomial Infections in Intensive Care Units. The developed platform was evaluated according to the utility and usability dimensions.
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Li, Man, Brian W. Pickering, Vernon D. Smith, Mirsad Hadzikadic, Ognjen Gajic, and Vitaly Herasevich. "Medical Informatics: An Essential Tool for Health Sciences Research in Acute Care." Bosnian Journal of Basic Medical Sciences 9, no. 1 (October 20, 2009): S34—S39. http://dx.doi.org/10.17305/bjbms.2009.2752.
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Medical Informatics has become an important tool in modern health care practice and research. In the present article we outline the challenges and opportunities associated with the implementation of electronic medical records (EMR) in complex environments such as intensive care units (ICU). We share our initial experience in the design, maintenance and application of a customized critical care, Microsoft SQL based, research warehouse, ICU DataMart. ICU DataMart integrates clinical and administrative data from heterogeneous sources within the EMR to support research and practice improvement in the ICUs. Examples of intelligent alarms – “sniffers”, administrative reports, decision support and clinical research applications are presented.
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Kаrmаzаnovsky, Grigory G., Evgeny V. Kondratyev, Ivan S. Gruzdev, Valeriya S. Tikhonova, Maria Yu Shantarevich, Kseniia A. Zamyatina, Vladislava I. Stashkiv, and Amiran Sh Revishvili. "Modern Radiation Diagnostics and Intelligent Personalized Technologies in Hepatopancreatology." Annals of the Russian academy of medical sciences 77, no. 4 (November 14, 2022): 245–53. http://dx.doi.org/10.15690/vramn2053.
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Timely instrumental diagnosis of diseases of the hepatopancreatoduodenal region, especially of an oncological nature, is the key to successful treatment, improving prognosis and improving the quality of life of patients. At the moment, the possibilities of radiation diagnostics make it possible to identify and evaluate the nature of the blood supply to the neoplasm, its prevalence, cellularity, and in the case of MRI studies with hepatospecific contrast agents, also evaluate the functional activity of liver cells. Nevertheless, the steady development of methods for treating cancer patients, in particular, chemotherapy, and a personalized approach to the choice of patient management tactics require a detailed assessment of the morphological types of certain neoplasms. The need for dynamic monitoring of the results of treatment, monitoring of accidentally detected, potentially malignant neoplasms, and the development of screening programs determine the steady increase in the number of CT and MR examinations performed annually in the world and in our country. These factors have led to the application of texture analysis or radiomics and machine learning algorithms. At the same time, such techniques as radiography, ultrasound, CT and MRI with extracellular and tissue-specific contrast enhancement, and MRI-DWI do not lose their significance. The ongoing research allows the Federal State Budgetary Institution National Medical Research Center of Surgery named after A.V. Vishnevsky of the Ministry of Health of Russia to implement the concept of preoperative non-invasive diagnosis and differential diagnosis of surgical and oncological diseases of the hepatopancreatoduodenal region and apply the knowledge gained in planning surgical treatment. Implementation of the problem of post-processor data processing of radiation diagnostics of surgical and oncological diseases of the hepatopancreatoduodenal region using radiomics and AI technologies is important and extremely relevant for modern medicine.
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Asadina, Habliya, Torib Hamzah, Dyah Titisari, and Bedjo Utomo. "A Centrifuge Calibrator Based on Personal Computer Equipped with Data Processor." Indonesian Journal of electronics, electromedical engineering, and medical informatics 1, no. 1 (August 22, 2019): 14–19. http://dx.doi.org/10.35882/ijeeemi.v1i1.3.
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Calibration is an activity to determine the conventional truth of the value of the appointment of a measuring instrument by comparing traceable standards to national and international standards for measurement and / or international units and certified reference materials. The purpose of this study is to develop a system of efficient and practical centrifuge calibrators by sending the calibration results directly via bluetooth to a PC. The main series of centrifuge calibrators are Arduino modules, laser sensors and Bluetooth.The high low signal is obtained from the reflection of the laser beam aimed at the reflector point on the centrifuge plate, processed in the Arduino module and displayed on the LCD, the calibration results can be directly seen in the Delphi program. The design of this module is also equipped with a Bluetooth transmitter to send data to a PC. This module can be used in medical equipment calibration laboratories. Based on the results of testing and data collection on the 8 Tube centrifuge with a Lutron Tachometer ratio, the error value was 0.0136%. After planning, experimenting, making modules, testing modules, and collecting data, it can be concluded that the tool "centrifuge calibrator equipped with PC-based data processors" can be used and according to planning because the fault tolerance does not exceed 10%.Keywords—Holter Monitor; Heart Monitoring; Arduino Microcontroller; SD Card Memory
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Теплякова, А. Р., and С. О. Старков. "APPLICATION OF COMPUTER VISION FOR DIAGNOSTICS OF NOSOLOGICAL UNITS ON MEDICAL IMAGES." Южно-Сибирский научный вестник, no. 4(44) (August 31, 2022): 134–48. http://dx.doi.org/10.25699/sssb.2022.44.4.004.
