Journal articles on the topic 'On-device Computation'
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Rasyad, Muhammad Abdullah, Favian Dewanta, and Sri Astuti. "All-in-one computation vs computational-offloading approaches: a performance evaluation of object detection strategies on android mobile devices." JURNAL INFOTEL 13, no. 4 (December 9, 2021): 216–22. http://dx.doi.org/10.20895/infotel.v13i4.700.
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Object detection gives a computer ability to classify objects in an image or video. However, high specified devices are needed to get a good performance. To enable devices with low specifications performs better, one way is offloading the computation process from a device with a low specification to another device with better specifications. This paper investigates the performance of object detection strategies on all-in-one Android mobile phone computation versus Android mobile phone computation with computational offloading on Nvidia Jetson Nano. The experiment carries out the video surveillance from the Android mobile phone with two scenarios, all-in-one object detection computation in a single Android device and decoupled object detection computation between an Android device and an Nvidia Jetson Nano. Android applications send video input for object detection using RTSP/RTMP streaming protocol and received by Nvidia Jetson Nano which acts as an RTSP/RTMP server. Then, the output of object detection is sent back to the Android device for being displayed to the user. The results show that the android device Huawei Y7 Pro with an average FPS performance of 1.82 and an average computing speed of 552 ms significantly improves when working with the Nvidia Jetson Nano, the average FPS becomes ten and the average computing speed becomes 95 ms. It means decoupling object detection computation between an Android device and an Nvidia Jetson Nano using the system provided in this paper successfully improves the detection speed performance.
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HAMANN, HEIKO, and HEINZ WÖRN. "EMBODIED COMPUTATION." Parallel Processing Letters 17, no. 03 (September 2007): 287–98. http://dx.doi.org/10.1142/s0129626407003022.
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The traditional computational devices and models, such as the von Neumann architecture or the Turing machine, are strongly influenced by concepts of central control and perfection. The standard models of computation seem to cover the reality of computation only partially and lack, in particular, in the ability to describe more natural forms of computation. In this paper we propose the concept of embodied computation, a straight forward advancement of well known concepts such as amorphous computing, emergent phenomena and embodied cognitive science. Many embodied microscopic computational devices form a single macroscopic device of embodied computation. The solution to computational problems emerges from a huge amount of local interactions. The system's memory is the sum of the positional information and possibly of the internal states. Such systems are very robust and allow different methodologies to analyze computation. To back this theoretic concept some results based on simulations are given and potential benefits of this approach are discussed.
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Wang, Anran, Maruchi Kim, Hao Zhang, and Shyamnath Gollakota. "Hybrid Neural Networks for On-Device Directional Hearing." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11421–30. http://dx.doi.org/10.1609/aaai.v36i10.21394.
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On-device directional hearing requires audio source separation from a given direction while achieving stringent human-imperceptible latency requirements. While neural nets can achieve significantly better performance than traditional beamformers, all existing models fall short of supporting low-latency causal inference on computationally-constrained wearables. We present DeepBeam, a hybrid model that combines traditional beamformers with a custom lightweight neural net. The former reduces the computational burden of the latter and also improves its generalizability, while the latter is designed to further reduce the memory and computational overhead to enable real-time and low-latency operations. Our evaluation shows comparable performance to state-of-the-art causal inference models on synthetic data while achieving a 5x reduction of model size, 4x reduction of computation per second, 5x reduction in processing time and generalizing better to real hardware data. Further, our real-time hybrid model runs in 8 ms on mobile CPUs designed for low-power wearable devices and achieves an end-to-end latency of 17.5 ms.
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Reys, Barbara J. "Mental Computation." Arithmetic Teacher 32, no. 6 (February 1985): 43–46. http://dx.doi.org/10.5951/at.32.6.0043.
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The ability to compute mentally. that is. to calculate exact numerical answers without the aid of any calculating or recording device, varies tremendously among indi vidual s. A quick survey of any elementary or secondary school classroom will document that some students perform mental computation quickly and accurately, whereas others are greatly hampered on even simple arithmetic problems by the withdrawal of paper and pencil. Occasionally history reports a case of an individual who ha developed extraordinary kill in mental computation. These individuals have both fascinated us and made us curious about the development of such skill. One such example wa Zerah Colburn. Born in Vermont in 1804, the son of a farmer, by the age of eight he was touring America and England displaying his exceptional mental calculating ability. He was able to give instantly the product of any two four-digit numbers. Asked to raise 8 to the sixteenth power, he gave the correct answer of 281 474 976 710 656 in a few seconds without the aid of any recording device (Eves 1969).
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Nasucha, M., Yohandri, J. T. S. Sumantyo, K. Hattori, and H. Kuze. "Computation calibration on distance measurement in an ultrasonic remote sensing device." Journal of Physics: Conference Series 1185 (April 2019): 012023. http://dx.doi.org/10.1088/1742-6596/1185/1/012023.
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Spanu, Alina Rodica, Daniel Besnea, Octavian G. Donţu, and Mihai Avram. "Improvements on Positional Accuracy by Using Mechatronic Device." Applied Mechanics and Materials 332 (July 2013): 206–11. http://dx.doi.org/10.4028/www.scientific.net/amm.332.206.
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Nowadays, the best positional accuracy has become a greater demand, due to the various technical requirements of the mechatronic systems. The imposed position shoul be found easily and repeatedly. The paper presents a method for controlling the positioning of the robot actuated with electrical motors. After the theoretical position computation, the software for Arduino board should compare the signals sent by the accelerometer with the imposed critical theoretic values. The measuring method takes into account the inertia forces for higher accuracy. The speed variation law for the electric motors provides better positioning, following dynamic conditions.
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Beal, Jacob, and Mirko Viroli. "Space–time programming." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2046 (July 28, 2015): 20140220. http://dx.doi.org/10.1098/rsta.2014.0220.
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Computation increasingly takes place not on an individual device, but distributed throughout a material or environment, whether it be a silicon surface, a network of wireless devices, a collection of biological cells or a programmable material. Emerging programming models embrace this reality and provide abstractions inspired by physics, such as computational fields, that allow such systems to be programmed holistically, rather than in terms of individual devices. This paper aims to provide a unified approach for the investigation and engineering of computations programmed with the aid of space–time abstractions, by bringing together a number of recent results, as well as to identify critical open problems.
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Ghosh, K., Y. Naresh, and N. Srichakradhar Reddy. "A Theoretical Investigation on the Dimensions and Annealing Effects of InAs/GaAs Quantum Dots for Device Applications at High Bit-Rate Optical Transmission Window of 1.3-1.55 μm." Advanced Materials Research 584 (October 2012): 423–27. http://dx.doi.org/10.4028/www.scientific.net/amr.584.423.
