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Journal articles on the topic 'Hardware identification'

Author: Grafiati

Published: 6 September 2023

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1

Moein, Samer, Fayez Gebali, T. Aaron Gulliver, and Abdulrahman Alkandari. "Hardware Trojan Identification and Detection." International Journal on Cryptography and Information Security 7, no. 2 (June 30, 2017): 1–20. http://dx.doi.org/10.5121/ijcis.2017.7201.

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MACHER*, Georg, Harald SPORER, Eugen BRENNER, and Christian KREINER. "Signal-Layer Security and Trust-Boundary Identification based on Hardware-Software Interface Definition." Journal of Ubiquitous Systems and Pervasive Networks 10, no. 1 (March 7, 2018): 1–9. http://dx.doi.org/10.5383/juspn.10.01.001.

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3

Sugisaka, Masanori, Shuuji Motomura, Takashi Kitaguchi, Toshiyuki Furuta, and Hirotosi Eguchi. "Hardware-based neural identification: Linear dynamical systems." Artificial Life and Robotics 1, no. 3 (September 1997): 151–55. http://dx.doi.org/10.1007/bf02471131.

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Alex, Anish T., Michel Dumontier, Jonathan S. Rose, and Christopher W. V. Hogue. "Hardware-accelerated protein identification for mass spectrometry." Rapid Communications in Mass Spectrometry 19, no. 6 (2005): 833–37. http://dx.doi.org/10.1002/rcm.1853.

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Mondal, Anindan, Rajesh Kumar Biswal, Mahabub Hasan Mahalat, Suchismita Roy, and Bibhash Sen. "Hardware Trojan Free Netlist Identification: A Clustering Approach." Journal of Electronic Testing 37, no. 3 (June 2021): 317–28. http://dx.doi.org/10.1007/s10836-021-05953-1.

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Zheng Zhaoyang, 郑朝阳, 张天舒 Zhang Tianshu, 范广强 Fan Guangqiang, 刘洋 Liu Yang, 吕立慧 Lü Lihui, and 项衍 Xiang Yan. "Identification Method of Ozone Lidar Hardware Failure Data." Chinese Journal of Lasers 46, no. 4 (2019): 0404004. http://dx.doi.org/10.3788/cjl201946.0404004.

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Santana Farias, Marcos, Nadia Nedjah, and Luiza de Macedo Mourelle. "Hardware implementation of subtractive clustering for radionuclide identification." Integration 46, no. 3 (June 2013): 220–29. http://dx.doi.org/10.1016/j.vlsi.2012.10.005.

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Ji, Yong Gang, Han Ming Zheng, and Yan Peng Zhang. "Electromagnetic Identification Intelligent Vehicle System Design." Applied Mechanics and Materials 401-403 (September 2013): 1695–98. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1695.

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This paper introduces the electromagnetic identification intelligent vehicles hardware system design diagram and software design process based MC9S12XSl28 microcontroller, focusing on the design of intelligent vehicle power unit circuit, the electromagnetic signal amplification circuit, the motor drive module, gives a detailed schematic. Software design give a specific design flow, integrated hardware and software design constitutes intelligent vehicles overall system.

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Cao, Qichun, Binqiang Wang, Gang Dong, Kekun Hu, and Hongbin Yang. "Operator Optimization Oriented Person Re-Identification." Journal of Physics: Conference Series 2284, no. 1 (June 1, 2022): 012019. http://dx.doi.org/10.1088/1742-6596/2284/1/012019.

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Abstract Using FPGA to accelerate the application of convolutional neural networks has attracted more and more attention. However, the design technology of the FPGA accelerator cannot follow up the latest achievements of the convolutional neural networks, nor can it consider optimizing the complex network structure combined with the characteristics of hardware to improve the accuracy of the network and simplify the implementation of hardware. To address these issues, in this paper, we adopt the development idea of algorithm-hardware co-design, compress the structure and optimize the operator for a state-of-the-art lightweight person re-identification network, and design a new network model SGCNet (Shift Gaussian Convolution Network). Specifically, SGCNet is obtained by structure optimization and operator replacement according to the optimization degree of the operator in the FPGA hardware environment. For convolution operation, only 1 × 1 kernel convolution operator is adopted in SGCNet, while spatial convolution is replaced by a more effective shift operation. SGCNet is more concise and clear in the operation types and complex structure of the network. Experimental evaluations demonstrate that SGCNet’s top-5 accuracy of 92.7% on ImageNet and R1 accuracy of 74.6% on CUHK03 of person re-identification data set, is higher than CMSNet network.