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Развитие технологий привело к тому, что множество нозологических единиц может быть диагностировано путём анализа медицинских снимков. С одной стороны, медицинская визуализация играет важную роль в оценке состояния пациентов врачами. С другой стороны, такой вид диагностики предполагает наличие влияния на объективность диагнозов человеческого фактора, так как даже опытные специалисты могут допускать ошибки. Несмотря на то, что интеллектуальные системы для постановки диагнозов по медицинским снимкам в настоящее время чаще всего вызывают недоверие со стороны медицинских работников, их разработка является важной задачей, так как они, хоть и не способны заменить квалифицированного специалиста, могут выступать в качестве его ассистента при постановке диагнозов. В статье приводится классификация медицинских снимков по способу их получения, описываются форматы их хранения и существующие программные модули для работы с ними, производится обзорнозологическихединиц, для диагностики которых могут применяться методы компьютерного зрения, рассматриваются существующие подходы. Основным методом работы является интегративный обзор литературы, полученные результаты необходимы для формирования представления о степени охвата отдельных видов инструментальных исследований с точки зрения методов, разработанных для обработки снимков, получаемых в результате их проведения. Статья отражает основные результаты обзора, проведенного в рамках исследования, целью которого является разработка модулей интеллектуальной системы, способной упрощать процесс диагностики ряда нозологических единиц. Несмотря на большое количество исследований в данной области, существует малое количество комплексных систем, в которых реализованы все стадии: от получения на вход исследований в исходном виде до формирования стандартизированного отчета, содержащего необходимые для подтверждения диагноза врача сведения. Существует ряд направлений, исследования в которых еще не являются многочисленными в силу того, что компьютерное зрение особенно активно развивается последние несколько лет. The development of technology has led to the fact that many nosological units can be diagnosed by analyzing medical images. On the one hand, medical imaging plays an important role in assessing the condition of patients by doctors. On the other hand, this type of diagnosis presupposes the influence of the human factor on the objectivity of diagnoses, since even experienced specialists can make mistakes. Despite the fact that intelligent systems for making diagnoses based on medical images currently most often cause distrust on the part of medical professionals, their development is an important task, since, although they are not able to replace a qualified specialist, they can act as his assistant when making diagnoses. The article provides a classification of medical images by the method of obtaining them, describes their storage formats and existing software modules for working with them. There is also a review of nosological units, for the diagnosis of which computer vision methods can be used, existing approaches are considered. The main method of research is an integrative review of the literature, and its results are necessary to form an idea of the extent of coverage of certain types of instrumental research in terms of methods developed for processing images obtained as a result of their conduct. The article reflects the main results of the review conducted within the framework of the study, the purpose of which is to develop modules of an intelligent system capable of simplifying the process of diagnosing a number of nosological units. Despite the large number of studies in this area, there are a small number of complex systems in which all stages are implemented: from receiving the input of studies in their original form to the formation of a standardized report containing the information necessary to confirm the doctor's diagnosis. There are a number of areas in which research is not yet numerous due to the fact that computer vision has been developing especially actively over the past few years.
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Balasekaran, Gomatheeshwari, Selvakumar Jayakumar, and Rocío Pérez de Prado. "An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning." Energies 14, no. 6 (March 23, 2021): 1788. http://dx.doi.org/10.3390/en14061788.
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With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms). Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.
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Aikhuele, Daniel Osezua, Ayodele A. Periola, Elijah Aigbedion, and Herold U. Nwosu. "Intelligent and Data-Driven Reliability Evaluation Model for Wind Turbine Blades." International Journal of Energy Optimization and Engineering 11, no. 1 (January 2022): 1–20. http://dx.doi.org/10.4018/ijeoe.298694.
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Wind energy is generated via the use of wind blades, turbines and generators that are deployed over a given area. To achieve a higher energy and system reliability, the wind blade and other units of the system must be designed with suitable materials. In this paper however, a computational intelligent model based on an artificial neutral network has been propose for the evaluation of the reliability of the wind turbine blade designed with the FRP material. The simulation results show that there was a reduction in the training mean square error, testing (re–training) mean square error and validation mean square error, when the number of training epochs is increased by 50% such that the minimum mean square error and maximum mean square error were 0.0011 and 0.0061, respectively. The low validation mean square error in the simulation results implies that the developed artificial neural network has a good accuracy when determining the reliability and the failure probability of the wind turbine blade.
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Thilagaraj, M., B. Dwarakanath, V. Pandimurugan, P. Naveen, M. S. Hema, S. Hariharasitaraman, N. Arunkumar, and Petchinathan Govindan. "A Novel Intelligent Hybrid Optimized Analytics and Streaming Engine for Medical Big Data." Computational and Mathematical Methods in Medicine 2022 (March 17, 2022): 1–11. http://dx.doi.org/10.1155/2022/7120983.
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Medical data processing is exponentially increasing day by day due to the frequent demand for many applications. Healthcare data is one such field, which is dynamically growing day by day. In today’s scenario, an enormous amount of sensing devices and data collection units have been employed to generate and collect medical data all over the world. These healthcare devices will result in big real-time data streams. Hence, healthcare-based big data analytics and monitoring have gained hawk-eye importance but needs improvisation. Recently, machine and deep learning algorithms have gained importance to analyze huge amounts of medical data, extract the information, and even predict the future insights of diseases and also cope with the huge volume of data. But applying the learning models to handle big/medical data streams remains to be a challenge among the researchers. This paper proposes the novel deep learning electronic record search engine algorithm (ERSEA) along with firefly optimized long short-term memory (LSTM) model for better data analytics and monitoring. The experimentations have been carried out using Apache Spark using the different medical respiratory data. Finally, the proposed framework results are contrasted with existing models. It shows the accuracy, sensitivity, and specificity like 94%, 93.5%, and 94% for less than 5 GB dataset, and also, more than 5 GB it provides 94%, 92%, and 93% to prove the extraordinary performance of the proposed framework.
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Bakhmutov, S. V., S. P. Chuprunov, and V. V. Debelov. "IASF-2022 results." Trudy NAMI, no. 4 (January 3, 2023): 6–14. http://dx.doi.org/10.51187/0135-3152-2022-4-6-14.
