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IEEE SENSORS LETTERS 2025年第1期9卷

作者： Biglari, Amin Hu, Qisong Tang, Wei New Mexico State Univ Klipsch Sch Elect & Comp Engn Las Cruces NM 88001 USA

In this letter, we present a pixel-level predictive sampling method for image sensing and processing to reduce the computing overhead for power-limited image sensing systems. The predictive sampling method scans through rows and columns to identify the location and value of the critical pixels, which are the turning points in the row and column arrays. The prediction is performed using the value of prior pixels and a predefined error threshold. When the prediction is successful, the pixel is marked as a noncritical pixel and is skipped for recording and processing. Only the critical pixels are selected for further processing. We proposed reconstruction methods that recover the raw image from the selected critical pixels using interpolation. The experimental results show that the proposed method can reduce the data throughput by 72% with an error of 1.6% for sparse images. The convolutional neural network model applied with this method can achieve a similar detection accuracy in a standard method while only using 27.1% of data size.

关键词： Sensors Image reconstruction Turning sampling methods Throughput Cameras Prediction algorithms Image sensors Image edge detection Computational modeling Sensor applications image processing image sensing machine learning predictive sampling

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predictive sampling for Efficient Pairwise Subjective Image Quality Assessment 23

Predictive Sampling for Efficient Pairwise Subjective Image ...

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31st ACM International Conference on Multimedia (MM)

作者： Mohammadi, Shima Ascenso, Joao Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9798400701085

Subjective image quality assessment studies are used in many scenarios, such as the evaluation of compression, super-resolution, and denoising solutions. Among the available subjective test methodologies, pair comparison is attracting popularity due to its simplicity, reliability, and robustness to changes in the test conditions, e.g. display resolutions. The main problem that impairs its wide acceptance is that the number of pairs to compare by subjects grows quadratically with the number of stimuli that must be considered. Usually, the paired comparison data obtained is fed into an aggregation model to obtain a final score for each degraded image and thus, not every comparison contributes equally to the final quality score. In the past years, several solutions that sample pairs (from all possible combinations) have been proposed, from random sampling to active sampling based on the past subjects' decisions. This paper introduces a novel sampling solution called predictive sampling for Pairwise Comparison (PS-PC) which exploits the characteristics of the input data to make a prediction of which pairs should be evaluated by subjects. The proposed solution exploits popular machine learning techniques to select the most informative pairs for subjects to evaluate, while for the other remaining pairs, it predicts the subjects' preferences. The experimental results show that PS-PC is the best choice among the available sampling algorithms with higher performance for the same number of pairs. Moreover, since the choice of the pairs is done a priori before the subjective test starts, the algorithm is not required to run during the test and thus much more simple to deploy in online crowdsourcing subjective tests.

关键词： Subjective Image Quality Assessment Pairwise Comparison predictive sampling

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Energy-efficient collection of wearable sensor data through predictive sampling

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Smart Health 2021年 21卷

作者： Lalouani, Wassila Younis, Mohamed White-Gittens, Ian Emokpae, Roland N. Emokpae, Lloyd E. CSEE Dept. Univ. of Maryland Baltimore County Baltimore MD United States Harlem Hospital NY 506 Lenox Ave New York 10037 NY United States LASARRUS Clinic and Research Center LLC. Baltimore MD United States

Wearable sensors have emerged as viable and attractive solutions for monitoring the health of people under risk of major problems such as hypertension, heart attacks, and athletes overstressing their bodies. These devices would report on the status of certain body organs to a gateway node over wireless links. A major challenge for effective use of these miniaturized devices is sustaining their operation using a limited energy supply. Therefore, minimizing energy consumption is a crucial design goal. In this paper we propose a novel approach for reducing the volume and frequency of data transmission through data sample prediction. Our methodology is based on applying advanced machine learning techniques to determine when data transmissions are skipped, and by implicitly making the gateway aware of the omitted samples in order to achieve accurate signal reconstruction. The paper also presents a data quantization technique for increased throughput and reduced energy overhead while sustaining desired medical assessment accuracy. Furthermore, a packet formation algorithm is proposed to leverage the available buffering space to improve bandwidth and energy utilization subject to latency constraints. The effectiveness of our approach is validated using publicly available Electrocardiography and Electromyography datasets and is shown not only to outperform conventional data compression methods but can also be applied in conjunction with them. © 2021 Elsevier Inc.