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Gude, Juan J., and Pablo García Bringas. "A Novel Control Hardware Architecture for Implementation of Fractional-Order Identification and Control Algorithms Applied to a Temperature Prototype." Mathematics 11, no. 1 (December 28, 2022): 143. http://dx.doi.org/10.3390/math11010143.

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In this paper, the conceptualization of a control hardware architecture aimed to the implementation of integer- and fractional-order identification and control algorithms is presented. The proposed hardware architecture combines the capability of implementing PC-based control applications with embedded applications on microprocessor- and FPGA-based real-time targets. In this work, the potential advantages of this hardware architecture over other available alternatives are discussed from different perspectives. The experimental prototype that has been designed and built to evaluate the control hardware architecture proposed in this work is also described in detail. The thermal-based process taking place in the prototype is characterized for being reconfigurable and exhibiting fractional behaviour, which results in a suitable equipment for the purpose of fractional-order identification and control. In order to demonstrate the applicability and effectiveness of the proposed control hardware architecture, integer- and fractional-order identification and control algorithms implemented in various control technologies have been applied to the temperature-based experimental prototype described before. Detailed discussion about results and identification and control issues are provided. The main contribution of this work is to provide an efficient and practical hardware architecture for implementing fractional-order identification and control algorithms in different control technologies, helping to bridge the gap between real-time hardware solutions and software-based simulations of fractional-order systems and controllers. Finally, some conclusions and concluding remarks are offered in the industrial context.

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Hejase, Mohammad, Arda Kurt, Tunc Aldemir, and Umit Ozguner. "Identification of Risk Significant Automotive Scenarios Under Hardware Failures." Electronic Proceedings in Theoretical Computer Science 269 (April 10, 2018): 59–73. http://dx.doi.org/10.4204/eptcs.269.6.

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Mendis, Gihan Janith, Jin Wei-Kocsis, and Arjuna Madanayake. "Deep Learning Based Radio-Signal Identification With Hardware Design." IEEE Transactions on Aerospace and Electronic Systems 55, no. 5 (October 2019): 2516–31. http://dx.doi.org/10.1109/taes.2019.2891155.

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McDonald, Gregor J., Jonathan S. Ellis, Richard W. Penney, and Richard W. Price. "Real-Time Vehicle Identification Performance Using FPGA Correlator Hardware." IEEE Transactions on Intelligent Transportation Systems 13, no. 4 (December 2012): 1891–95. http://dx.doi.org/10.1109/tits.2012.2189881.

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Godfrey, K. R., A. S. McCormack, and J. O. Flower. "Applying system identification using commercially available software and hardware." Control Engineering Practice 3, no. 9 (September 1995): 1247–54. http://dx.doi.org/10.1016/0967-0661(95)00123-c.

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Danielsson, M., A. Go, K. Jon-And, Th Lindblad, E. Machado, and M. Timm. "A hardware neural network for on-line particle identification." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 350, no. 1-2 (October 1994): 322–26. http://dx.doi.org/10.1016/0168-9002(94)91181-9.

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Godfrey, K. R., A. S. Mccormack, and J. O. Flower. "Applying System Identification using Commercially Available Software and Hardware." IFAC Proceedings Volumes 27, no. 8 (July 1994): 1081–86. http://dx.doi.org/10.1016/s1474-6670(17)47852-9.

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Bagul, Priyanka, and Vandana Inamdar. "Hardware Obfuscation Based Watermarking Technique for IPR Ownership Identification." International Journal of Reconfigurable Computing 2023 (July 3, 2023): 1–13. http://dx.doi.org/10.1155/2023/4550758.

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As the reuse of IP cores or the development of frequently used hardware modules is gaining more attention in the semiconductor industry, the misappropriation of the owner’s identity is a rising concern. Therefore, imprinting the owner’s identity in the form of a watermark or signature on the IP core is essential to avoid intellectual property right (IPR) infringement. In view of this, a watermarking technique is proposed in the present manuscript. A constraint-based dynamic watermarking method to generate the owner’s signature is proposed in conjunction with the logic encryption-based hardware obfuscation method. The method formulated in this manuscript consciously makes use of a basic switching component for embedding a watermark with IP core and hardware obfuscation, to achieve a lower overhead budget. Through the switching mechanism, the embedded watermark can be made detectable to legitimate end users off chip via test pin. The logic encryption-based method is set for accessing the watermark. Furthermore, an encrypted functionality is set as the signature generator module for generating owner’s signature. This provides hardware obfuscation and two-stage authentication mechanism for the generation of owner’s signature, and as a result of this, double-layer protection is achieved. Furthermore, a novel method to configure input key for signature generation module and to formulate owner’s signature is proposed. The viability of the present watermark technique for real-life application is checked on the ground of transparency, security, reliability, performance overhead, and robustness. Since the watermark in the proposed method is embedded outside the IP core, it does not cause any latency for the IP core functionality. Thus, even with significantly lower area overhead (∼<1.4%), the proposed method is able to provide higher robustness in terms of lower probability of coincidence (PC = 4.68 e − 97).