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In this paper, we present theoretical model and computations for tuning the photoluminescence (PL) emission of InAs/GaAs quantum dots at 1.3 -1.55 μm by optimizing its height and base dimensions through quantum mechanical concepts. Simulation on the annealing induced compositional change in the QDs was carried out using Fick’s diffusion model. Results from our computation illustrated that lower base size of 10 nm and larger height QDs of 5.1 nm can be effectively utilized for extending the PL emission to longer wavelengths with minimal blue-shift on annealing. This highlights the potential of our model and computation to assist in precisely engineering the optical properties of QD materials for specific device applications.
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Mumolo, Enzo. "VHDL Design of a Scalable VLSI Sorting Device Based on Pipelined Computation." Journal of Computing and Information Technology 12, no. 1 (2004): 1. http://dx.doi.org/10.2498/cit.2004.01.01.
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Song, Chao, Jing Cui, H. Wang, J. Hao, H. Feng, and Ying Li. "Quantum computation with universal error mitigation on a superconducting quantum processor." Science Advances 5, no. 9 (September 2019): eaaw5686. http://dx.doi.org/10.1126/sciadv.aaw5686.
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Medium-scale quantum devices that integrate about hundreds of physical qubits are likely to be developed in the near future. However, these devices will lack the resources for realizing quantum fault tolerance. Therefore, the main challenge of exploring the advantage of quantum computation is to minimize the impact of device and control imperfections without complete logical encoding. Quantum error mitigation is a solution satisfying the requirement. Here, we demonstrate an error mitigation protocol based on gate set tomography and quasi-probability decomposition. One- and two-qubit circuits are tested on a superconducting device, and computation errors are successfully suppressed. Because this protocol is universal for digital quantum computers and algorithms computing expected values, our results suggest that error mitigation can be an essential component of near-future quantum computation.
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Sekigawa, Hiroshi. "Research on the effect of perturbation on algebraic problems and the methodology to cope with it." Impact 2019, no. 10 (December 30, 2019): 58–60. http://dx.doi.org/10.21820/23987073.2019.10.58.
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The reliance modern society has on computing is easily forgotten on a day-to-day basis. However, most people begin the day by looking at a mobile phone. A device that is connected to the Internet and gets the day started with weather updates, messages you've received via email or social media and the morning news. Then as they head out of the houses into the streets for the morning commute, they receive up to the minute directions, public transport schedules, delays or road traffic interruptions. During their commute people may even listen to their favourite songs, compressed to fit on the device in near perfect quality. All this, and more, is powered on some level by computing and it is hard to now imagine a world without it. However, it is equally hard to imagine what actually goes into making all of this happen. Perhaps, one has heard of the limits of computation, usually dependent on hardware like silicon chips. Less well known though is the mathematical nature of computation, the algorithms and calculations that go into designing the software which powers our economies and modern lifestyle. 'Computation underpins all computer science, a discipline that studies the processes that interact with data which we often refer to as programs,' outlines computation expert Professor Hiroshi Sekigawa, who is based at the Tokyo University of Science. Computation itself is a calculation that is carried out based on an explicitly designed model, like an algorithm. These algorithms make up the programs in our computers and are designed to solve problems. While there are different modes of developing algorithms and computing models, numeric computation is the one that is easily applicable to scientific computing. Fields like engineering, the physical sciences, life sciences, medicine and business all use elements of scientific computing to, among other things make detailed models of the world. 'To allow these computations to run more efficiently, using less memory, the technique of floating-point arithmetic is used,' explains Sekigawa. 'In short, this method uses formulas to represent real numbers resulting in approximations. This is useful for systems containing miniscule or immense numbers and require speedy processing times.' He says the drawback is that error analysis is needed to validate these outputs based on approximations. His team are working to improve on the current situation by exploring different modes of computation and the effects of combing them.
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DU, LIU-GE, KANG LI, FAN-MIN KONG, and YUAN HU. "PARALLEL 3D FINITE-DIFFERENCE TIME-DOMAIN METHOD ON MULTI-GPU SYSTEMS." International Journal of Modern Physics C 22, no. 02 (February 2011): 107–21. http://dx.doi.org/10.1142/s012918311101618x.
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Finite-difference time-domain (FDTD) is a popular but computational intensive method to solve Maxwell's equations for electrical and optical devices simulation. This paper presents implementations of three-dimensional FDTD with convolutional perfect match layer (CPML) absorbing boundary conditions on graphics processing unit (GPU). Electromagnetic fields in Yee cells are calculated in parallel millions of threads arranged as a grid of blocks with compute unified device architecture (CUDA) programming model and considerable speedup factors are obtained versus sequential CPU code. We extend the parallel algorithm to multiple GPUs in order to solve electrically large structures. Asynchronous memory copy scheme is used in data exchange procedure to improve the computation efficiency. We successfully use this technique to simulate pointwise source radiation and validate the result by comparison to high precision computation, which shows favorable agreements. With four commodity GTX295 graphics cards on a single personal computer, more than 4000 million Yee cells can be updated in one second, which is hundreds of times faster than traditional CPU computation.
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Rupp, Jennifer L. M. "(Invited) Strain Engineering for Li and Oxygen Ionic Transport for Solid State Batteries in Energy Storage, Fuel Cells and Memristive Neuromorphic Computing Devices." ECS Meeting Abstracts MA2018-01, no. 32 (April 13, 2018): 1952. http://dx.doi.org/10.1149/ma2018-01/32/1952.
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Energy storage and neuromorphic computation concepts based on oxygen and lithium ionic movements rely in the device preformance on the available defects and their mobilities. Through this paper we explore novel concepts on the use of strain for crystalline or polyamorphic oxide films to engineer the ionic transference of either lithium of oxygen ionic carriers as electrolyte components for either solid state batteries, fuel cells or memristive neuromorphic computational devices. The interplay of structure-strain-lattice symmetry break ups-ionic transfer vs. device performance engineering is in focus.
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Zhu, Xi, Yi Sun, Haijun Liu, Qingjiang Li, and Hui Xu. "Simulation of the Spiking Neural Network based on Practical Memristor." MATEC Web of Conferences 173 (2018): 01025. http://dx.doi.org/10.1051/matecconf/201817301025.
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In order to gain a better understanding of the brain and explore biologically-inspired computation, significant attention is being paid to research into the spike-based neural computation. Spiking neural network (SNN), which is inspired by the understanding of observed biological structure, has been increasingly applied to pattern recognition task. In this work, a single layer SNN architecture based on the characteristics of spiking timing dependent plasticity (STDP) in accordance with the actual test of the device data has been proposed. The device data is derived from the Ag/GeSe/TiN fabricated memristor. The network has been tested on the MNIST dataset, and the classification accuracy attains 90.2%. Furthermore, the impact of device instability on the SNN performance has been discussed, which can propose guidelines for fabricating memristors used for SNN architecture based on STDP characteristics.
