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A high-throughput fully digit-serial polynomial basis finite field $\text{GF}(2^{m})$ multiplier for IoT applications

A high-throughput fully digit-serial polynomial basis finite...

引用

IEEE Region 10 International conference TENCON

作者： Siva Ramakrishna Pillutla Lakshmi Boppana National Institute of Technology Warangal Warangal India

The performance of many data security and reliability applications depends on computations in finite fields GF (2 m ). In finite field arithmetic, field multiplication is a complex operation and is also used in other operations such as inversion and exponentiation. By considering the application domain needs, a variety of efficient algorithms and architectures are proposed in the literature for field GF (2 m ) multiplier. With the rapid emergence of Internet of Things (IoT) and Wireless sensor Networks (WSN), many resource-constrained devices such as IoT edge devices and WSN end nodes came into existence. The data bus width of these constrained devices is typically smaller. Digit-level architectures which can make use of the full data bus are suitable for these devices. In this paper, we propose a new fully digit-serial polynomial basis finite field GF (2 m ) multiplier where both the operands enter the architecture concurrently at digit-level. Though there are many digit-level multipliers available for polynomial basis multiplication in the literature, it is for the first time to propose a fully digit-serial polynomial basis multiplier. The proposed multiplication scheme is based on the multiplication scheme presented in the literature for a redundant basis multiplication. The proposed polynomial basis multiplication results in a high-throughput architecture. This multiplier is applicable for a class of trinomials, and this class of irreducible polynomials is highly desirable for IoT edge devices since it allows the least area and time complexities. The proposed multiplier achieves better throughput when compared with previous digit-level architectures.

关键词： Computer architecture Elliptic curve cryptography Hardware Clocks Internet of Things Wireless sensor networks

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ECNet: An Efficient and Context-Aware Network for Street Scene Parsing 9

ECNet: An Efficient and Context-Aware Network for Street Sce...

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9th International conference on Parallel architectures, algorithms and Programming (PAAP)

作者： Jiang, Bin Tu, Wenxuan Yang, Chao Xiao, Yi Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781538694039

Semantic segmentation for scene parsing in traffic not only needs to be precise, but also needs to be efficient for further applications in self-driving system. Most existing approaches employ a heavyweight structure as base network and mutil-scale module to enhance the context information, which often suffers from problems of modeling inefficiency and contextual information missing, respectively. We propose an Efficient and Context-Aware Network (ECNet) based on DenseNet121 and a novel mutil-scale mechanism. Firstly, we stack dense blocks with hybird dilation rates to extract feature maps, which is much more efficient in parameter computation and model storage. Secondly, we present an Enhanced Atrous Spatial Pyramid Pooling (EASPP) module which allows it to enlarge receptive field and obtain more discriminative features compared with Atrous Spatial Pyramid Pooling (ASPP). Additionally, our Decomposed Residual Block (DRB) module adopts decomposed convolution layer without increasing too much computational burden in order to boost the spatial information, which beneficially narrows the gap in semantic levels and spatial resolution. Experimental results on CityScapes and Camvid datasets show that our method is not only efficient but also performs more accurate for street scene understanding.

关键词： traffic scene deep learning image semantic segmentation dilated convolution feature fusion

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Multi-sensor-fusion System for People Counting applications

Multi-Sensor-Fusion System for People Counting Applications

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International conference on Societal Automation (SA)

作者： Michal Stec Viktor Herrmann Benno Stabernack Embedded Systems Group Fraunhofer Heinrich Hertz Institute HHI Berlin Germany

ISBN: (纸本)9781728133461

For economical as well as environmental reasons, logistical planning and efficient assignment of transport vehicles in public transportation need a precise knowledge of passenger utilization. To provide reliable figures sophisticated counting methods are demanded. Previously developed systems, mostly using a single 2D image sensor or a 3D depth sensor, can not fully achieve the required accuracy. In this paper, we present a robust people counting algorithm, based on multi sensor data fusion. Our solution runs on embedded systems with reasonable requirements with respect to computational power. 3D distance information, obtained from the ToF system, is used to perform the basic detection of objects. Once detected, the objects get additional classification features exploiting the data provided by a RGB camera and an IR thermal sensor. We describe the current state of fusing and processing of the collected data including the detection, classification (vital, non-vital), as well as sequential tracking and counting. We provide current counting results along with insights to future development concepts to improve the stated algorithms especially in terms of vital, non-vital classification and object recognition.

关键词： Cameras Image color analysis Three-dimensional displays Calibration Target tracking Two dimensional displays Thermal sensors

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Conditional Training Based GM and GM-OPELM Data fusion Schemes in Wireless sensor Networks

Conditional Training Based GM and GM-OPELM Data Fusion Schem...