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Hu, Wenjie, Yuxin Zhang, Hongrui Zhang, and Weigang Chen. "Hardware Acceleration of Identifying Barcodes in Multiplexed Nanopore Sequencing." Electronics 11, no. 16 (August 19, 2022): 2596. http://dx.doi.org/10.3390/electronics11162596.

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In multiplexed sequencing, the identification of DNA sequencing barcodes can effectively reduce the probability of sample misassignment. However, the great quantity of sequence data requires a high-throughput identification method. Therefore, based on a barcode identification scheme combining cyclic shifting with dynamic programming (DP), this paper proposes, implements and tests a hardware accelerator that can accelerate barcode identification. In the accelerator, considering that the computational complexity of the DP algorithm can be expressed as the multiplication of the lengths of both involved sequences, we design a systolic array structure with simplified processing element (PE) and a parallel circuit architecture to identify the insertion and deletion errors based on the traceback. The accelerator is implemented on a field-programmable gate array (FPGA), and its performance is compared with that of software implemented on a general-purpose computer. The experimental results indicate that, compared with the software implementation, the accelerator can achieve speedups of two orders of magnitude for longer barcodes.

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Hikawa, Hiroomi, Kazutoshi Harada, and Takenori Hirabayashi. "Hardware Feedback Self-Organizing Map and its Application to Mobile Robot Location Identification." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 8 (October 20, 2007): 937–45. http://dx.doi.org/10.20965/jaciii.2007.p0937.

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We propose new hardware architecture for the self-organizing map (SOM) and feedback SOM (FSOM). Due to the parallel structure in the SOM and FSOM algorithm, customized hardware considerably speeds-up processing. Proposed hardware FSOM identifies the location of a mobile robot from a sequence of direction data. The FSOM is self-trained to cluster data to identify where the robot is. The proposed FSOM design is described in C and VHDL, and its performance is tested by simulation using actual sensor data from an experimental mobile robot. Results show that the hardware FSOM succeeds in self-learning to find the robot’s location. The hardware FSOM is estimated to process 6,992 million weight-vector elements per second.

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Kim, Sung-Kyoung, Tae Hyun Kim, and Seokhie Hong. "Fiat-shamir identification scheme immune to the hardware fault attacks." ACM Transactions on Embedded Computing Systems 12, no. 1s (March 2013): 1–11. http://dx.doi.org/10.1145/2435227.2435261.

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Rao, Jia, and Cheng-Zhong Xu. "Online Capacity Identification of Multitier Websites Using Hardware Performance Counters." IEEE Transactions on Parallel and Distributed Systems 22, no. 3 (March 2011): 426–38. http://dx.doi.org/10.1109/tpds.2010.92.

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22

Neshina, Y. G., А. D. Mekhtiyev, A. D. Alkina, P. A. Dunayev, and Z. D. Manbetova. "HARDWARE-SOFTWARE COMPLEX FOR IDENTIFICATION OF ROCK DISPLACEMENT IN PITS." SERIES OF GEOLOGY AND TECHNICAL SCIENCES 3, no. 459 (June 14, 2023): 180–92. http://dx.doi.org/10.32014/2023.2518-170x.308.

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23

Abraham, Jobin Reji. "Identification of Children Using HOG." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 2014–21. http://dx.doi.org/10.22214/ijraset.2022.47773.

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Abstract: Adults often utilize biometric recognition for a variety of tasks that require a personal identification evidence to be validated. Children's biometric identification, however, is still a challenge. In addition to helping find lost children and their families, developing border control systems to stop child trafficking, and assisting electronic recordkeeping systems, addressing this issue helps safeguard children against identity theft and identity fraud. Researchers are gathering biometric data from newborns' fingerprints, irises, and outer ears in order to start creating biometric recognition systems for kids. Children's ear mode was implemented using the hardware and software that were previously utilized for adults. Existing hardware was utilized in iris mode to find iris pictures. In order to capture children's fingerprints and transform the pictures into a backdrop format that complies with current international standards for issuing and comparing minutiae, new image processing gear and software have been created. Based on analyzing the effectiveness of usage and measuring the amount of performance, the benefits and drawbacks of utilizing each of these strategies throughout the first year of life were compared. Recommendations for the usage of each approach were given, despite the fact that they weren't always best practices.