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Roy, Urmimala, Tanmoy Pramanik, Subhendu Roy, Avhishek Chatterjee, Leonard F. Register, and Sanjay K. Banerjee. "Machine Learning for Statistical Modeling." ACM Transactions on Design Automation of Electronic Systems 26, no. 3 (February 2021): 1–17. http://dx.doi.org/10.1145/3440014.
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We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of devices simulated a priori, and the accuracy of prediction by the trained SVR model has been demonstrated. To produce a switching time distribution from the trained ML model, we only had to generate the dataset to train and validate the model, which needed ∼500 hours of computation. On the other hand, if 10 6 samples were to be simulated using the same computation resources to generate a switching time distribution from micromagnetic simulations, it would have taken ∼250 days. Spin-transfer-torque random access memory (STTRAM) has been used to demonstrate the method. However, different physical systems may be considered, different ML models can be used for different physical systems and/or different device parameter sets, and similar ends could be achieved by training the ML model using measured device data.
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Piazza, Alfredo J., and Can E. Korman. "Semiconductor Device Noise Computation Based on the Deterministic Solution of the Poisson and Boltzmann Transport Equations." VLSI Design 8, no. 1-4 (January 1, 1998): 381–85. http://dx.doi.org/10.1155/1998/92597.
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Numerical simulation results of noise due to current fluctuations along an n+−n−n+ submicron structure are presented. The mathematical framework is based on the interpretation of the equations describing electron transport in the semiclassical transport model as stochastic differential equations (SDE). According to this formalism the key computations for the spectral density describing the noise process are reduced to a special initial value problem for the Boltzmann transport equation (BTE). The algorithm employed in the computation of the space dependent noise autocovariance function involves two main processes: the stationary self-consistent solution of the Boltzmann and Poisson equations, and a transient solution of the BTE with special initial conditions. The solution method for the BTE is based on the Legendre polynomial method. Noise due to acoustic and optical scattering and the effects of nonparabolicity are considered in the physical model.
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Maddilety, C. "Work Allocating Strategy Using a Powerful Prioritized Tasks in Mobile Cloud Computing Atmosphere." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 361–75. http://dx.doi.org/10.22214/ijraset.2021.38799.
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Abstract: In recent times, users necessitate and expect more demanding criteria to perform computational in-depth applications on their mobile devices. Based on the mobile device limitations such as processing power and battery life, Mobile Cloud Computing (MCC) is turned to be a more attractive choice to influence these drawbacks as a mobile computation can be provided to the cloud, which is coined as Mobile computation deceive. Prevailing researches on mobile computation offloading determines offloading mobile computation to single cloud. Moreover, in real time environment, computation service can be offered by multiple clouds for every computation services. Therefore, a novel and an interesting research crisis in mobile computation offloading begins with, how to choose a computation service for every tasks of mobile computation like computation time, energy consumption and cost of using these computation services. This is also termed as multi-site computation offloading in mobile cloud computation. In this examination deceive computation to diverse cloudlets/data centres with respect to task scheduling is formulated for examination. So, a Searching algorithm known as Accelerated Cuckoo Search Algorithm based job splittingis designed to attain higher data transmission rate in the MCC. The results of the certain method outperform the prevailing methods in terms of effectual job splitting; transmission speed, Bandwidth used, execution time of a job, transmission value, through put value, buffering overhead and reduced waiting time. The simulation was carried out in Clouds environment for good output. Keywords: Computation Deceive, Mobile Cloud Computing, Scheduling, Searching Algorithm, WorkSplitting.
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Ahmad, Hamza Sajjad, Muhammad Junaid Arshad, and Muhammad Sohail Akram. "Device Authentication and Data Encryption for IoT Network by Using Improved Lightweight SAFER Encryption With S-Boxes." International Journal of Embedded and Real-Time Communication Systems 12, no. 3 (July 2021): 1–13. http://dx.doi.org/10.4018/ijertcs.2021070101.
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To send data over the network, devices need to authenticate themselves within the network. After authentication, the device will be able to send the data in-network. After authentication, secure communication of devices is an important task that is done with an encryption method. IoT network devices have a very small circuit with low resources and low computation power. By considering low power, less memory, low computation, and all the aspect of IoT devices, an encryption technique is needed that is suitable for this type of device. As IoT networks are heterogeneous, each device has different hardware properties, and all the devices are not on one scale. To make IoT networks secure, this paper starts with the secure authentication mechanism to verify the device that wants to be a part of the network. After that, an encryption algorithm is presented that will make the communication secure. This encryption algorithm is designed by considering all the important aspects of IoT devices (low computation, low memory, and cost).
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Zhang, Jianfei, and Lei Zhang. "Efficient CUDA Polynomial Preconditioned Conjugate Gradient Solver for Finite Element Computation of Elasticity Problems." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/398438.
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Graphics processing unit (GPU) has obtained great success in scientific computations for its tremendous computational horsepower and very high memory bandwidth. This paper discusses the efficient way to implement polynomial preconditioned conjugate gradient solver for the finite element computation of elasticity on NVIDIA GPUs using compute unified device architecture (CUDA). Sliced block ELLPACK (SBELL) format is introduced to store sparse matrix arising from finite element discretization of elasticity with fewer padding zeros than traditional ELLPACK-based formats. Polynomial preconditioning methods have been investigated both in convergence and running time. From the overall performance, the least-squares (L-S) polynomial method is chosen as a preconditioner in PCG solver to finite element equations derived from elasticity for its best results on different example meshes. In the PCG solver, mixed precision algorithm is used not only to reduce the overall computational, storage requirements and bandwidth but to make full use of the capacity of the GPU devices. With SBELL format and mixed precision algorithm, the GPU-based L-S preconditioned CG can get a speedup of about 7–9 to CPU-implementation.
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Hua, Guan-Jie, Che-Lun Hung, and Chuan Yi Tang. "Hadoop-MCC: Efficient Multiple Compound Comparison Algorithm Using Hadoop." Combinatorial Chemistry & High Throughput Screening 21, no. 2 (April 17, 2018): 84–92. http://dx.doi.org/10.2174/1386207321666180102120641.
