A Novel Computation Method for Tag Estimates in DFSA-based RFID Systems

2021 ◽  
Vol 10 (6) ◽  
pp. 513-518
Author(s):  
Young-Beom Kim
Keyword(s):  
Computation Method ◽  
Rfid Systems

A linear perturbation computation method applied to hydrodynamic instability growth predictions in ICF targets

2006 ◽  
Vol 133 ◽  
pp. 201-204
Author(s):  
J.-M. Clarisse ◽  
C. Boudesocque-Dubois ◽  
J.-P. Leidinger ◽  
J.-L. Willien
Keyword(s):  
Hydrodynamic Instability ◽  
Linear Perturbation ◽  
Computation Method ◽  
Instability Growth

Analysis of magnetic force and dynamic characteristic for non-contact permanent magnet linear drive device

2020 ◽  
Vol 64 (1-4) ◽  
pp. 1337-1345
Author(s):  
Chuan Zhao ◽  
Feng Sun ◽  
Junjie Jin ◽  
Mingwei Bo ◽  
Fangchao Xu ◽  
...  
Keyword(s):  
Finite Element ◽  
Permanent Magnet ◽  
Dynamic Characteristic ◽  
Driving Force ◽  
Magnetic Circuit ◽  
Response Speed ◽  
Computation Method ◽  
Element Analysis ◽  
Element Simulation ◽  
The Relationship

This paper proposes a computation method using the equivalent magnetic circuit to analyze the driving force for the non-contact permanent magnet linear drive system. In this device, the magnetic driving force is related to the rotation angle of driving wheels. The relationship is verified by finite element analysis and measuring experiments. The result of finite element simulation is in good agreement with the model established by the equivalent magnetic circuit. Then experiments of displacement control are carried out to test the dynamic characteristic of this system. The controller of the system adopts the combination control of displacement and angle. The results indicate that the system has good performance in steady-state error and response speed, while the maximum overshoot needs to be reduced.

Study of Precise Computation Method of Magnetic Loss Considering Blanking Press Strain

2017 ◽  
Vol 137 (3) ◽  
pp. 254-260 ◽  
Author(s):  
Satoshi Doi ◽  
Tetsuya Aoki ◽  
Keiichi Okazaki ◽  
Yasuhito Takahashi ◽  
Koji Fujiwara
Keyword(s):  
Magnetic Loss ◽  
Computation Method

AMEBA - Evolutionary Computation Method: Comparison and Toolbox Development

2016 ◽  
Vol 26 (4) ◽  
pp. 229-236
Author(s):  
Marko Corn ◽  
Maja Atanasijević-Kunc
Keyword(s):  
Evolutionary Computation ◽  
Method Comparison ◽  
Computation Method

DAP: Efficient Detection Against Probabilistic Cloning Attacks in Anonymous RFID Systems

2021 ◽  
pp. 1-1
Author(s):  
Honglong Chen ◽  
Xin Ai ◽  
Kai Lin ◽  
Na Yan ◽  
Zhibo Wang ◽  
...  
Keyword(s):  
Rfid Systems ◽  
Efficient Detection

scholarly journals A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

2021 ◽  
Vol 11 (12) ◽  
pp. 5416
Author(s):  
Yanheng Liu ◽  
Minghao Yin ◽  
Xu Zhou
Keyword(s):  
Collaborative Filtering ◽  
User Preference ◽  
Computation Method ◽  
Group Recommendation ◽  
Preference Rating ◽  
Filtering Method ◽  
Personal Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Group Members ◽  
Individual Recommendation

The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree of preference of the user to the category. Secondly, we establish a group feature preference model, and the similarity of the group and other users’ feature preference is obtained based on the check-ins. Thirdly, the intragroup divergence of POIs is measured according to the POI preference of group members and their friends. Finally, the preference rating of the group for each location is calculated based on a collaborative filtering method and intragroup divergence computation, and the top-ranked score of locations are the recommendation results for the group. Experiments have been conducted on two LBSN datasets, and the experimental results on precision and recall show that the performance of the proposed method is superior to other methods.

scholarly journals Towards an Efficient CNN Inference Architecture Enabling In-Sensor Processing

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1955
Author(s):  
Md Jubaer Hossain Pantho ◽  
Pankaj Bhowmik ◽  
Christophe Bobda
Keyword(s):  
Power Consumption ◽  
High Speed ◽  
Image Sensor ◽  
Machine Learning Algorithms ◽  
Hierarchical Optimization ◽  
Computation Method ◽  
Optimization Approach ◽  
Efficient Computation ◽  
Feature Maps ◽  
Dynamic Power

The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution neural networks (CNNs) are largely adopted to infer knowledge due to their surprising success in automation, surveillance, and many other application domains. However, the convolution operations’ overwhelming computation demand has somewhat limited their use in remote sensing edge devices. In these platforms, real-time processing remains a challenging task due to the tight constraints on resources and power. Here, the transfer and processing of non-relevant image pixels act as a bottleneck on the entire system. It is possible to overcome this bottleneck by exploiting the high bandwidth available at the sensor interface by designing a CNN inference architecture near the sensor. This paper presents an attention-based pixel processing architecture to facilitate the CNN inference near the image sensor. We propose an efficient computation method to reduce the dynamic power by decreasing the overall computation of the convolution operations. The proposed method reduces redundancies by using a hierarchical optimization approach. The approach minimizes power consumption for convolution operations by exploiting the Spatio-temporal redundancies found in the incoming feature maps and performs computations only on selected regions based on their relevance score. The proposed design addresses problems related to the mapping of computations onto an array of processing elements (PEs) and introduces a suitable network structure for communication. The PEs are highly optimized to provide low latency and power for CNN applications. While designing the model, we exploit the concepts of biological vision systems to reduce computation and energy. We prototype the model in a Virtex UltraScale+ FPGA and implement it in Application Specific Integrated Circuit (ASIC) using the TSMC 90nm technology library. The results suggest that the proposed architecture significantly reduces dynamic power consumption and achieves high-speed up surpassing existing embedded processors’ computational capabilities.

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