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2019 IEEE Region 10 conference: Technology, Knowledge, and Society, TENCON 2019

作者： Pillutla, Siva Ramakrishna Boppana, Lakshmi National Institute of Technology Warangal Dept. of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9781728118956

The performance of many data security and reliability applications depends on computations in finite fields \text{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 \text{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 \text{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. © 2019 IEEE.

关键词： Internet of things

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RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving

RGB and LiDAR fusion based 3D Semantic Segmentation for Auto...

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International conference on Intelligent Transportation

作者： Khaled El Madawi Hazem Rashed Ahmad El Sallab Omar Nasr Hanan Kamel Senthil Yogamani Valeo R&D Cairo Egypt Cairo University Valeo Visions Systems Ireland

ISBN: (纸本)9781538670255

LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or due to shadows where the performance of cameras is degraded. LiDAR perception is gradually becoming mature for algorithms including object detection and SLAM. However, semantic segmentation algorithm remains to be relatively less explored. Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation. Our main contribution is to convert the RGB image to a polar-grid mapping representation used for LiDAR and design early and mid-level fusion architectures. Additionally, we design a hybrid fusion architecture that combines both fusion algorithms. We evaluate our algorithm on KITTI dataset which provides segmentation annotation for cars, pedestrians and cyclists. We evaluate two state-of-the-art architectures namely SqueezeSeg and PointSeg and improve the mIoU score by 10% in both cases relative to the LiDAR only baseline.

关键词： Laser radar Three-dimensional displays Semantics Image segmentation Feature extraction Computer architecture Cameras

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Off-the-shelf sensor vs. experimental radar - How much resolution is necessary in automotive radar classification?

Off-the-shelf sensor vs. experimental radar - How much resol...

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International conference on Information fusion

作者： Nicolas Scheiner Ole Schumann Florian Kraus Nils Appenrodt Jürgen Dickmann Bernhard Sick Environment Perception Mercedes-Benz AG Stuttgart Germany Intelligent Embedded Systems University of Kassel Kassel Germany

ISBN: (数字)9780578647098

ISBN: (纸本)9781728168302

Radar-based road user detection is an important topic in the context of autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to refine during subsequent signal processing. On the other hand, a new sensor generation is waiting in the wings for its application in this challenging field. In this article, two sensors of different radar generations are evaluated against each other. The evaluation criterion is the performance on moving road user object detection and classification tasks. To this end, two data sets originating from an off-the-shelf radar and a high resolution next generation radar are compared. Special attention is given on how the two data sets are assembled in order to make them comparable. The utilized object detector consists of a clustering algorithm, a feature extraction module, and a recurrent neural network ensemble for classification. For the assessment, all components are evaluated both individually and, for the first time, as a whole. This allows for indicating where overall performance improvements have their origin in the pipeline. Furthermore, the generalization capabilities of both data sets are evaluated and important comparison metrics for automotive radar object detection are discussed. Results show clear benefits of the next generation radar. Interestingly, those benefits do not actually occur due to better performance at the classification stage, but rather because of the vast improvements at the clustering stage.

关键词： Robot sensing systems Next generation networking Automotive engineering Clustering algorithms Radar detection Object detection

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Computationally efficient algorithm for spectral separation of narrow-band signals from multiple sources

Computationally efficient algorithm for spectral separation ...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Phani Kumar Nyshadham D R Shivakumar Matte Venkata Swaroop INTEL Corporation Bangalore India Bangalore India

ISBN: (数字)9781728152639

ISBN: (纸本)9781728152646

In digital signal processing, spectral source separation involves recovering the original signal components from the set of mixed signals in the spectral domain. There are many applications where the signal separation continues to be the underlying problem to be solved. In this paper, we present a computationally efficient algorithm for performing narrowband signal separation from signal mixtures for a special set of mixing coefficients in the frequency domain. The problem dealt with in this paper, apparently looks under-determined while we prove that there is a unique solution to the problem. The proposed algorithm is proved for correctness through MATLAB simulations. Results show that the proposed algorithm is efficient in terms of computational complexity which is in line with the theoretical measurements. In addition to the computational gain, the proposed algorithm is suited for high performance computing architectures like OpenCL, CUDA because the solution makes use of standard parallel computing libraries.