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Maiorov, Nikolaj Nikolaevich, and Vitaly Evgenyevich . Taratun. "Research and development of software solution for identifying objects of material flow accounting systems based on CCSDS standards." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2022, no. 1 (January 31, 2022): 72–80. http://dx.doi.org/10.24143/2073-5529-2022-1-72-80.

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Although RFID hardware and software solutions for material flow and supply chain systems are widespread, their implementation today is realized at the local process and system level. When solving the problem of transmitting information about equipment between different systems, errors of inconsistency in data formats occur. This situation arises in the practical work of instrument making and space industry enterprises. There is a fragmenta-tion of the used information environments, models and methods. Today the development of new models and methods of identification of objects of material flow is important; they would provide data flexibility and the ability to integrate with existing RFID hardware systems, readers and other hardware systems. As a methodological support there have been chosen CCSDS standards, which offer solutions and development of hardware and software packages to solve the problem of identification of objects based on RFID tags. A systematic approach to representing the object identification levels has been offered and the need to develop a new system that is applicable for the identification throughout the life cycle of the object of material flow has been proved. The analysis of changing CCSDS standards in the development of hardware and software systems is presented to solve the problem of identification of objects based on RFID tags, the developed new model of data representation for the identification of the object is given, the practical implementation in the form of a subprogram is given. In addition, the possibility of adding new information about the object of the material flow is implemented, which has caused an opportunity to solve the problem of monitoring its movement in practice. The presented solution is versatile and can be used with a large number of well-known models of RFID tags. The identification system has been tested successfully so far and the results are presented in the standard CCSDS “Spacecraft onboard interface services – RFID tag encoding specification”.

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Fedák, Viliam, and Ján Bačík. "Hardware Design for State Vector Identification of a Small Helicopter Model." Applied Mechanics and Materials 282 (January 2013): 107–15. http://dx.doi.org/10.4028/www.scientific.net/amm.282.107.

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The paper deals with hardware design for sensory system of a small helicopter model that is characterized by a long-term stability and in real time generates data about helicopter state variables during helicopter flight. The sensor system is based on powerful 32-bit processors with the cores ARM7 and Cortex-M3. The main unit for data processing presents an embedded computer built on a mini-ITX motherboard with processor Intel i3. As the helicopter presents a system with six degrees of freedom and in the fact, during the flight, there is not any fixed point that would enable to caliber the sensors placed on the helicopter board, for processing of sensor data complex stochastic calculations are necessary. They are based on the discrete Kalman filter that present a main computing tool of the control system.

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Imran, Naveed, and Ronald F. DeMara. "Distance-Ranked Fault Identification of Reconfigurable Hardware Bitstreams via Functional Input." International Journal of Reconfigurable Computing 2014 (2014): 1–21. http://dx.doi.org/10.1155/2014/279673.

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Distance-Ranked Fault Identification (DRFI)is a dynamic reconfiguration technique which employs runtime inputs to conduct online functional testing of fielded FPGA logic and interconnect resources without test vectors. At design time, a diverse set of functionally identical bitstream configurations are created which utilize alternate hardware resources in the FPGA fabric. An ordering is imposed on the configuration pool as updated by the PageRank indexing precedence. The configurations which utilize permanently damaged resources and hence manifest discrepant outputs, receive lower rank are thus less preferred for instantiation on the FPGA. Results indicate accurate identification of fault-free configurations in a pool of pregenerated bitstreams with a low number of reconfigurations and input evaluations. For MCNC benchmark circuits, the observed reduction in input evaluations is up to 75% when comparing the DRFI technique to unguided evaluation. The DRFI diagnosis method is seen to isolate all 14 healthy configurations from a pool of 100 pregenerated configurations, and thereby offering a 100% isolation accuracy provided the fault-free configurations exist in the design pool. When a complete recovery is not feasible, graceful degradation may be realized which is demonstrated by the PSNR improvement of images processed in a video encoder case study.

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Barankova, I. I., U. V. Mikhailova, and G. I. Lukyanov. "Software development and hardware means of hidden usb-keylogger devices identification." Journal of Physics: Conference Series 1441 (January 2020): 012032. http://dx.doi.org/10.1088/1742-6596/1441/1/012032.