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IEEE Pacific Rim conference on Communications, Computers and Signal Processing

作者： Lei Yang Qing Zhao Yindi Jing Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

As a key infrastructure of Internet of Things (loT), wireless sensor networks (WSN) can be utilized in a wide range of applications. The prediction based data fusion methods provide effective tools to reduce the amount of data transmissions while maintaining prediction accuracy. Recently a grey prediction model (GM) combining optimally-pruned extreme learning machine (OPELM) data fusion method has been proposed and shown to have good performance. However, the existing GM- OPELM method performs model training and broadcasting before each prediction, resulting in high complexity and energy consumption. In this paper the conditional training based GM (CT-GM) and GM-OPELM (CT-GM-OPELM) are proposed. By introducing an error threshold, the algorithms only perform model training when the prediction error is beyond the threshold. Compared with existing GM and GM-OPELM methods, the CT- GM and CT-GM-OPELM methods not only can achieve the higher rate of acceptable prediction and better time efficiency but also has significant reduction in the energy consumption on model training and transmissions.

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On Learning Joint Multi-biometric Representations by Deep fusion

On Learning Joint Multi-biometric Representations by Deep Fu...

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IEEE International conference on Biometrics: Theory, applications, and Systems (BTAS)

作者： Naser Damer Kristiyan Dimitrov Andreas Braun Arjan Kuijper Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany

ISBN: (数字)9781728115221

ISBN: (纸本)9781728115238

Multi-biometrics combines different biometric sources to enhance recognition, template protection, and indexing performances. One of the main challenges here is the need for joint discriminant feature representation of multi-biometric data. This is typically achieved by feature-level fusion, imposing limitations on the combinations of biometric characteristics and algorithms. Including multiple imaging sources within deep-learning networks was generally limited to multiple sources of images of the same physical object, e.g., multi-spectral object detection. Previous biometrics works were limited to use deep-learning to extract representations of single biometric characteristics. In contrast to that, our work studies creating representations of one identity by sampling different physical objects, i.e. biometric characteristics. We adapted three architectures successfully to produce and discuss jointly learned representations for different levels of correlated data, modalities, instances, and presentations. Our evaluation proved the applicability of jointly learning biometric representations, especially when the data correlation is low.

关键词： Iris recognition Face Indexing Machine learning Correlation Feature extraction Task analysis

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Computationally Efficient Distributed Multi-sensor Multi-Bernoulli Filter 21

Computationally Efficient Distributed Multi-Sensor Multi-Ber...

引用

21st International conference on Information fusion (fusion)

作者： Li, Suqi Yi, Wei Wang, Bailu Kong, Lingjiang Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9780996452762

This paper proposes a computationally efficient distributed fusion algorithm with multi-Bernoulli (MB) random finite sets (RFSs) based on generalized Covariance Intersection (GCI). The GCI fusion with MB filter (GCI-MB) involves the computation of the generalized MB (GMB) fused density determined by a set of hypotheses growing exponentially with object number. Hence, its applications with multiple targets are quite restrictive, which further motivates an efficient fusion algorithm. In this paper, we propose a novel approximation of the GCI-MB fusion. By discarding the hypotheses with negligible weights, the GCI-GMB fusion amounts to parallelized fusions performed with several smaller groups of Bernoulli components. As such, the computation of the GMB fused density is significantly simplified, with the number of hypotheses reduced dramatically and a practical appealing parallelizable structure achieved. Based on the proposed approximation, a computationally efficient GCI-MB fusion algorithm which can harness large amount of objects is devised. Furthermore, we present the analysis on both the characterization of the L-1-error and the computational complexity of the proposed fusion algorithm compared with the standard GCI-GMB fusion. Our analysis shows that the proposed fusion algorithm can reduce the computational expense as well as memories dramatically with slight approximation error. Numerical experiments using the Gaussian implementation for a challenging scenario with twenty objects demonstrate the performance of the proposed fusion algorithm.

关键词： Approximation algorithms Radio frequency sensor fusion Computational complexity Indexes Information filtering"

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Optimizing Activity Detection via sensor fusion 9

Optimizing Activity Detection via Sensor Fusion

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9th International conference on Information, Intelligence, Systems and applications (IISA)

作者： Khondoker, Farib Thornton, Trevor Spanias, Andreas Shanthamallu, Uday Shankar Arizona State Univ Sch ECEE SenSIP Ctr SenSIP Sensors & Devices REU Tempe AZ 85287 USA

ISBN: (纸本)9781538681619

Internet of Things (IoT) has enabled several applications related to data analytics. In this paper, an intuitive method for optimizing activity detection data is presented. Further applications include exploring detection accuracies of physical activities such as walking intensity and movement on stairs. This method utilizes different Microcontroller Units (MCUs) with embedded sensors which are used for activity detection. Additionally, this method also incorporates supervised learning - more specifically the Fine Gaussian SVM, to generate a predictive model for activity optimization.