关键词： sensor arrays Array signal processing Spectral analysis Source separation Signal processing algorithms Delays

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fusion of Data from Radar and Depth sensors Applied for Healthcare-Oriented Characterization of Persons’ Gait

Fusion of Data from Radar and Depth Sensors Applied for Heal...

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Signal Processing algorithms, architectures, Arrangements and applications (SPA)

作者： Paweł Mazurek Jakub Wagner Roman Z. Morawski Faculty of Electronics and Information Technology Institute of Radioelectronics and Multimedia Technology Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9781728139906

The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors applied in a system for unobtrusive monitoring of elderly persons. The performance of monitoring systems, based on both types of sensors - when used separately and when used in combination - has been compared with respect to their potential for decreasing the uncertainty of estimation of several quantities, carrying information important for medical and healthcare services, viz. the state of being in motion, the direction of the movement, the travelled distance and the mean walking speed. The results of the numerical experimentation - performed on real-world data - show that the fusion of data enables more robust and accurate estimation of those parameters, and therefore more reliable monitoring of elderly persons.

关键词： Monitoring Legged locomotion sensor fusion Estimation Radar Biomedical monitoring

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A Cognitive Based Channel Assortment Using Ant-Colony Optimized Stable Path Selection in an IoTN

A Cognitive Based Channel Assortment Using Ant-Colony Optimi...

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International conference on Devices, Circuits and Systems (ICDCS)

作者： Nalluri Prophess Raj Kumar G. Josemin Bala Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9781728163680

ISBN: (纸本)9781728163697

Routing protocols have ability to discover and maintain routes in the network. However, the efficiency of designed routing protocol depends on the node capability and application requirement. IoTN (Internet of Things Network) is being adopted into real time applications such as finding shortest path to reach destination, traffic management, rerouting path based on weather conditions, happy home architectures, weather systems and so on. Despite enormous applications of IoTN, the battery life and energy efficiency are limited to fulfil applications in IoTN. Hence the selected features for the proposed algorithm contribute towards optimizing the path to select best and least distance path to destination using ant-based system. Proposed Ant-colony optimized stable path (AOSP) selection algorithm focus on network selection stages like Selection of acquaintances, channel decisions and Energy management. sensors can communicate with target BS (Base station) to forward or relay the data efficiently to reach server. The optimal intermediate sensors can act as cluster head and collect information from group of sensors with minimal energy spent which increase the throughput and decrease the packet dropping ratio.

关键词： sensors Base stations Routing Clustering algorithms Wireless sensor networks Reliability Measurement

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Aerial-DeepSearch: Distributed Multi-Agent Deep Reinforcement Learning for Search Missions

Aerial-DeepSearch: Distributed Multi-Agent Deep Reinforcemen...

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IEEE Internatonal conference on Mobile Adhoc and sensor Systems (MASS)

作者： Vidyasagar Sadhu Chuanneng Sun Arman Karimian Roberto Tron Dario Pompili Rutgers University New Brunswick NJ USA Boston University Boston MA USA

ISBN: (数字)9781728198668

ISBN: (纸本)9781728198675

Search and Rescue (SAR) is an important part of several applications of national and social interest. Existing solutions for search missions in both terrestrial and aerial domains are mostly limited to single agent and specific environments; however, search missions can significantly benefit from the use of multiple agents that can quickly adapt to new environments. In this paper, we propose a framework based on Multi-Agent Deep Reinforcement Learning (MADRL) that realizes the actor-critic framework in a distributed manner for coordinating multiple Unmanned Aerial Vehicles (UAVs) in the exploration of unknown regions. One of the original aspects of our work is that the actors represent simulated or actual UAVs exploring the environment in parallel instead of traditional computer threads. Also, we propose addition of Long Short Term Memory (LSTM) neural network layers to the actor and critic architectures to handle imperfect communication and partial observability scenarios. The proposed approach has been evaluated in a grid world and has been compared against other competing algorithms such as Multi-Agent Q-Learning, Multi-Agent Deep Q-Learning to show its advantages. More generally, our approach could be extended to image-based/continuous action space environments as well.