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Llamas, César, Manuel A. González, Carmen Hernández, and Jesús Vegas. "Open source hardware based sensor platform suitable for human gait identification." Pervasive and Mobile Computing 38 (July 2017): 154–65. http://dx.doi.org/10.1016/j.pmcj.2016.07.003.

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Liang, Yen-Chu, and Yun-Ping Sun. "Hardware-In-The-Loop Simulations of Hole/Crack Identification in a Composite Plate." Materials 13, no. 2 (January 16, 2020): 424. http://dx.doi.org/10.3390/ma13020424.

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The technology of hardware-in-the-loop simulations (HILS) plays an important role in the design of complex systems, for example, the structural health monitoring (SHM) of aircrafts. Due to the high performance of personal computers, HILS can provide practical solutions to many problems in engineering and sciences, especially in the huge systems, giant dams for civil engineering, and aircraft system. This study addresses the HILS in hole/crack identification in composite laminates. The multiple loading modes method is used for hole/crack identification. The signals of strains measured from the data-acquisition (DAQ) devices are accomplished by the graphical software LabVIEW. The results represent the actual responses of multiple loading mode tests of real specimens. A personal computer is employed to execute the identification work according to the strain data from DAQ devices by using a nonlinear optimization approach. When all the criteria are satisfied, the final identification results will be obtained. HILS will achieve real time identification of hole/crack in the composite plate by using the actual response measured from the sensors. Not only the size, but also the location and orientation of the crack/hole in a composite plate are successfully identified herein.

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Padmaja, B., P. Naga Shyam Bhargav, H. Ganga Sagar, B. Diwakar Nayak, and M. Bhushan Rao. "Indian Currency Denomination Recognition and Fake Currency Identification." Journal of Physics: Conference Series 2089, no. 1 (November 1, 2021): 012008. http://dx.doi.org/10.1088/1742-6596/2089/1/012008.

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Abstract Visually impaired and senior citizens find it difficult to identify different banknotes, driving the need for an automated system to recognize currency notes. This study proposes recognizing Indian currency notes of various denominations using Deep Learning through the CNN model. While not recognizing currency notes is one issue, identifying fake notes is another major issue. Currency counterfeiting is the illegal imitation of currency to deceive its recipient. The current existing methodologies for identifying a phony note rely on hardware. A method completely devoid of hardware that relies on specific security features to help distinguish a legitimate currency note from an illegitimate one is much needed. These features are extracted using the boundary box region of interest (ROI) and Canny Edge detection in OpenCV implemented in Python, and the multi scale template matching algorithm is applied to match the security features and differentiate fake notes from legitimate notes.

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Diana, Lorenzo, Jia Xu, and Luca Fanucci. "Oil Spill Identification from SAR Images for Low Power Embedded Systems Using CNN." Remote Sensing 13, no. 18 (September 10, 2021): 3606. http://dx.doi.org/10.3390/rs13183606.

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Oil spills represent one of the major threats to marine ecosystems. Satellite synthetic-aperture radar (SAR) sensors have been widely used to identify oil spills due to their ability to provide high resolution images during day and night under all weather conditions. In recent years, the use of artificial intelligence (AI) systems, especially convolutional neural networks (CNNs), have led to many important improvements in performing this task. However, most of the previous solutions to this problem have focused on obtaining the best performance under the assumption that there are no constraints on the amount of hardware resources being used. For this reason, the amounts of hardware resources such as memory and power consumption required by previous solutions make them unsuitable for remote embedded systems such as nano and micro-satellites, which usually have very limited hardware capability and very strict limits on power consumption. In this paper, we present a CNN architecture for semantically segmenting SAR images into multiple classes. The proposed CNN is specifically designed to run on remote embedded systems, which have very limited hardware capability and strict limits on power consumption. Even if the performance in terms of results accuracy does not represent a step forward compared with previous solutions, the presented CNN has the important advantage of being able to run on remote embedded systems with limited hardware resources while achieving good performance. The presented CNN is compatible with dedicated hardware accelerators available on the market due to its low memory footprint and small size. It also provides many additional very significant advantages, such as having shorter inference times, requiring shorter training times, and avoiding transmission of irrelevant data. Our goal is to allow embedded low power remote devices such as satellite systems for remote sensing to be able to directly run CNNs on board, so that the amount of data that needs to be transmitted to ground and processed on ground can be substantially reduced, which will be greatly beneficial in significantly reducing the amount of time needed for identification of oil spills from SAR images.