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Aim and Objective: In the past decade, the drug design technologies have been improved enormously. The computer-aided drug design (CADD) has played an important role in analysis and prediction in drug development, which makes the procedure more economical and efficient. However, computation with big data, such as ZINC containing more than 60 million compounds data and GDB-13 with more than 930 million small molecules, is a noticeable issue of time-consuming problem. Therefore, we propose a novel heterogeneous high performance computing method, named as Hadoop-MCC, integrating Hadoop and GPU, to copy with big chemical structure data efficiently. Materials and Methods: Hadoop-MCC gains the high availability and fault tolerance from Hadoop, as Hadoop is used to scatter input data to GPU devices and gather the results from GPU devices. Hadoop framework adopts mapper/reducer computation model. In the proposed method, mappers response for fetching SMILES data segments and perform LINGO method on GPU, then reducers collect all comparison results produced by mappers. Due to the high availability of Hadoop, all of LINGO computational jobs on mappers can be completed, even if some of the mappers encounter problems. Results: A comparison of LINGO is performed on each the GPU device in parallel. According to the experimental results, the proposed method on multiple GPU devices can achieve better computational performance than the CUDA-MCC on a single GPU device. Conclusion: Hadoop-MCC is able to achieve scalability, high availability, and fault tolerance granted by Hadoop, and high performance as well by integrating computational power of both of Hadoop and GPU. It has been shown that using the heterogeneous architecture as Hadoop-MCC effectively can enhance better computational performance than on a single GPU device.
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Marto, Marco, Vladimir A. Bushenkov, Keith M. Reynolds, José G. Borges, and Susete Marques. "A Web-Based Approach for Visualizing Interactive Decision Maps." Information 12, no. 1 (December 24, 2020): 9. http://dx.doi.org/10.3390/info12010009.
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This research expands the applicability of the Feasible Goals (FGoal) Pareto frontier multiple criteria method to display the Edgeworth–Pareto hull using interactive decision maps (IDMs). Emphasis is placed upon the development of a communication architecture to display the Pareto frontiers, which includes a client device, a web server, and a dedicated computation server implemented with sockets. A standalone application on the latter processes client-server requests and responses to display updated information on the client. Specifically, the dedicated computation server is responsible for calculating the information needed to generate the Edgeworth–Pareto hull. This is delivered to the web server to generate the IDM to be displayed on the client device. The key innovation of this work is a tool that is developed to aid decision-makers with a network-based computational architecture that includes a computational server constantly in communication with a web server for fast responses to client requests to represent IDMs. Results show that this innovation avoids time-consuming communication, and this approach to represent IDMs on the web facilitates collaboration among decision-makers because they can analyze several complex problems in different browser windows and decide which problem and solution better correspond to their aims.
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Kang, Seongju, Jaegi Hwang, and Kwangsue Chung. "Domain-Specific On-Device Object Detection Method." Entropy 24, no. 1 (January 1, 2022): 77. http://dx.doi.org/10.3390/e24010077.
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Object detection is a significant activity in computer vision, and various approaches have been proposed to detect varied objects using deep neural networks (DNNs). However, because DNNs are computation-intensive, it is difficult to apply them to resource-constrained devices. Here, we propose an on-device object detection method using domain-specific models. In the proposed method, we define object of interest (OOI) groups that contain objects with a high frequency of appearance in specific domains. Compared with the existing DNN model, the layers of the domain-specific models are shallower and narrower, reducing the number of trainable parameters; thus, speeding up the object detection. To ensure a lightweight network design, we combine various network structures to obtain the best-performing lightweight detection model. The experimental results reveal that the size of the proposed lightweight model is 21.7 MB, which is 91.35% and 36.98% smaller than those of YOLOv3-SPP and Tiny-YOLO, respectively. The f-measure achieved on the MS COCO 2017 dataset were 18.3%, 11.9% and 20.3% higher than those of YOLOv3-SPP, Tiny-YOLO and YOLO-Nano, respectively. The results demonstrated that the lightweight model achieved higher efficiency and better performance on non-GPU devices, such as mobile devices and embedded boards, than conventional models.
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Tjandra, Yozef, and Sanga Lawalat. "PARALLEL NUMERICAL COMPUTATION: A COMPARATIVE STUDY ON CPU-GPU PERFORMANCE IN PI DIGITS COMPUTATION." Jurnal Pilar Nusa Mandiri 18, no. 2 (September 13, 2022): 93–100. http://dx.doi.org/10.33480/pilar.v18i2.3291.
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As the usage of GPU (Graphical Processing Unit) for non-graphical computation is rising, one important area is to study how the device helps improve numerical calculations. In this work, we present a time performance comparison between purely CPU (serial) and GPU-assisted (parallel) programs in numerical computation. Specifically, we design and implement the calculation of the hexadecimal -digit of the irrational number Pi in two ways: serial and parallel. Both programs are based upon the BBP formula for Pi in the form of infinite series identity. We then provide a detailed time performance analysis of both programs based on the magnitude. Our result shows that the GPU-assisted parallel algorithm ran a hundred times faster than the serial algorithm. To be more precise, we offer that as the value grows, the ratio between the execution time of the serial and parallel algorithms also increases. Moreover, when it is large enough, that is This GPU efficiency ratio converges to a constant, showing the GPU's maximally utilized capacity. On the other hand, for sufficiently small enough, the serial algorithm performed solely on the CPU works faster since the GPU's small usage of parallelism does not help much compared to the arithmetic complexity.
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Górecki, Paweł. "Compact Thermal Modeling of Power Semiconductor Devices with the Influence of Atmospheric Pressure." Energies 15, no. 10 (May 12, 2022): 3565. http://dx.doi.org/10.3390/en15103565.
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The efficiency of the heat dissipation process generated in semiconductor devices depends on many factors, related both to the parameters of the cooling system and environmental factors. Regarding the latter factors, ambient temperature and volume in which the device operates are typically indicated as the most important. However, in the case of the operation of semiconductor devices in non-standard conditions, e.g., in stratospheric airships, the thermal parameters of the device are significantly affected by a low value of atmospheric pressure. This paper presents a compact thermal model of a semiconductor device, considering the effects of reduced atmospheric pressure along with its experimental verification under various cooling conditions, thus obtaining high compliance for computation and measurement results. The formulated model is dedicated to circuit-level simulations, and it enables computations of the junction temperature of the semiconductor device in a short time. It is also shown that lowering atmospheric pressure can double the value of the junction-ambient thermal resistance.
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Khan, Prince Waqas, Khizar Abbas, Hadil Shaiba, Ammar Muthanna, Abdelrahman Abuarqoub, and Mashael Khayyat. "Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach." Electronics 9, no. 6 (June 17, 2020): 1010. http://dx.doi.org/10.3390/electronics9061010.
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Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from 0.979 % and gradually decreases to 0.87 % . Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.
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Lan, Yanwen, Xiaoxiang Wang, Chong Wang, Dongyu Wang, and Qi Li. "Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems." Electronics 8, no. 12 (November 30, 2019): 1430. http://dx.doi.org/10.3390/electronics8121430.