关键词： Instruction sets Neural networks Reinforcement learning Unmanned aerial vehicles sensor systems Observability Long short term memory

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Improved decentralized multi-sensor navigation system for airborne applications

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GPS SOLUTIONS 2018年第3期22卷 1-13页

作者： Jiang, Wei Li, Yong Rizos, Chris Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Engn Res Ctr EMC & GNSS Technol Rail Tran State Key Lab Rail Traff Control & Safety Beijing Peoples R China Navexplorer Technol Sydney NSW Australia Univ New South Wales Sch Civil & Environm Engn Sydney NSW Australia

The integration of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) technologies is a very useful navigation option for high-accuracy positioning in many applications. However, its performance is still limited by GNSS satellite availability and satellite geometry. To address such limitations, a non-GNSS-based positioning technology known as "Locata" is used to augment a standard GNSS/INS system. The conventional methods for multi-sensor integration can be classified as being either in the form of centralized Kalman filtering (CKF), or decentralized Kalman filtering. However, these two filtering architectures are not always ideal for real-world applications. To satisfy both accuracy and reliability requirements, these three integration algorithms-CKF, federated Kalman filtering (FKF) and an improved decentralized filtering, known as global optimal filtering (GOF)-are investigated. In principle, the GOF is derived from more information resources than the CKF and FKF algorithms. These three algorithms are implemented in a GPS/Locata/INS integrated navigation system and evaluated using data obtained from a flight test. The experimental results show that the position, velocity and attitude solution derived from the GOF-based system indicate improvements of 30, 18.4 and 20.8% over the CKF- and FKF-based systems, respectively.

关键词： GNSS Locata INS Multi-sensor navigation Data fusion

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Towards Computationally-Efficient Cognitive sensor Systems for Autonomous Vehicles 18

Towards Computationally-Efficient Cognitive Sensor Systems f...

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18th IEEE International conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Rajanarayana, Shashanka Marigi Kumar, Sumeet Zjajo, Amir van Leuken, Rene Delft Univ Technol Circuits & Syst Grp Delft Netherlands

ISBN: (纸本)9781728114194

Advanced driving assistance systems (ADAS) prepave regulators, consumers and corporations for the medium-term reality of autonomous driving with adaptive cruise control, collision avoidance and lane departure warning system. Various sensors like camera, RADAR and LIDAR, integrated into the vehicle assist driving. In addition, deep learning approaches are utilized in a wide range of applications ranging from object detection and scene segmentation to engine fault diagnosis and emission management to detect vehicle network intrusion. In this paper, we scope out the state of the art sensors subsystems in terms of its functionality, characteristics, specifications and communication protocol, and we describe cognitive deep learning based algorithms required for environment perception through these sensors. Subsequently, we analyze the cognitive algorithm by profiling the standard deep learning models, explore different compute platforms and possible algorithm and hardware optimization scenarios.

关键词： Vision radar lidar deep learning convolution neural networks spatial architectures ASIC GPU

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On Update Protocols in Wireless sensor Networks 1

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6th International conference on sensor Networks, sensorNETS 2017 and 7th International conference on sensor Networks, sensorNETS 2018

作者： Schwindl, Tobias Volbert, Klaus Schwab, Maximilian Ostbayerische Technische Hochschule Regensburg Prüfeninger Straße 58 Regensburg93059 Germany

ISBN: (数字)9783030301101

ISBN: (纸本)9783030301095

There has been a lot of research been done in the domain of Wireless sensor Networks in recent years. Nowadays, Wireless sensor Networks are in operation in a wide range of different scenarios and applications, like energy management services, heat and water billing as well as smoke detectors. However, research and development will be continued in this domain. During the operation of such a network, software updates need to be done seldom. In contrast to this, software updates need to be done very frequently during development and testing for uploading a new firmware on umpteen nodes. In this paper, we examine such a software update for a particular, but popular and often used sensor network platform. There are already interesting research papers about the process of updating sensor nodes. Our specific focus relies on the technical part of such an update process. We will argue why these already existing update processes do not cover our defiances. The objective of our software update protocol is to enable the developer to update many nodes in a reliable and very fast fashion during the development and testing process. For this reason, energy consumption is considered only marginally. We do not need a multi-hop protocol, due to the fact that all devices are in range, e.g. in a laboratory. In this paper we survey well known update protocols and architectures for software updates in WSN, discuss the solutions and compare them to our approach. As a conclusion of our extensive simulation follows to sum up that the developed protocols do a fast and scalable as well as a reliable update. © 2019, Springer Nature Switzerland AG.

关键词： Firmware

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