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EhKan, Phaklen, Timothy Allen, and Steven F. Quigley. "FPGA Implementation for GMM-Based Speaker Identification." International Journal of Reconfigurable Computing 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/420369.

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In today's society, highly accurate personal identification systems are required. Passwords or pin numbers can be forgotten or forged and are no longer considered to offer a high level of security. The use of biological features, biometrics, is becoming widely accepted as the next level for security systems. Biometric-based speaker identification is a method of identifying persons from their voice. Speaker-specific characteristics exist in speech signals due to different speakers having different resonances of the vocal tract. These differences can be exploited by extracting feature vectors such as Mel-Frequency Cepstral Coefficients (MFCCs) from the speech signal. A well-known statistical modelling process, the Gaussian Mixture Model (GMM), then models the distribution of each speaker's MFCCs in a multidimensional acoustic space. The GMM-based speaker identification system has features that make it promising for hardware acceleration. This paper describes the hardware implementation for classification of a text-independent GMM-based speaker identification system. The aim was to produce a system that can perform simultaneous identification of large numbers of voice streams in real time. This has important potential applications in security and in automated call centre applications. A speedup factor of ninety was achieved compared to a software implementation on a standard PC.

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Brescia, Elia, Donatello Costantino, Federico Marzo, Paolo Roberto Massenio, Giuseppe Leonardo Cascella, and David Naso. "Automated Multistep Parameter Identification of SPMSMs in Large-Scale Applications Using Cloud Computing Resources." Sensors 21, no. 14 (July 9, 2021): 4699. http://dx.doi.org/10.3390/s21144699.

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Parameter identification of permanent magnet synchronous machines (PMSMs) represents a well-established research area. However, parameter estimation of multiple running machines in large-scale applications has not yet been investigated. In this context, a flexible and automated approach is required to minimize complexity, costs, and human interventions without requiring machine information. This paper proposes a novel identification strategy for surface PMSMs (SPMSMs), highly suitable for large-scale systems. A novel multistep approach using measurement data at different operating conditions of the SPMSM is proposed to perform the parameter identification without requiring signal injection, extra sensors, machine information, and human interventions. Thus, the proposed method overcomes numerous issues of the existing parameter identification schemes. An IoT/cloud architecture is designed to implement the proposed multistep procedure and massively perform SPMSM parameter identifications. Finally, hardware-in-the-loop results show the effectiveness of the proposed approach.

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O. Agbeyangi, Abayomi. "Attendance Management System using Radio Frequency Identification Technology." APTIKOM Journal on Computer Science and Information Technologies 2, no. 3 (November 1, 2017): 117–23. http://dx.doi.org/10.11591/aptikom.j.csit.70.

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Educational institutions' administrators are mostly concerned about student irregular attendance coupled with the tendency of impersonation among students. The old method of taking attendance by calling names or signing on paper is time-consuming and not secure. In this paper, the development of a RFID (radio frequency identification)-based attendance management system that handled attendance taking seamlessly via the use of RFID cards is presented. The complete system is made up of a hardware and software part. The hardware was implemented using a Microcontroller and RFID reader as the main component while the Attendance Management application was implemented using C# programming language and MySQL for the database. The results gathered during the testing showed that the system performs excellently and scored 93% on accurate reading of RFID cards. The developed system is seen to have eliminated the bottlenecks associated with manual attendance taking and its application is not limited to tertiary institution.

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Bao, Ya Ping, Li Liu, Yuan Wang, and Qian Song. "Fingerprint Identification System Based on DSP." Advanced Materials Research 468-471 (February 2012): 920–23. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.920.

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This paper introduced a fast fingerprint identification system based on TMS320VC5416 DSP chip and MBF200 solidity fingerprint sensor. It precipitates fingerprint identification device developing into the direction of miniaturization, embedded and automatic.It recommends fingerprint identification system hardware and software design and the main system processing flow, aim at fingerprint identification arithmetic, the influence of system operation speed is being researched at the same time. High-speed data acquisition system is been built in order to achieve a DSP fingerprint identification system with high efficiency and low cost.

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Zhao, Jian Wen, and Sheng Rong Fan. "A Method for Parameter Identification of Mechanical System Based on Differential Motion Equations." Applied Mechanics and Materials 278-280 (January 2013): 1664–69. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1664.