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The hierarchical edge-cloud enabled paradigm has recently been proposed to provide abundant resources for 5G wireless networks. However, the computation and communication capabilities are heterogeneous which makes the potential advantages difficult to be fully explored. Besides, previous works on mobile edge computing (MEC) focused on server caching and offloading, ignoring the computational and caching gains brought by the proximity of user equipments (UEs). In this paper, we investigate the computation offloading in a three-tier cache-assisted hierarchical edge-cloud system. In this system, UEs cache tasks and can offload their workloads to edge servers or adjoining UEs by device-to-device (D2D) for collaborative processing. A cost minimization problem is proposed by the tradeoff between service delay and energy consumption. In this problem, the offloading decision, the computational resources and the offloading ratio are jointly optimized in each offloading mode. Then, we formulate this problem as a mixed-integer nonlinear optimization problem (MINLP) which is non-convex. To solve it, we propose a joint computation offloading and resource allocation optimization (JORA) scheme. Primarily, in this scheme, we decompose the original problem into three independent subproblems and analyze their convexity. After that, we transform them into solvable forms (e.g., convex optimization problem or linear optimization problem). Then, an iteration-based algorithm with the Lagrange multiplier method and a distributed joint optimization algorithm with the adoption of game theory are proposed to solve these problems. Finally, the simulation results show the performance of our proposed scheme compared with other existing benchmark schemes.
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Aydın, Musa, Yiğit Uysallı, Ekin Özgönül, Berna Morova, Fatmanur Tiryaki, Elif Nur Firat-Karalar, Buket Doğan, and Alper Kiraz. "An LED-Based structured illumination microscope using a digital micromirror device and GPU accelerated image reconstruction." PLOS ONE 17, no. 9 (September 9, 2022): e0273990. http://dx.doi.org/10.1371/journal.pone.0273990.
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When combined with computational approaches, fluorescence imaging becomes one of the most powerful tools in biomedical research. It is possible to achieve resolution figures beyond the diffraction limit, and improve the performance and flexibility of high-resolution imaging systems with techniques such as structured illumination microscopy (SIM) reconstruction. In this study, the hardware and software implementation of an LED-based super-resolution imaging system using SIM employing GPU accelerated parallel image reconstruction is presented. The sample is illuminated with two-dimensional sinusoidal patterns with various orientations and lateral phase shifts generated using a digital micromirror device (DMD). SIM reconstruction is carried out in frequency space using parallel CUDA kernel functions. Furthermore, a general purpose toolbox for the parallel image reconstruction algorithm and an infrastructure that allows all users to perform parallel operations on images without developing any CUDA kernel code is presented. The developed image reconstruction algorithm was run separately on a CPU and a GPU. Two different SIM reconstruction algorithms have been developed for the CPU as mono-thread CPU algorithm and multi-thread OpenMP CPU algorithm. SIM reconstruction of 1024 × 1024 px images was achieved in 1.49 s using GPU computation, indicating an enhancement by ∼28 and ∼20 in computation time when compared with mono-thread CPU computation and multi-thread OpenMP CPU computation, respectively.
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Wang, Huawei, Ye Li, Yingnan Jiao, and Zhengping Jin. "An efficient outsourced attribute-based encryption scheme in the device-to-device mobile network." International Journal of Distributed Sensor Networks 15, no. 7 (July 2019): 155014771986550. http://dx.doi.org/10.1177/1550147719865507.
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Device-to-device communication is considered as one of the hopeful technologies for proximal communication, which plays a vital role in the wireless systems and 5G cellular networks. The outsourced attribute-based encryption scheme is convinced to be very suitable for secure device-to-device communication since it allows not only fine-grained sharing of encrypted data but also achieves high efficiency in the decryption of general attribute-based encryption schemes. However, almost all existing outsourced attribute-based encryption schemes can hardly be applied directly in the device-to-device communication because many heavy computation operations, such as pairing and modular exponentiations, cannot be taken on the mobile devices in the device-to-device network. In this article, we propose a concept of outsourcing threshold decryption for attribute-based encryption and design a new efficient outsourcing threshold decryption scheme for ciphertext-policy attribute-based encryption. In our definition of outsourcing threshold decryption, the decryption, which is a computationally expensive operation, is outsourced to multiple semi-trusted and lightweight computing devices determined by an access structure and can be jointly taken by these devices. Our scheme supports proxy re-encryption which enables the decryption delegation. Finally, security and efficiency analyses of our proposed method indicate that our proposal guarantees strong security against chosen plaintext attacks and requires less outsourced computation and communication cost than the existing outsourced attribute-based encryption schemes.
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Supatmi, S., and I. D. Sumitra. "Fingerprint Matching Using Bozorth3 Algorithm and Parallel Computation on NVIDIA Compute Unified Device Architecture." IOP Conference Series: Materials Science and Engineering 879 (August 7, 2020): 012109. http://dx.doi.org/10.1088/1757-899x/879/1/012109.
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Yu, Shimeng, Yi Wu, Rakesh Jeyasingh, Duygu Kuzum, and H. S. Philip Wong. "An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation." IEEE Transactions on Electron Devices 58, no. 8 (August 2011): 2729–37. http://dx.doi.org/10.1109/ted.2011.2147791.
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Rashid, Muhammad, Malik Imran, and Asher Sajid. "An Efficient Elliptic-Curve Point Multiplication Architecture for High-Speed Cryptographic Applications." Electronics 9, no. 12 (December 12, 2020): 2126. http://dx.doi.org/10.3390/electronics9122126.
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This work presents an efficient high-speed hardware architecture for point multiplication (PM) computation of Elliptic-curve cryptography using binary fields over GF(2163) and GF(2571). The efficiency is achieved by reducing: (1) the time required for one PM computation and (2) the total number of required clock cycles. The required computational time for one PM computation is reduced by incorporating two modular multipliers (connected in parallel), a serially connected adder after multipliers and two serially connected squarer units (one after the first multiplier and another after the adder). To optimize the total number of required clock cycles, the point addition and point double instructions for PM computation of the Montgomery algorithm are re-structured. The implementation results after place-and-route over GF(2163) and GF(2571) on a Xilinx Virtex-7 FPGA device reveal that the proposed high-speed architecture is well-suited for the network-related applications, where millions of heterogeneous devices want to connect with the unsecured internet to reach an acceptable performance.
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Khera, Nikhil, Krishna Sarthak Tiwari, Vaibhav Tripathi, G. Sai Vinit, and Nagaraja J. "Literature Survey of Computation Offloading for Mobile Applications in Mobile Edge Computation." Journal of Computer Science Engineering and Software Testing 8, no. 1 (February 28, 2022): 65–74. http://dx.doi.org/10.46610/jocses.2022.v08i01.005.