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On 18–19 October 2022, the annual International Automobile Scientific Forum (IASF-2022) “Sustainable Development of Russian Automotive Industry under Present-Day Conditions” took place. The event format assumed both offline and online participation. The issues of providing technological sovereignty of the Russian automotive industry and development of domestic component base, technical and technological developments in the field of innovative land vehicles and systems were discussed during the Forum. Within the Forum, an exhibition of technical achievements of FSUE “NAMI” and manufacturers of automotive vehicles and components was held dedicated to the domestic innovative solutions in terms of vehicle decarbonization, highly automated intelligent vehicles and intelligent control systems. Components of electric, hybrid and intelligent vehicles, electronic control units, assemblies and aggregates of powertrains, electric traction vehicles and hybrid vehicles with additional power sources were also presented. The competition was held in two categories: “Best Student Scientific Paper” and “Best Graduate Student and Young Specialist Scientific Paper”. 18 applications were fi led for the competition from the educational, scientific and production companies and organizations.
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Ahmed, Moustafa, Yas Al-Hadeethi, Ahmed Bakry, Hamed Dalir, and Volker J. Sorger. "Integrated photonic FFT for photonic tensor operations towards efficient and high-speed neural networks." Nanophotonics 9, no. 13 (June 26, 2020): 4097–108. http://dx.doi.org/10.1515/nanoph-2020-0055.
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AbstractThe technologically-relevant task of feature extraction from data performed in deep-learning systems is routinely accomplished as repeated fast Fourier transforms (FFT) electronically in prevalent domain-specific architectures such as in graphics processing units (GPU). However, electronics systems are limited with respect to power dissipation and delay, due to wire-charging challenges related to interconnect capacitance. Here we present a silicon photonics-based architecture for convolutional neural networks that harnesses the phase property of light to perform FFTs efficiently by executing the convolution as a multiplication in the Fourier-domain. The algorithmic executing time is determined by the time-of-flight of the signal through this photonic reconfigurable passive FFT ‘filter’ circuit and is on the order of 10’s of picosecond short. A sensitivity analysis shows that this optical processor must be thermally phase stabilized corresponding to a few degrees. Furthermore, we find that for a small sample number, the obtainable number of convolutions per {time, power, and chip area) outperforms GPUs by about two orders of magnitude. Lastly, we show that, conceptually, the optical FFT and convolution-processing performance is indeed directly linked to optoelectronic device-level, and improvements in plasmonics, metamaterials or nanophotonics are fueling next generation densely interconnected intelligent photonic circuits with relevance for edge-computing 5G networks by processing tensor operations optically.
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Ayyasamy, S. "Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare." December 2021 3, no. 4 (November 26, 2021): 305–16. http://dx.doi.org/10.36548/jaicn.2021.4.003.
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Recently, the development and integration of various sensor control with smart intelligent unit is used in medical field through IoT. However, there is still a lot of space for growth in the medical and health industry's use of new technology. The traditional nurse care unit is managed through medical staffs, and the expanding medical demands creates the hospital’s patients records to be updated inefficiently. Since this is now an urgent need, developing a realistic, smart medical nursing care unit at low cost with a system capable of facilitating the effective and convenient administration of medical staff has taken a new significance. The proposed framework, conducted in the analysis to monitor medical records and activities of the emergency care unit patients, functions as a nurse and gives patients the nurse care satisfaction. The patients' actual location may be obtained for the first time by cloud computing based smart system. The precise location of the patient is critical to rescue the patient in emergency situation. This research work illustrates that the intelligent nurse care unit is the main phase called Smart Medical Nursing Care (SMNC). It contains several sensor units and by the combination of many sensors in the sensor module, it takes very less reaction time to connect or communicate both sides i.e., between patients and medical staffs.
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Mane, D. T., and U. V. Kulkarni. "A Survey on Supervised Convolutional Neural Network and Its Major Applications." International Journal of Rough Sets and Data Analysis 4, no. 3 (July 2017): 71–82. http://dx.doi.org/10.4018/ijrsda.2017070105.
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With the advances in the computer science field, various new data science techniques have been emerged. Convolutional Neural Network (CNN) is one of the Deep Learning techniques which have captured lots of attention as far as real world applications are considered. It is nothing but the multilayer architecture with hidden computational power which detects features itself. It doesn't require any handcrafted features. The remarkable increase in the computational power of Convolutional Neural Network is due to the use of Graphics processor units, parallel computing, also the availability of large amount of data in various variety forms. This paper gives the broad view of various supervised Convolutional Neural Network applications with its salient features in the fields, mainly Computer vision for Pattern and Object Detection, Natural Language Processing, Speech Recognition, Medical image analysis.
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Bidgar, Neha Shivaji. "Fast, Intelligent and Secure an Embedded Health-care Supervisory System." International Journal of Reconfigurable and Embedded Systems (IJRES) 5, no. 2 (August 21, 2016): 77. http://dx.doi.org/10.11591/ijres.v5.i2.pp77-84.
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<p class="IEEEAbtract">The world of medical supervisory system should remotely monitor various parameters of patients with help of electronics. This work aims at monitoring parameters in fast, intelligent and secure way. The work emphasizes on designing an embedded system which handles critical parameters of patients in hospital/nursing units. The data samples are processed using an ARM based CPU’s and to achieve performance metrics TI RTOS is used and validated. The system generates interrupts based on the priority of each of critical parameters with a threshold in it and enables alarming/warning system. The data is then transferred to IoT layer using a CC3100 TI based SoC for further reference and processing.</p>In this paper, 4 parameters has been monitored for designing system such as measuring Respiratory rate for human breath, MMG signal for muscle movement, Blood pressure rate as well as Temperature. RTOS helps with scheduling as well as with interrupts also helps Wi-Fi module to work with it. Wi-Fi module provides many security options as WEP, WAP, and WAP2.
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Siv-Lee, Lor, and Linda Morgan. "Implementation of Wireless “Intelligent” Pump IV Infusion Technology in a Not-for-Profit Academic Hospital Setting." Hospital Pharmacy 42, no. 9 (September 2007): 832–40. http://dx.doi.org/10.1310/hpj4209-832.