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Correct identification of system parameters contributes to construct more precise model of the system and therefore improves the performance of controller. The current identification algorithms have some disadvantages, such as the complication of algorithms and the high requirement on hardware. In this paper, we propose a new system identification method which only utilizes the data from serve motor encoder. Based on the differential equations of motion and the theory of undetermined coefficients, mechanical parameters of the system can be solved using the least square method with the equations constructed based on kinematic parameters obtained by experimental tests. The identified mechanical parameters are then used to construct the system transfer function and a SIMULINK model. The method is validated by comparing the results between simulation and experiment. This method has advantages of low requirement on hardware and simple algorithm. It is proper to be applied in mechanical parameter identification of servo system.

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Bin Ibne R, Mamun, Md Syedul Ami, Fazida Hanim Hashim, and Khandaker Asaduzzama. "Hardware Approach of ANN Based Iris Recognition for Real-time Biometric Identification." Journal of Applied Sciences 11, no. 16 (August 1, 2011): 2984–92. http://dx.doi.org/10.3923/jas.2011.2984.2992.

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Lee, Zachary E., and K. Max Zhang. "Scalable identification and control of residential heat pumps: A minimal hardware approach." Applied Energy 286 (March 2021): 116544. http://dx.doi.org/10.1016/j.apenergy.2021.116544.

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Li, Qingzheng, and Amine Bermak. "A Low-Power Hardware-Friendly Binary Decision Tree Classifier for Gas Identification." Journal of Low Power Electronics and Applications 1, no. 1 (March 9, 2011): 45–58. http://dx.doi.org/10.3390/jlpea1010045.

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Grimaila, M. R., and J. Pineda de Gyvez. "Parametric fault identification and dynamic compensation techniques for cellular neural network hardware." IEE Proceedings - Circuits, Devices and Systems 143, no. 5 (1996): 295. http://dx.doi.org/10.1049/ip-cds:19960478.

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Campbell, Catherine, Chelsea Kramer, Shelley Kelsey, and W. James King. "Identification of EMR Hardware and Space Design Requirements using Human Factors Analyses." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 3, no. 1 (June 2014): 50–57. http://dx.doi.org/10.1177/2327857914031007.

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Electronic Medical Records (EMR) are being implemented globally in the hope of improving patient care, provider coordination, documentation accuracy, and information availability. Numerous factors impact successful EMR implementation including usability, accessibility and unique characteristics of the sociotechnical system within which it will be used. This paper describes the application of human factors methods to support effective EMR implementation at one pediatric hospital. The focus is on the problem of hardware selection and placement – a topic that has not received much attention in the literature to date. The requirements gathering process for two outpatient clinics included a task and gap analysis of current clinic workflows that led to the identification of specific hardware and design recommendations supporting future EMR workflows. Lessons learned post-implementation and requirements associated with hospital wide practices were extrapolated to generate guiding principles that apply to EMR implementation in other outpatient clinics.

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Lecomte, Maxime, Jacques Fournier, and Philippe Maurine. "An On-Chip Technique to Detect Hardware Trojans and Assist Counterfeit Identification." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 25, no. 12 (December 2017): 3317–30. http://dx.doi.org/10.1109/tvlsi.2016.2627525.

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Zhao, Yang, Xinguo Wei, Gangyi Wang, and Jian Li. "Real-time star identification using synthetic radial pattern and its hardware implementation." Acta Astronautica 131 (February 2017): 1–9. http://dx.doi.org/10.1016/j.actaastro.2016.11.015.

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Wu, K., Q. Zhang, and A. Hansen. "Modelling and identification of a hydrostatic transmission hardware-in-the-loop simulator." International Journal of Vehicle Design 34, no. 1 (2004): 52. http://dx.doi.org/10.1504/ijvd.2004.003894.

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Wang, Xueyang, Sek Chai, Michael Isnardi, Sehoon Lim, and Ramesh Karri. "Hardware Performance Counter-Based Malware Identification and Detection with Adaptive Compressive Sensing." ACM Transactions on Architecture and Code Optimization 13, no. 1 (April 5, 2016): 1–23. http://dx.doi.org/10.1145/2857055.

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Wang, Shu Lin, Zhi Jie Yang, Gang Liu, and Yan Yun Xu. "Research on Long Distance Radio Frequency Identification System of Toolholder." Advanced Materials Research 317-319 (August 2011): 1876–81. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1876.

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Considering the difficulty in identifying the tool system in the process of tool management, this paper proposes a design of toolholder identification system which is capable of storing all tool date using commercially available radio frequency identification (RFID) tags. The system is composed of radio frequency electronic tags, ultra high frequency reader and computer identification system. The paper gives the hardware and software design of toolholder identification system.