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Despite their progress and improvements, mobile devices are still regarded as restricted computer devices. Users are growing more discerning, expecting to be able to run computationally intensive applications on their smartphones or tablets. As a result, Mobile Cloud Computing (MCC) integrates mobile computing and Cloud Computing in order to use offloading techniques to increase the capabilities of mobile devices. Although offloading programmes to the cloud might improve mobile device performance, it can also increase processing delay. Inevitably, the quality of user service (QoS) suffers as a result, particularly for specific applications (especially workflow applications). To address the problem of network latency, a new paradigm known as mobile edge computing (MEC) has been developed, which may be thought of as a subset of MCC. We also see deployment of Cloudlets, which are a form of edge server, to minimize latency and energy usage by offloading applications to the cloud, resulting in an efficient and cost-effective architecture as well as various decision-making techniques for offloading. And so, a literature survey regarding this will be presented which focuses on various methods of offloading and its decision-making techniques.
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Mosiężny, Jçdrzej, and Bartosz Ziegler. "Meshing strategy for hybrid tet-hex PHES layered bed heat storage." MATEC Web of Conferences 240 (2018): 03009. http://dx.doi.org/10.1051/matecconf/201824003009.
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Article describes a meshing strategy for efficient computation of generic layered heat storage device for pumped heat energy storage (PHES). Mesh for structural solid body and fluid boundary layer is with unstructured flow field is presented. A rationale for geometry choice, mesh topology and different types of mesh topologies considered in this study. The effect of coarse and fine mesh on computational time and accuracy of results for transient analysis of coupled fluid and heat flow is described.
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Han, Yuelin, and Qi Zhu. "Joint Computation Offloading and Resource Allocation for NOMA-Enabled Multitask D2D System." Wireless Communications and Mobile Computing 2022 (July 28, 2022): 1–14. http://dx.doi.org/10.1155/2022/5349571.
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Due to the limited computing capacity of mobile device and high network-accessing delay, user in the area where mobile terminals are densely distributed (e.g., schools, malls, and hospitals) will experience high latency when processing multiple computation-intensive tasks. In this paper, a computation offloading scheme based on Device-to-Device (D2D) communication is proposed to deal with the problem that users have multiple tasks to process. Exploiting nonorthogonal multiple access (NOMA), user can offload tasks to multiple nearby devices that have available idle computing resource. We aim to minimize the user’s total cost including time latency, energy consumption, and offloading charge, which is formulated as a mixed integer nonlinear programming (MINLP) problem. We use decomposition approach to solve our problem and propose a two-layer optimization scheme named Multitask Joint Computation Offloading and Resource Allocation (MT-JCORA). In the inner layer, the NOMA-transmission time optimization problem in given task offloading decision is proved as a strictly convex problem. In the outer layer, we design a heuristic algorithm based on GA algorithm to obtain the optimal task offloading decision. Simulation results demonstrate that MT-JCORA can effectively reduce the total cost of user compared with related schemes.
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Roysam, Badrinath. "The maximum-likelihood method for em autoradiography: Small-system implementation technique." Proceedings, annual meeting, Electron Microscopy Society of America 46 (1988): 424–25. http://dx.doi.org/10.1017/s0424820100104182.
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Electron Microscopic Autoradiography (EMA) is of growing importance as a technique for the quantification of radiolabelled biochemicals in subcellular structures. The Maximum-Likelihood (ML) method of Miller et al. has been shown to be substantially superior to the conventional crossfire (OF) method. In this paper, we present a method for reducing the computational requirements of the ML method to a point where it becomes feasible to implement it on a small computer. We then describe EMA/PC, which is a device-independent computer program that implements the complete ML method on the IBM-PC class of machines.We have shown that the iterative Expectation-Maximization (EM) algorithm has a high computational complexity which can be reduced dramatically via a problem reformulation which involves the computation and use of sufficient statistics, which we call Grain-Organelle Probabilities (GOPs). The computation of GOPs requires the integration of a two-dimensional point-spread function over regions defining subcellular structures in the given set of electron micrographs.
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Horsman, Clare, Susan Stepney, Rob C. Wagner, and Viv Kendon. "When does a physical system compute?" Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 470, no. 2169 (September 8, 2014): 20140182. http://dx.doi.org/10.1098/rspa.2014.0182.
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Computing is a high-level process of a physical system. Recent interest in non-standard computing systems, including quantum and biological computers, has brought this physical basis of computing to the forefront. There has been, however, no consensus on how to tell if a given physical system is acting as a computer or not; leading to confusion over novel computational devices, and even claims that every physical event is a computation. In this paper, we introduce a formal framework that can be used to determine whether a physical system is performing a computation. We demonstrate how the abstract computational level interacts with the physical device level, in comparison with the use of mathematical models in experimental science. This powerful formulation allows a precise description of experiments, technology, computation and simulation, giving our central conclusion: physical computing is the use of a physical system to predict the outcome of an abstract evolution . We give conditions for computing, illustrated using a range of non-standard computing scenarios. The framework also covers broader computing contexts, where there is no obvious human computer user. We introduce the notion of a ‘computational entity’, and its critical role in defining when computing is taking place in physical systems.
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Chang, Jiang, Gongping Wu, and Hanwei Tang. "Influence of Ignition Channel on the Ignition Performance of Ignition Device." Mathematical Problems in Engineering 2018 (June 3, 2018): 1–14. http://dx.doi.org/10.1155/2018/4270790.
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Based on relative theories of gas dynamics and computational fluid dynamics, the flow field computation software ANSYS Fluent was used to simulate the steady flow field of the solid type ignition device of liquid-propellant rocket engine in two working conditions (condition I: without ignition channel, condition II: with ignition channel). On this basis, the influence of ignition channel on the working characteristics of the solid type ignition device of the liquid-propellant rocket engine was analyzed and experimentally tested. The results showed that when the pressure in the combustion chamber was atmospheric pressure, under condition II, the gas velocity at the throat of the ignition device did not reach the sonic velocity, and the position of sonic velocity moved to the downstream section of the ignition channel. Compared to condition I, the gas velocity and energy at the ignition outlet increased, which would be beneficial for initial ignition, and the gas pressure and temperature at the throat increased as well, indicating that the structural strength at the throat should be evaluated. The gas flow, gas pressure, and gas temperature at the ignition outlet decreased compared to working condition I, yet the changes were small and would have minimal effect on the ignition performance. During the pressure increase process in the combustion chamber, the gas pressure, velocity, temperature, flow, and energy at the ignition outlet experienced a steady stage in both working conditions before coming to an inflection point. The inflection point under condition II is smaller than that under condition I. To improve the ignition reliability, the working pressure of the ignition device should be further increased.