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Purpose This paper describes the implementation of wireless “intelligent” pump intravenous (IV) infusion technology in a not-for-profit academic, multicampus hospital system in the United States. Methods The process of implementing a novel infusion system in a multicampus health care institution (main campus plus three satellite campuses) is described. Details are provided regarding the timelines involved, the process for the development of the drug libraries, and the initial implementation within and across campuses. Results In early 2004, with the end of the device purchase contract period nearing, a multidisciplinary committee evaluated potential IV infusion pumps for hospital use. In April 2004, the committee selected the Plum A+ infusion system with Hospira MedNet software and wireless capabilities (Hospira Inc., Lake Forest, IL). Implementation of the single-channel IV infusion system took place July through October 2005 following installation of the wireless infrastructure throughout the multicampus facility. Implementation occurred in July, one campus at a time; the three smaller satellite campuses went “live” before the main campus. Implementation of the triple-channel IV infusion system took place in March 2006 when the wireless infrastructure was completed and fully functional throughout the campuses, software was upgraded, and drug library revisions were completed and uploaded. Conclusion “Intelligent” pump technology provided a framework to standardize drug concentrations used in the intensive care units. Implementation occurred transparently without any compromise of patient care. Many lessons were learned during implementation that explained the initial suboptimal compliance with safety software use. In response, the committee developed strategies to increase software utilization rates, which resulted in improved acceptance by nursing staff and steadily improving compliance rates. Wireless technology has supported remote device management, prospective monitoring, the avoidance of medication error, and the timely education of health care professionals regarding potential medication errors.
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Sun, Jun, and Qiulan Luo. "Research on Application of Health Medical Information Science Data Sharing Standard System in Sports Rehabilitation." Journal of Medical Imaging and Health Informatics 11, no. 3 (March 1, 2021): 996–1003. http://dx.doi.org/10.1166/jmihi.2021.3352.
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The development of medical information technology has rapidly promoted the development of medical information technology towards intelligence. Health medical data provides basic data resource protection for intelligent medical services and smart medical services. This article abstracts the typical models of domestic and international health medical information management services, and provides theoretical basis and practical reference for the building of an evaluation index system for health management service capabilities. An system for health management service capabilities under a data sharing standard system was constructed, and the status and linkage of health management services were investigated and comprehensively evaluated to provide an index system and empirical data for evaluating hospital health management service capabilities. Finally, analyze the advantages and disadvantages of health management services under the medical consortium framework, propose countermeasures to improve the health management service system, provide decision-making references for units to improve their health management service capabilities, optimize health management service models, and formulate health management services for relevant government departments Provisional policies to promote the implementation of health management.
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Cheng, Jiangzhou, Cai Zhu, Wenlong Fu, Canxia Wang, and Jing Sun. "An Imitation medical diagnosis method of hydro-turbine generating unit based on Bayesian network." Transactions of the Institute of Measurement and Control 41, no. 12 (February 10, 2019): 3406–20. http://dx.doi.org/10.1177/0142331219826665.
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In order to improve the intelligent level of fault diagnosis and condition maintenance of hydropower units, an Imitation medical diagnosis method (IMDM) is proposed in this study. IMDM uses Bayesian networks (BN) as the technical framework, including three components: machine learning BN model, expert empirical BN model, and maintenance decision model. Its characteristics are as follows: (i) the machine learning model uses a new node selection method to solve the problem that the traditional fault diagnosis model is difficult to connect with the state monitoring system. (ii) The expert experience BN model improves the traditional method: using the fault tree model to transform the BN structure, Noisy-Or model to simplify conditional probability table, and fuzzy comprehensive evaluation method to obtain the conditional probability. (iii) By introducing the expected utility theory, a maintenance decision model is innovated, which makes sure the optimal maintenance decision scheme after the fault can be better selected. The performance of this proposed method is evaluated by using the experimental data. The results show that the accuracy of the fault reasoning model is higher than 80%, and the maintenance decision model successfully selects 236 optimal maintenance decision schemes from 3159 schemes generated by 13 faults.
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Shi, Yangyang, Jingou Liang, Xuedong Zhou, Biao Ren, Haohao Wang, Qi Han, Hao Li, and Lei Cheng. "Effects of a Novel, Intelligent, pH-Responsive Resin Adhesive on Cariogenic Biofilms In Vitro." Pathogens 11, no. 9 (September 5, 2022): 1014. http://dx.doi.org/10.3390/pathogens11091014.
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Background: Secondary caries often result in a high failure rate of resin composite restoration. Herein, we studied the dodecylmethylaminoethyl methacrylate–modified resin adhesive (DMAEM@RA) to investigate its pH-responsive antimicrobial effect on Streptococcus mutans and Candida albicans dual-species biofilms and on secondary caries. Methods: Firstly, the pH-responsive antimicrobial experiments including colony-forming units, scanning electron microscopy and exopoly-saccharide staining were measured. Secondly, lactic acid measurement and transverse microradiography analysis were performed to determine the preventive effect of DMAEM@RA on secondary caries. Lastly, quantitative real-time PCR was applied to investigate the antimicrobial effect of DMAEM@RA on cariogenic virulence genes. Results: DMAEM@RA significantly inhibited the growth, EPS, and acid production of Streptococcus mutans and Candida albicans dual-species biofilms under acidic environments (p < 0.05). Moreover, at pH 5 and 5.5, DMAEM@RA remarkably decreased the mineral loss and lesion depth of tooth hard tissue (p < 0.05) and down-regulated the expression of cariogenic genes, virulence-associated genes, and pH-regulated genes of dual-species biofilms (p < 0.05). Conclusions: DMAEM@RA played an antibiofilm role on Streptococcus mutans and Candida albicans dual-species biofilms, prevented the demineralization process, and attenuated cariogenic virulence in a pH-dependent manner.