关键词： Body area sensor network Energy optimization In-network data processing predictive sampling Wearable devices

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New Online Kernel Ridge Regression via Incremental predictive sampling 19

New Online Kernel Ridge Regression via Incremental Predictiv...

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28th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Xu, Shan Zhang, Xiao Liao, Shizhong Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

ISBN: (纸本)9781450369763

Online kernel ridge regression via existing sampling approaches, which aim at approximating the kernel matrix as accurately as possible, is independent of learning and has a cubic time complexity with respect to the sampling size for updating hypothesis. In this paper, we propose a new online kernel ridge regression via an incremental predictive sampling approach, which has the nearly optimal accumulated loss and performs efficiently at each round. We use the estimated ridge leverage score of the labeled matrix, which depends on the accumulated loss at each round, to construct the predictive sampling distribution, and use this sampling probability for the Nystrom approximation. To avoid calculating the inverse of the approximated kernel matrix directly, we use the Woodbury formula to accelerate the computation and adopt the truncated incremental singular value decomposition to update the generalized inverse of the intersection matrix. Our online kernel ridge regression has a time complexity of O(tmk +kappa(3)) for updating hypothesis at round t where.. is the truncated rank of the intersection matrix, and enjoys a regret bound of order O (root T), where T is the time horizon. Experimental results show that the proposed online kernel ridge regression via the incremental predictive sampling performs more stably and efficiently than the online kernel ridge regression via existing online sampling approaches that directly approximate the kernel matrix.

关键词： online kernel ridge regression predictive sampling incremental matrix computation singular value decomposition

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Machine Learning Enabled Secure Collection of Phasor Data in Smart Power Grid Networks 16

Machine Learning Enabled Secure Collection of Phasor Data in...

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16th IEEE International Conference on Mobility, Sensing and Networking (MSN)

作者： Lalouani, Wassila Younis, Mohamed Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21228 USA

ISBN: (纸本)9781728199160

In a smart power grid, phasor measurement devices provide critical status updates in order to enable stabilization of the grid against fluctuations in power demands and component failures. Particularly the trend is to employ a large number of phasor measurement units (PMUs) that are inter-networked through wireless links. We tackle the vulnerability of such a wireless PMU network to message replay and false data injection (FDI) attacks. We propose a novel approach for avoiding explicit data transmission through PMU measurements prediction. Our methodology is based on applying advanced machine learning techniques to forecast what values will be reported and associate a level of confidence in such prediction. Instead of sending the actual measurements, the PMU sends the difference between actual and predicted values along with the confidence level. By applying the same technique at the grid control or data aggregation unit, our approach implicitly makes such a unit aware of the actual measurements and enables authentication of the source of the transmission. Our approach is data-driven and varies over time;thus it increases the PMU network resilience against message replay and FDI attempts since the adversary's messages will violate the data prediction protocol. The effectiveness of approach is validated using datasets for the IEEE 14 and IEEE 39 bus systems and through security analysis.

关键词： Phasor networks Replay attack False data injection predictive sampling Recurrent neural networks (RNN)

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predictive sampling and Reconfigurable Technology Based Intelligent Memory-measurement System

Predictive Sampling and Reconfigurable Technology Based Inte...

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2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Pages 1-476

作者： Guangping Qi Ping Song Kejie Li School of Aerospace Science and Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9781424424948;9781424424955

Design ideas for the intelligent memory and measure system were presented in this paper. A CPLD, which is the core controller in the data acquisition system, controls the work of ADC, the process of the state machine, the memory and reading of data, the communication with a MCU and transceiver. The predictive sampling technology is proposed in the system for triggering the ADC to sample signal at appropriate time. Reconfigurable technology makes the system can adjust its sampling rate from 0 to 40Msps. The memory and restoring of data is elaborately designed. The experiment indicates the system with intelligence can work reliably and accurately.

关键词： CPLD memory-measurement system predictive sampling changeable sampling rate

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