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Ali, Abid, Muhammad Munawar Iqbal, Harun Jamil, Faiza Qayyum, Sohail Jabbar, Omar Cheikhrouhou, Mohammed Baz, and Faisal Jamil. "An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing." Sensors 21, no. 13 (July 1, 2021): 4527. http://dx.doi.org/10.3390/s21134527.
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Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.
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Delaney, P., and J. C. Greer. "Classical computation with quantum systems." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 462, no. 2065 (November 4, 2005): 117–35. http://dx.doi.org/10.1098/rspa.2005.1565.
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As semiconductor electronic devices scale to the nanometer range and quantum structures (molecules, fullerenes, quantum dots, nanotubes) are investigated for use in information processing and storage, it becomes useful to explore the limits imposed by quantum mechanics on classical computing. To formulate the problem of a quantum mechanical description of classical computing, electronic device and logic gates are described as quantum sub-systems with inputs treated as boundary conditions, outputs expressed as operator expectation values, and transfer characteristics and logic operations expressed through the sub-system Hamiltonian, with constraints appropriate to the boundary conditions. This approach, naturally, leads to a description of the sub-systems in terms of density matrices. Application of the maximum entropy principle subject to the boundary conditions (inputs) allows for the determination of the density matrix (logic operation), and for calculation of expectation values of operators over a finite region (outputs). The method allows for an analysis of the static properties of quantum sub-systems.
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Hajder, Piotr, and Łukasz Rauch. "Moving Multiscale Modelling to the Edge: Benchmarking and Load Optimization for Cellular Automata on Low Power Microcomputers." Processes 9, no. 12 (December 9, 2021): 2225. http://dx.doi.org/10.3390/pr9122225.
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Numerical computations are usually associated with the High Performance Computing. Nevertheless, both industry and science tend to involve devices with lower power in computations. This is especially true when the data collecting devices are able to partially process them at place, thus increasing the system reliability. This paradigm is known as Edge Computing. In this paper, we propose the use of devices at the edge, with lower computing power, for multi-scale modelling calculations. A system was created, consisting of a high-power device—a two-processor workstation, 8 RaspberryPi 4B microcomputers and 8 NVidia Jetson Nano units, equipped with GPU processor. As a part of this research, benchmarking was performed, on the basis of which the computational capabilities of the devices were classified. Two parameters were considered: the number and performance of computing units (CPUs and GPUs) and the energy consumption of the loaded machines. Then, using the calculated weak scalability and energy consumption, a min–max-based load optimization algorithm was proposed. The system was tested in laboratory conditions, giving similar computation time with same power consumption for 24 physical workstation cores vs. 8x RaspberryPi 4B and 8x Jetson Nano. The work ends with a proposal to use this solution in industrial processes on example of hot rolling of flat products.
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Ahuja, Sanjay P., and Jesus Zambrano. "Mobile Cloud Computing: Offloading Mobile Processing to the Cloud." Computer and Information Science 9, no. 1 (January 31, 2016): 90. http://dx.doi.org/10.5539/cis.v9n1p90.
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<p class="zhengwen">The current proliferation of mobile systems, such as smart phones and tablets, has let to their adoption as the primary computing platforms for many users. This trend suggests that designers will continue to aim towards the convergence of functionality on a single mobile device (such as phone + mp3 player + camera + Web browser + GPS + mobile apps + sensors). However, this conjunction penalizes the mobile system both with respect to computational resources such as processor speed, memory consumption, disk capacity, and in weight, size, ergonomics and the component most important to users, battery life. Therefore, energy consumption and response time are major concerns when executing complex algorithms on mobile devices because they require significant resources to solve intricate problems.</p><p>Offloading mobile processing is an excellent solution to augment mobile capabilities by migrating computation to powerful infrastructures. Current cloud computing environments for performing complex and data intensive computation remotely are likely to be an excellent solution for offloading computation and data processing from mobile devices restricted by reduced resources. This research uses cloud computing as processing platform for intensive-computation workloads while measuring energy consumption and response times on a Samsung Galaxy S5 Android mobile phone running Android 4.1OS.</p>
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Nicolau, Dan V., Mercy Lard, Till Korten, Falco C. M. J. M. van Delft, Malin Persson, Elina Bengtsson, Alf Månsson, Stefan Diez, Heiner Linke, and Dan V. Nicolau. "Parallel computation with molecular-motor-propelled agents in nanofabricated networks." Proceedings of the National Academy of Sciences 113, no. 10 (February 22, 2016): 2591–96. http://dx.doi.org/10.1073/pnas.1510825113.
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The combinatorial nature of many important mathematical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers. There have been significant efforts in conceiving parallel-computation approaches in the past, for example: DNA computation, quantum computation, and microfluidics-based computation. However, these approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective. Here, we report the foundations of an alternative parallel-computation system in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. Exploring the network in a parallel fashion using a large number of independent, molecular-motor-propelled agents then solves the mathematical problem. This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation. We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance {2, 5, 9} of the subset sum problem, which is a benchmark NP-complete problem. Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.
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Salim, Mikail Mohammed, Inyeung Kim, Umarov Doniyor, Changhoon Lee, and Jong Hyuk Park. "Homomorphic Encryption Based Privacy-Preservation for IoMT." Applied Sciences 11, no. 18 (September 20, 2021): 8757. http://dx.doi.org/10.3390/app11188757.
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Healthcare applications store private user data on cloud servers and perform computation operations that support several patient diagnoses. Growing cyber-attacks on hospital systems result in user data being held at ransom. Furthermore, mathematical operations on data stored in the Cloud are exposed to untrusted external entities that sell private data for financial gain. In this paper, we propose a privacy-preserving scheme using homomorphic encryption to secure medical plaintext data from being accessed by attackers. Secret sharing distributes computations to several virtual nodes on the edge and masks all arithmetic operations, preventing untrusted cloud servers from learning the tasks performed on the encrypted patient data. Virtual edge nodes benefit from cloud computing resources to accomplish computing-intensive mathematical functions and reduce latency in device–edge node data transmission. A comparative analysis with existing studies demonstrates that homomorphically encrypted data stored at the edge preserves data privacy and integrity. Furthermore, secret sharing-based multi-node computation using virtual nodes ensures data confidentiality from untrusted cloud networks.
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Syropoulos, Apostolos. "A (Basis for a) Philosophy of a Theory of Fuzzy Computation." Kairos. Journal of Philosophy & Science 20, no. 1 (June 1, 2018): 181–201. http://dx.doi.org/10.2478/kjps-2018-0009.