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Farhy, Yael, João Veríssimo, and Harald Clahsen. "Universal and particular in morphological processing: Evidence from Hebrew." Quarterly Journal of Experimental Psychology 71, no. 5 (January 1, 2018): 1125–33. http://dx.doi.org/10.1080/17470218.2017.1310917.
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Do properties of individual languages shape the mechanisms by which they are processed? By virtue of their non-concatenative morphological structure, the recognition of complex words in Semitic languages has been argued to rely strongly on morphological information and on decomposition into root and pattern constituents. Here, we report results from a masked priming experiment in Hebrew in which we contrasted verb forms belonging to two morphological classes, Paal and Piel, which display similar properties, but crucially differ on whether they are extended to novel verbs. Verbs from the open-class Piel elicited familiar root priming effects, but verbs from the closed-class Paal did not. Our findings indicate that, similarly to other (e.g., Indo-European) languages, down-to-the-root decomposition in Hebrew does not apply to stems of non-productive verbal classes. We conclude that the Semitic word processor is less unique than previously thought: Although it operates on morphological units that are combined in a non-linear way, it engages the same universal mechanisms of storage and computation as those seen in other languages.
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Leonardi, Luca, Lucia Lo Bello, Gaetano Patti, and Orazio Ragusa. "A Network Architecture and Routing Protocol for the MEDIcal WARNing System." Journal of Sensor and Actuator Networks 10, no. 3 (June 30, 2021): 44. http://dx.doi.org/10.3390/jsan10030044.
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The MEDIcal WARNing (MEDIWARN) system continuously and automatically monitors the vital parameters of pre-intensive care hospitalized patients and, thanks to an intelligent processing system, provides the medical teams with a better understanding of their patients’ clinical condition, thus enabling a prompt reaction to any change. Since the hospital units generally lack a wired infrastructure, a wireless network is required to collect sensor data in a server for processing purposes. This work presents the MEDIWARN communication system, addressing both the network architecture and a simple, lightweight and configurable routing protocol that fits the system requirements, such as the ability to offer path redundancy and mobility support without significantly increasing the network workload and latency. The novel protocol, called the MultiPath Routing Protocol for MEDIWARN (MP-RPM), was therefore designed as a solution to support low-latency reliable transmissions on a dynamic network while limiting the network overhead due to the control messages. The paper describes the MEDIWARN communication system and addresses the experimental performance evaluation of an implementation in a real use-case scenario. Moreover, the work discusses a simulative assessment of the MEDIWARN communication system performance obtained using different routing protocols. In particular, the timeliness and reliability results obtained by the MP-RPM routing protocol are compared with those obtained by two widely adopted routing protocols, i.e., the Ad-hoc On-demand Distance Vector (AODV) and the Destination-Sequenced Distance-Vector Routing (DSDV).
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Qin, Yuxiao, Li Sun, and Qingsong Hua. "Environmental Health Oriented Optimal Temperature Control for Refrigeration Systems Based on a Fruit Fly Intelligent Algorithm." International Journal of Environmental Research and Public Health 15, no. 12 (December 14, 2018): 2865. http://dx.doi.org/10.3390/ijerph15122865.
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The recent decades have witnessed refrigeration systems playing an important role in the life of human beings, with wide applications in various fields, including building comfort, food storage, food transportation and the medical special care units. However, if the temperature is not controlled well, it will lead to many harmful public health effects, such as the human being catching colds, food spoilage and harm to the recovering patients. Besides, refrigeration systems consume a significant portion of the whole society’s electricity usage, which consequently contributes a considerable amount of carbon emissions into the public environment. In order to protect human health and improve the energy efficiency, an optimal control strategy is designed in this paper with the following steps: (1) identifying the refrigeration system model based on a least squares method; (2) tuning an initial group of parameters of the proportional-integral-derivative (PID) controller via the pidTuner Toolbox of Matlab; (3) using an intelligent algorithm, namely fruit fly optimization (FOA), to further optimize the parameters of the PID controller. By comparing the optimal PID controller and the controller provided in the reference, the simulation results demonstrate that the proposed optimal PID controller can produce a more controllable temperature, with less tacking overshoot, less settling time, and more stable performance under a constant set-point.
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Yue, Weiqi, Lijuan Zhang, Lei Zhang, Jie Huang, Jian Wan, and Naixue Xiong. "Med-Tree: A Medical Ontology Tree Combined with the Graph Attention Networks for Medication Recommendation." Electronics 11, no. 21 (October 31, 2022): 3558. http://dx.doi.org/10.3390/electronics11213558.
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Medication recommendation based on Electronic Health Records (EHRs) is a significant research direction in the field of intelligent medicine, which aims to recommend personalized medication combinations for patients based on their historical and current physical conditions. However, since the structural and temporal characteristics of medical records are affected by many uncertain factors, there are many limitations in medication recommendation methods based on EHRs. Specifically, most existing works either fail to adequately assess the structural correlation and temporal dependency among various medical entities or ignore existing knowledge of Drug–Drug Interactions (DDI), which could lead to adverse outcomes. These factors contribute to poor recommendation quality. Therefore, we propose a medical ontology tree model combined with the Graph Attention Networks (GAT) for medication recommendations. First, the class hierarchy extracted from the medical ontology and the GAT model is used to learn the ICD-9 codes of diagnoses and procedures, which enriches the semantic representation of medical entities. Secondly, Gate Recurrent Units (GRU) are used to learn the temporal characteristics of medical entities. Finally, memory bank, dynamic memory and DDI graph are used to optimize the hidden layer results, which improve the accuracy of the model. Experimental results show that the proposed model is superior to the previous methods in all evaluation indicators, and the recommended results have a lower DDI rate.