关键词： Predictive models Feature extraction Support vector machines Accelerometers Gyroscopes Legged locomotion Machine learning algorithms

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Radar and Vision sensor fusion for Object Detection in Autonomous Vehicle Surroundings 10

Radar and Vision Sensor Fusion for Object Detection in Auton...

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10th International conference on Ubiquitous and Future Networks (ICUFN)

作者： Kim, Jihun Han, Dong Seog Senouci, Benaoumeur Kyungpook Natl Univ Sch Elect Engn Daegu South Korea ECE Paris Cent Elect Engn Sch Paris France

ISBN: (纸本)9781538646465

Multi-sensor data fusion for advanced driver assistance systems (ADAS) in the automotive industry has received much attention recently due to the emergence of self-driving vehicles and road traffic safety applications. Accurate surroundings recognition through sensors is critical to achieving efficient advanced driver assistance systems (ADAS). In this paper, we use radar and vision sensors for accurate object recognition. However, since sensors-pecific data have different coordinates, the data coordinate calibrate is essential. In this paper, we introduce the coordinate calibration algorithms between radar and vision images and perform sensor calibrating using data obtained from actual sensors.

关键词： Autonomous Vehicle sensor fusion radar Vision sensor calibration

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MulAV: Multilevel and Explainable Detection of Android Malware with Data fusion 18th

MulAV: Multilevel and Explainable Detection of Android Malwa...

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18th International conference on algorithms and architectures for Parallel Processing (ICA3PP)

作者： Li, Qun Chen, Zhenxiang Yan, Qiben Wang, Shanshan Ma, Kun Shi, Yuliang Cui, Lizhen Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China Univ Nebraska Lincoln NE USA Shandong Univ Jinan 250101 Shandong Peoples R China

ISBN: (纸本)9783030050634;9783030050627

With the popularization of smartphones, the number of mobile applications has grown substantially. However, many malware are emerging and thus pose a serious threat to the user's mobile phones. Malware detection has become a public concern that requires urgent resolution. In this paper, we propose MulAV, a multilevel and explainable detection method with data fusion. Our method obtain information from multiple levels (the APP source code, network traffic, and geospatial information) and combine it with machine learning method to train a model which can identify mobile malware with high accuracy and few false alarms. Experimental result shows that MulAV outperforms other anti-virus scanners and methods and achieves a detection rate of 97.8% with 0.4% false alarms. Furthermore, for the benefit of users, MulAV displays the explanation for each detection, thus revealing relevant properties of the detected malware.

关键词： Android malware detection Data fusion Multilevel Result explanation

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Effect of walking variations on complementary filter based inertial data fusion for ankle angle measurement

Effect of walking variations on complementary filter based i...

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IEEE International conference on Computational Intelligence and Virtual Environments for Measurement Systems and applications

作者： Lin Meng Baihan Li Craig Childs Arjan Buis Feng He Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China University of Strathclyde Glasgow United Kingdom

ISBN: (数字)9781538683446

ISBN: (纸本)9781538683453

A key problem on the measurement of lower-limb joint angles using inertial sensors is drift resulted in error accumulation after time integration. Several types of methods have been proposed to eliminate the drift. Among these methods, complementary filter-based sensor fusion algorithms are widely used in real-time applications due to its efficiency. Results from existing studies have shown that the performance of methods is relevant to walking speed. However, factors of walking variation have not been explored. This study first systematically investigated the walking variation factors and their effects on the accuracy of a proposed sensor fusion method during treadmill walking. Ten able-bodied participants participated in the experiment and walked on a treadmill with three different speeds (0.5, 1.0 and 1.5 m/s). A 12 camera Vicon motion capture system was used as the reference. The accuracy of the proposed method was evaluated in terms of the root-mean-square errors (RMSE), offsets and Pearson's correlation coefficients (PCC) in phases of a normalised gait cycle. A general linear model of analysis of variance (ANOVA) was used to analyze the factors including treadmill speed and gait phases. Results showed both factors had a significant influence on the RMSE, and only the treadmill speed had a significant influence on the offset. It provides an insight to improve the complementary filter-based method in future work.

关键词： sensors Foot Legged locomotion Angular velocity Knee Data integration Analysis of variance

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