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Abstract Vagueness is a linguistic phenomenon as well as a property of physical objects. Fuzzy set theory is a mathematical model of vagueness that has been used to define vague models of computation. The prominent model of vague computation is the fuzzy Turing machine. This conceptual computing device gives an idea of what computing under vagueness means, nevertheless, it is not the most natural model. Based on the properties of this and other models of vague computing, an attempt is made to formulate a basis for a philosophy of a theory of fuzzy computation.
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Roy, Avirup, Hrishikesh Dutta, Henry Griffith, and Subir Biswas. "An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation." Sensors 22, no. 7 (March 25, 2022): 2514. http://dx.doi.org/10.3390/s22072514.
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A lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estimation with the result of two tasks: the detection of sip from gestures with which the bottle is handled by its user and the detection of first sips after a bottle refill. This predictive volume estimation framework incorporates a self-correction mechanism that can minimize the error after each bottle fill-up cycle, which makes the system robust to errors from the sip classification module. In this paper, a detailed characterization of sip detection is performed to understand the accuracy-complexity tradeoffs by developing and implementing a variety of different ML models with varying complexities. The maximum energy consumed by the entire framework is around 119 mJ during a maximum computation time of 300 μs. The energy consumption and computation times of the proposed framework is suitable for implementation in low-power embedded hardware that can be incorporated in consumer grade water bottles.
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Wang, Yanyan, Lin Wang, Ruijuan Zheng, Xuhui Zhao, and Muhua Liu. "Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes." Sensors 21, no. 7 (March 28, 2021): 2347. http://dx.doi.org/10.3390/s21072347.
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In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay.
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Zhang, Fu Sheng, and Ge Ning Xu. "Kinematic Analysis on Multi-Bar Linkage of Loader Working Device Based on Transformation Theory of Mechanisms." Advanced Materials Research 421 (December 2011): 287–92. http://dx.doi.org/10.4028/www.scientific.net/amr.421.287.
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By using the traditional Assur Groups method analysis multi-bar linkage kinematics parameters with an advanced plane connecting rod, complex nonlinear equations should be solved. To solve this tedious and error-prone problem，The senior Assur Groups should be transformed into combination of second grade Assur Groups, virtual single component and constraints single component with the aid of the theory of senior Assur Groups transformation. The problem can be simplified. Based on the transformation theory of mechanisms, the eight-bar linkage working device of loader was analysised. The results showed the method could avoid the nonlinear equations, reduce the computation amounts and enhance the computing rate. It could be applied in various forms of senior Assur Groups
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Fetzer, Valerie, Marcel Keller, Sven Maier, Markus Raiber, Andy Rupp, and Rebecca Schwerdt. "PUBA: Privacy-Preserving User-Data Bookkeeping and Analytics." Proceedings on Privacy Enhancing Technologies 2022, no. 2 (March 3, 2022): 447–516. http://dx.doi.org/10.2478/popets-2022-0054.
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Abstract In this paper we propose Privacy-preserving User-data Bookkeeping & Analytics (PUBA), a building block destined to enable the implementation of business models (e.g., targeted advertising) and regulations (e.g., fraud detection) requiring user-data analysis in a privacy-preserving way. In PUBA, users keep an unlinkable but authenticated cryptographic logbook containing their historic data on their device. This logbook can only be updated by the operator while its content is not revealed. Users can take part in a privacy-preserving analytics computation, where it is ensured that their logbook is up-to-date and authentic while the potentially secret analytics function is verified to be privacy-friendly. Taking constrained devices into account, users may also outsource analytic computations (to a potentially malicious proxy not colluding with the operator).We model our novel building block in the Universal Composability framework and provide a practical protocol instantiation. To demonstrate the flexibility of PUBA, we sketch instantiations of privacy-preserving fraud detection and targeted advertising, although it could be used in many more scenarios, e.g. data analytics for multi-modal transportation systems. We implemented our bookkeeping protocols and an exemplary outsourced analytics computation based on logistic regression using the MP-SPDZ MPC framework. Performance evaluations using a smartphone as user device and more powerful hardware for operator and proxy suggest that PUBA for smaller logbooks can indeed be practical.
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Fournier, Arthur, Franjieh El Khoury, and Samuel Pierre. "A Client/Server Malware Detection Model Based on Machine Learning for Android Devices." IoT 2, no. 3 (June 24, 2021): 355–74. http://dx.doi.org/10.3390/iot2030019.
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The rapid adoption of Android devices comes with the growing prevalence of mobile malware, which leads to serious threats to mobile phone security and attacks private information on mobile devices. In this paper, we designed and implemented a model for malware detection on Android devices to protect private and financial information, for the mobile applications of the ATISCOM project. This model is based on client/server architecture, to reduce the heavy computations on a mobile device by sending data from the mobile device to the server for remote processing (i.e., offloading) of the predictions. We then gradually optimized our proposed model for better classification of the newly installed applications on Android devices. We at first adopted Naive Bayes to build the model with 92.4486% accuracy, then the classification method that gave the best accuracy of 93.85% for stochastic gradient descent (SGD) with binary class (i.e., malware and benign), and finally the regression method with numerical values ranging from −100 to 100 to manage the uncertainty predictions. Therefore, our proposed model with random forest regression gives a good accuracy in terms of performance, with a good correlation coefficient, minimum computation time and the smallest number of errors for malware detection.
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Hu, Heng-Cheng, and Pi-Chung Wang. "Computation Offloading Game for Multi-Channel Wireless Sensor Networks." Sensors 22, no. 22 (November 11, 2022): 8718. http://dx.doi.org/10.3390/s22228718.
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Computation offloading for wireless sensor devices is critical to improve energy efficiency and maintain service delay requirements. However, simultaneous offloadings may cause high interferences to decrease the upload rate and cause additional transmission delay. It is thus intuitive to distribute wireless sensor devices in different channels, but the problem of multi-channel computation offloading is NP-hard. In order to solve this problem efficiently, we formulate the computation offloading decision problem as a decision-making game. Then, we apply the game theory to address the problem of allowing wireless sensor devices to make offloading decisions based on their own interests. In the game theory, not only are the data size of wireless sensor devices and their computation capability considered but the channel gain of each wireless sensor device is also included to improve the transmission rate. The consideration could evenly distribute wireless sensor devices to different channels. We prove that the proposed offloading game is a potential game, where the Nash equilibrium exists in each game after all device states converge. Finally, we extensively evaluate the performance of the proposed algorithm based on simulations. The simulation results demonstrate that our algorithm can reduce the number of iterations to achieve Nash equilibrium by 16%. Moreover, it improves the utilization of each channel to effectively increase the number of successful offloadings and lower the energy consumption of wireless sensor devices.