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Xu, Baochun, Yu Wang, Haoao Cui, Haoran Niu, Yijian Liu, Zhongli Li, and Da Chen. "Full Soft Capacitive Omnidirectional Tactile Sensor Based on Micro-Spines Electrode and Hemispheric Dielectric Structure." Biosensors 12, no. 7 (July 10, 2022): 506. http://dx.doi.org/10.3390/bios12070506.
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Flourishing in recent years, intelligent electronics is desirably pursued in many fields including bio-symbiotic, human physiology regulatory, robot operation, and human–computer interaction. To support this appealing vision, human-like tactile perception is urgently necessary for dexterous object manipulation. In particular, the real-time force perception with strength and orientation simultaneously is critical for intelligent electronic skin. However, it is still very challenging to achieve directional tactile sensing that has eminent properties, and at the same time, has the feasibility for scale expansion. Here, a fully soft capacitive omnidirectional tactile (ODT) sensor was developed based on the structure of MWCNTs coated stripe electrode and Ecoflex hemisphere array dielectric. The theoretical analysis of this structure was conducted for omnidirectional force detection by finite element simulation. Combined with the micro-spine and the hemispheric hills dielectric structure, this sensing structure could achieve omnidirectional detection with high sensitivity (0.306 ± 0.001 kPa−1 under 10 kPa) and a wide response range (2.55 Pa to 160 kPa). Moreover, to overcome the inherent disunity in flexible sensor units due to nano-materials and polymer, machine learning approaches were introduced as a prospective technical routing to recognize various loading angles and finally performed more than 99% recognition accuracy. The practical validity of the design was demonstrated by the detection of human motion, physiological activities, and gripping of a cup, which was evident to have great potential for tactile e-skin for digital medical and soft robotics.
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Shukla, Piyush, Oluwatobi Akanbi, Asakipaam Simon Atuah, Amer Aljaedi, Mohamed Bouye, and Shakti Sharma. "Cryptography-Based Medical Signal Securing Using Improved Variation Mode Decomposition with Machine Learning Techniques." Computational Intelligence and Neuroscience 2022 (September 12, 2022): 1–13. http://dx.doi.org/10.1155/2022/7307552.
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There is no question about the value that digital signal processing brings to the area of biomedical research. DSP processors are used to sample and process the analog inputs that are received from a human organ. These inputs come from the organ itself. DSP processors, because of their multidimensional data processing nature, are the electrical components that take up the greatest space and use the most power. In this age of digital technology and electronic gizmos, portable biomedical devices represent an essential step forward in technological advancement. Electrocardiogram (ECG) units are among the most common types of biomedical equipment, and their functions are absolutely necessary to the process of saving human life. In the latter part of the 1990s, portable electrocardiogram (ECG) devices began to appear on the market, and research into their signal processing and electronics design capabilities continues today. System-on-chip (SoC) design refers to the process through which the separate computing components of a DSP unit are combined onto a single chip in order to achieve greater power and space efficiency. In the design of biomedical DSP devices, this body of research presents a number of different solutions for reducing power consumption and space requirements. Using serial or parallel data buses, which are often the region that consumes the most power, it is possible to send data between the system-on-chip (SoC) and other components. To cut down on the number of needless switching operations that take place during data transmission, a hybrid solution that makes use of the shift invert bus encoding scheme has been developed. Using a phase-encoded shift invert bus encoding approach, which embeds the two-bit indication lines into a single-bit encoded line, is one way to solve the issue of having two distinct indicator bits. This method reduces the problem. The PESHINV approach is compared to the SHINV method that already exists, and the comparison reveals that the suggested PESHINV method reduces the total power consumption of the encoding circuit by around 30 percent. The computing unit of the DSP processor is the target of further optimization efforts. Virtually, all signal processing methods need memory and multiplier circuits to function properly.
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Li, Zongying, Cheng Xing, Tingting Li, Linxiang Du, and Na Wang. "Hypochloremia is associated with increased risk of all-cause mortality in patients in the coronary care unit: A cohort study." Journal of International Medical Research 48, no. 4 (April 2020): 030006052091150. http://dx.doi.org/10.1177/0300060520911500.
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Objective Serum chloride disorders have been gaining increased attention. We aimed to assess the impact of serum chloride on all-cause mortality in critically ill patients in coronary care units (CCUs). Methods We extracted clinical data from the Multiparameter Intelligent Monitoring in Intensive Care III database. We used data for the first CCU admission of each patient; baseline data were extracted within 24 hours after CCU admission. Statistical methods included the Lowess smoothing technique, Cox proportional hazards model, and subgroup analyses. Results A total 5616 patients who met the inclusion criteria were included. We observed a U-shaped relationship between admission chloride levels and 30-day all-cause mortality. In multivariate analysis adjusted for age, ethnicity, and sex, both hyper- and hypochloremia were significant predictors of risk of 30-day, 90-day, and 365-day all-cause mortality. After adjusting additional clinical characteristics, hypochloremia remained a significant predictor of risk of 30-day all-cause mortality (hazard ratio, 1.47; 95% confidence interval, 1.19–1.83). For 90-day and 365-day all-cause mortality, similar significant robust associations were found. Conclusions We observed a U-shaped relationship between admission chloride levels and 30-day all-cause mortality among patients in the CCU. Hypochloremia was associated with increased risk of all-cause mortality in these patients.
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Pan, Jianguo, Zhengxin Hu, Sisi Yin, and Meizi Li. "GRU with Dual Attentions for Sensor-Based Human Activity Recognition." Electronics 11, no. 11 (June 6, 2022): 1797. http://dx.doi.org/10.3390/electronics11111797.