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Suryono, Dwi. "Analisis Keamanan Jaringan Hardware Trojan Pada IoT." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 4 (December 13, 2022): 3529–37. http://dx.doi.org/10.35957/jatisi.v9i4.2845.

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Hardware Trojan merupakan salah satu serangan terhadap keamanan Internet of Things (IoT) yang mencakup serangan terhadap label Radio Frequency Identification (RFID), jaringan komunikasi maupun pada privasi data. Tujuan dari riset ini adalah mendeskripsikan keamanan jaringan Hardware Trojan pada loT dengan desain literature review atau tinjauan pustaka. Analisis data model Miles dan Huberman digunakan dalam penganalisisan data. Hasilnya, perangkat IoT tidak dapat sepenuhnya aman sampai kita meletakkan fondasi keamanan yang kuat perangkat keras untuk mereka. Semua upaya dan biaya yang dilakukan untuk mengamankan perangkat dapat menjadi sia-sia jika perangkat memiliki Hardware Trojan. Keamanan perangkat keras yang tertanam di IoT adalah kebutuhan untuk melindungi “identitas” perangkat untuk melindungi mereka terhadap gangguan, dan untuk melindungi privasi serta keamanan data yang mereka hasilkan. Penggunaan Side-Channel Analysis memberikan lapisan keamanan lain di sekitar kriptografi kunci yang digunakan untuk melindungi integritas, kerahasiaan, dan keaslian sistem dan data.

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Ding, Dian, Lanqing Yang, Yi-Chao Chen, and Guangtao Xue. "Leakage or Identification." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 4 (December 27, 2021): 1–23. http://dx.doi.org/10.1145/3494984.

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The convenience of laptops brings with it the risk of information leakage, and conventional security systems based on the password or the explicit biometric do little to alleviate this problem. Biometric identification based on anatomical features provides far stronger security; however, a lack of suitable sensors on laptops limits the applicability of this technology. In this paper, we developed a behavior-irrelevant user identification system applicable to laptops with a metal casing. The proposed scheme, referred to as LeakPrint, is based on leakage current, wherein the system uses an earphone to capture current leaking through the body and then transmits the corresponding signal to a server for identification. The user identification is achieved via denoising, dimension reduction, and feature extraction. Compared to other biometric identification methods, the proposed system is less dependent on external hardware and more robust to environmental noise. The experiments in real-world environments demonstrated that LeakPrint can verify user identity with high accuracy (93.6%), while providing effective defense against replay attacks (96.5%) and mimicry attacks (90.9%).

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DIANOV, Vyacheslav N., and Tamara A. GEVONDYAN. "QUESTIONS HARDWARE BASSBOOST MAGNETO-LEVITATION VEHICLES." Transportation systems and technology 1, no. 1 (March 15, 2015): 112–27. http://dx.doi.org/10.17816/transsyst201511112-127.

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One of the hallmarks of the magneto-levitation transport are a big, life cycle and safety. The concept of security and life-cycle are important parameters: the probability of failure, failure rate, reliability, time to return to a safe status. Improving the reliability and security of modern rolling stock is possible through the improvement of railway automatics and telemechanics. Operated system monitoring and diagnostics of rolling stock consisting of a complex stationary, airborne and mobile systems, solve the problems of the prevention of failures and accelerated deterioration, maintain the technical characteristics of the equipment at a predetermined level and providing an assigned resource. The transition to a more technically complex objects - magnetolevitation vehicles - requires additional development funds to improve reliability, including the detection and diagnosis of failure States and conditions of intermittent failures - failures. The reliability of the control and diagnostic systems have many times exceed the reliability of the rolling equipment and perform the following main functions: - settings of sensors and measurement channels the parameters for a specific object; - identification of the elements of the object, registration of extreme deviations, their localization and signaling of limit deviations of parameters; - formation of a database state changes of elements, determination of residual life of components and of the facility as a whole.

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Godovnikov, Evgeniy A., Olga A. Petuhova, Tatiana V. Pronkina, Ruslan T. Usmanov, and Anatoliy V. Shitselov. "Automated system for determining heat lossesof the educational audience. Hardware part." Yugra State University Bulletin 17, no. 4 (December 23, 2021): 55–62. http://dx.doi.org/10.17816/byusu20210455-62.

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The article discusses the structure of the hardware and internetworking of automated room heat loss identification system on example of the Ugra State University classroom. The temperature controller has been developed taking into account the specifics of the room. A heat meter with telemetry capability has been selected.

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