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Human Activity Recognition (HAR) is nowadays widely used in intelligent perception and medical detection, and the use of traditional neural networks and deep learning methods has made great progress in this field in recent years. However, most of the existing methods assume that the data has independent identical distribution (I.I.D.) and ignore the data variability of different individual volunteers. In addition, most deep learning models are characterized by many parameters and high resources consumption, making it difficult to run in real time on embedded devices. To address these problems, this paper proposes a Gate Recurrent Units (GRU) network fusing the channel attention and the temporal attention for human activity recognition method without I.I.D. By using channel attention to mitigate sensor data bias, GRU and the temporal attention are used to capture important motion moments and aggregate temporal features to reduce model parameters. Experimental results show that our model outperforms existing methods in terms of classification accuracy on datasets without I.I.D., and reduces the number of model parameters and resources consumption, which can be easily used in low-resource embedded devices.
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Pan, Jianguo, Zhengxin Hu, Sisi Yin, and Meizi Li. "GRU with Dual Attentions for Sensor-Based Human Activity Recognition." Electronics 11, no. 11 (June 6, 2022): 1797. http://dx.doi.org/10.3390/electronics11111797.
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Human Activity Recognition (HAR) is nowadays widely used in intelligent perception and medical detection, and the use of traditional neural networks and deep learning methods has made great progress in this field in recent years. However, most of the existing methods assume that the data has independent identical distribution (I.I.D.) and ignore the data variability of different individual volunteers. In addition, most deep learning models are characterized by many parameters and high resources consumption, making it difficult to run in real time on embedded devices. To address these problems, this paper proposes a Gate Recurrent Units (GRU) network fusing the channel attention and the temporal attention for human activity recognition method without I.I.D. By using channel attention to mitigate sensor data bias, GRU and the temporal attention are used to capture important motion moments and aggregate temporal features to reduce model parameters. Experimental results show that our model outperforms existing methods in terms of classification accuracy on datasets without I.I.D., and reduces the number of model parameters and resources consumption, which can be easily used in low-resource embedded devices.
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Xu, Guoxiang, and Hao Jin. "Using Artificial Intelligence Technology to Solve the Electronic Health Service by Processing the Online Case Information." Journal of Healthcare Engineering 2021 (November 26, 2021): 1–12. http://dx.doi.org/10.1155/2021/9637018.
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With the continuous improvement of economic level and the continuous development of science and technology in China, information technology has begun to integrate into all walks of life. Medical units have begun to change from the traditional medical system to the intelligent system, and the processing of online case information has become an important component of medical informationization. To improve the efficiency of dealing with online case information, this study proposes to establish a fully connected neural network model to deal with online cases. Using jieba word segmentation tool and data preprocessing technology, the data of electronic medical records are sorted out, and the data are quantified using Word2Vec and other tools, and the data on electronic medical records are converted into one-hot binary variables. The quantified data are trained into a fully connected neural model, and the accuracy rate is about 88%. It is compared with naive Bayes and decision tree classification methods, and then a comparative experiment is carried out by solving e-health services in different ways. The results show that the fully connected neural network model has the best classification effect: the highest accuracy rate is about 93.7%, the highest precision rate is about 94.0%, the highest recall rate is about 95.3%, and the highest F1 score is about 94.6%. However, using artificial intelligence technology to solve electronic health services has great advantages, among which efficiency, assistance, and service satisfaction are all higher than 90%, which provides favorable technical support for electronic health services.
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Brown, S. H., G. Wright, and P. L. Elkin. "Biomedical Informatics: We Are What We Publish." Methods of Information in Medicine 52, no. 06 (2013): 538–46. http://dx.doi.org/10.3414/me13-01-0041.
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SummaryIntroduction: This article is part of a For-Discussion-Section of Methods of Information in Medicine on “Biomedical Informatics: We are what we publish“. It is introduced by an editorial and followed by a commentary paper with invited comments. In subsequent issues the discussion may continue through letters to the editor.Objective: Informatics experts have attempted to define the field via consensus projects which has led to consensus statements by both AMIA. and by IMIA. We add to the output of this process the results of a study of the Pubmed publications with abstracts from the field of Biomedical Informatics.Methods: We took the terms from the AMIA consensus document and the terms from the IMIA definitions of the field of Biomedical Informatics and combined them through human review to create the Health Infor -matics Ontology. We built a terminology server using the Intelligent Natural Language Processor (iNLP). Then we downloaded the entire set of articles in Medline identified by searching the literature by “Medical Informatics” OR “Bioinformatics”. The articles were parsed by the joint AMIA / IMIA terminology and then again using SNOMED CT and for the Bioinformatics they were also parsed using HGNC Ontology.Results: We identified 153,580 articles using “Medical Informatics” and 20,573 articles using “Bioinformatics”. This resulted in 168,298 unique articles and an overlap of 5,855 articles. Of these 62,244 articles (37%) had titles and abstracts that contained at least one concept from the Health Infor -matics Ontology. SNOMED CT indexing showed that the field interacts with most all clinical fields of medicine.Conclusions: Further defining the field by what we publish can add value to the consensus driven processes that have been the mainstay of the efforts to date. Next steps should be to extract terms from the literature that are uncovered and create class hierarchies and relationships for this content. We should also examine the high occurring of MeSH terms as markers to define Biomedical Informatics. Greater understanding of the Biomedical Informatics Literature has the potential to lead to improved self-awareness for our field.
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Meteshkin, K., and M. Kukhar. "FORMALIZATION OF PROCESSES AND PHENOMENA IN THE SYSTEM "REASONABLE INSTITUTION OF HIGHER EDUCATION" ON THE EXAMPLE OF SPECIALTY 193 "GEODESY AND EARTHLES"." Municipal economy of cities 3, no. 170 (June 24, 2022): 239–46. http://dx.doi.org/10.33042/2522-1809-2022-3-170-239-246.
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The article analyzes the state of informatization of processes and phenomena in modern institutions of higher education. The main conceptual provisions of modeling of processes and phenomena of higher education institutions are offered in the work. Models of correspondence of options of interaction of units of providing higher education institutions and procedures of formalization technology, models of educational and methodical providing, models of digital platforms of knowledge of institutions of higher education and models of procedures of interaction of elements of management of institutions of higher education by methods of category theory are developed. Methods of systems analysis, mathematical logic, category theory, heuristic methods were used to build models. The results were obtained. The problem is formulated, which is to unite the positive aspects of existing and existing information technologies, as well as the elimination of negative aspects that hinder the processes of information integration and the development of intellectual rules for interaction. Developed an example of formalization, in our opinion, the most complex and most important for the university educational and methodological support, abstracting from most types of support. Schematic solutions, as well as experience in organizing student learning based on the support system of educational processes were shown using models of disciplines and professional knowledge of students, as well as by combining them in one specialty, called digital platform of professional knowledge. A metamodel of a higher level of generalization has been developed, which allows its use in modeling other types of software. It is characterized by the complexity of relations between participants in the learning process, on the one hand, standardization and strict requirements for learning processes on the other hand, which led to the construction of learning processes in the language of theory of categories and functions. Formalization of learning processes in a more abstract language allowed to develop this metamodel. The development of the basis for the knowledge base of the intelligent system "Smart Institution of Higher Education" will solve a wide range of tasks at different levels of the hierarchy of management, control, monitoring, scheduling processes and phenomena in higher education institutions.
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Ogonowski, Jarosław, and Robert Milewski. "Design of a System Supporting the Collection of Information on the Completed Didactic Classes at Medical University of Białystok as an Attempt at Improving the Quality of Education." Studies in Logic, Grammar and Rhetoric 66, no. 3 (December 1, 2021): 625–33. http://dx.doi.org/10.2478/slgr-2021-0038.
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Abstract Obtaining a sufficient amount of measurable and reliable results of student surveys has always posed a challenge for university teams tasked with the provision of the quality of education. This is especially visible at faculties where education is based on the classic classroom-based model, which then transfers to clinical units, hospital wards, and specialist laboratories. The highly unpredictable pandemic situation caused by the SARS-CoV-2 virus raises the bar for the evaluation of didactics. Fortunately, the continuous technological progress in the area of Artificial Intelligence makes it possible to design the implementation of tools that would improve the position of systems for the management of courses of studies. The evaluation survey for didactic classes, as one of the last output data obtained during the process, may finally become a fully recognized source of information about the conducted classes and the teachers themselves. On the other hand, it may become a tool for those surveyed to influence the quality of classes, express their opinion, present suggestions and propositions generally pertaining to changes in the process of education. New information technologies not only make it possible to improve the effectiveness of reaching the recipients, but also provide completely new, very reliable methods of acquisition of credible behaviour, used as integration data in solutions based on machine communication. Using Artificial Intelligence coupled with data may make it possible to use intelligent communication for effective management of the process of surveying – a solution that has so far been used in business, in the form of the so-called bots. As a result, this would lead to an ongoing, fully quantitative and qualitative, assessment of classes.
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Hong, Na, Chun Liu, Jianwei Gao, Lin Han, Fengxiang Chang, Mengchun Gong, and Longxiang Su. "State of the Art of Machine Learning–Enabled Clinical Decision Support in Intensive Care Units: Literature Review." JMIR Medical Informatics 10, no. 3 (March 3, 2022): e28781. http://dx.doi.org/10.2196/28781.
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Background Modern clinical care in intensive care units is full of rich data, and machine learning has great potential to support clinical decision-making. The development of intelligent machine learning–based clinical decision support systems is facing great opportunities and challenges. Clinical decision support systems may directly help clinicians accurately diagnose, predict outcomes, identify risk events, or decide treatments at the point of care. Objective We aimed to review the research and application of machine learning–enabled clinical decision support studies in intensive care units to help clinicians, researchers, developers, and policy makers better understand the advantages and limitations of machine learning–supported diagnosis, outcome prediction, risk event identification, and intensive care unit point-of-care recommendations. Methods We searched papers published in the PubMed database between January 1980 and October 2020. We defined selection criteria to identify papers that focused on machine learning–enabled clinical decision support studies in intensive care units and reviewed the following aspects: research topics, study cohorts, machine learning models, analysis variables, and evaluation metrics. Results A total of 643 papers were collected, and using our selection criteria, 97 studies were found. Studies were categorized into 4 topics—monitoring, detection, and diagnosis (13/97, 13.4%), early identification of clinical events (32/97, 33.0%), outcome prediction and prognosis assessment (46/97, 47.6%), and treatment decision (6/97, 6.2%). Of the 97 papers, 82 (84.5%) studies used data from adult patients, 9 (9.3%) studies used data from pediatric patients, and 6 (6.2%) studies used data from neonates. We found that 65 (67.0%) studies used data from a single center, and 32 (33.0%) studies used a multicenter data set; 88 (90.7%) studies used supervised learning, 3 (3.1%) studies used unsupervised learning, and 6 (6.2%) studies used reinforcement learning. Clinical variable categories, starting with the most frequently used, were demographic (n=74), laboratory values (n=59), vital signs (n=55), scores (n=48), ventilation parameters (n=43), comorbidities (n=27), medications (n=18), outcome (n=14), fluid balance (n=13), nonmedicine therapy (n=10), symptoms (n=7), and medical history (n=4). The most frequently adopted evaluation metrics for clinical data modeling studies included area under the receiver operating characteristic curve (n=61), sensitivity (n=51), specificity (n=41), accuracy (n=29), and positive predictive value (n=23). Conclusions Early identification of clinical and outcome prediction and prognosis assessment contributed to approximately 80% of studies included in this review. Using new algorithms to solve intensive care unit clinical problems by developing reinforcement learning, active learning, and time-series analysis methods for clinical decision support will be greater development prospects in the future.