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Analysis of Wireless Multimedia sensor Network 2

Analysis of Wireless Multimedia Sensor Network

引用

2nd International conference on Power Energy Environment and intelligent Control, PEEIC 2019

作者： Yadav, Virender Arora, Ginni Amity Institute of Information Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9781728117935

There is many applications of wireless multimedia sensor networks such as surveillance system and telemedicine. To ensure to have a broad deployment of the services strip requirement processing of multimedia content should be followed. There are limited technological resources that can be taken into account in terms of energy computation and brand with storage. So the wireless networks are very essential to achieve goals that can be hard to implement due to the existence of multimedia data that ordinarily requires complex compression and combination algorithms. This paper also reviews on focus of the performance and analysis of wireless multimedia sensor network and also gives a clear understanding of its performance. This paper describe the various application based on wireless multimedia network sensors and its architecture. © 2019 IEEE.

关键词： Wireless sensor networks

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Adaptive sampling rate converter for wireless sensor networks

Adaptive sampling rate converter for wireless sensor network...

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International conference on Soft Computing and Signal processing, ICSCSP 2018

作者： Swetha, P. Srinivasa Rao, S. Chandrasekhar Reddy, P. JNTUH HyderabadTelangana India Malla Reddy College of Engineering and Technology SecunderabadTelangana India JNTUCE HyderabadTelangana India

ISBN: (纸本)9789811335990

With the advent of technology, wireless sensor networks (WSNs) are becoming more preferable for diverse applications like in industrial settings for machine monitoring and in large buildings and bridges for structural integrity monitoring. These WSNs comprise different types of sensors based on the application. Each of these sensors transmits their information at different sampling rates to the central gateway (like IOT gateway) in the WSN. In this scenario, to meet the real-time rates, adaptive sampling rate converters (ASRCs) are necessary to the gateway for up/down converting the data collected from different sensors of the network. Therefore, this paper emphasizes the necessity of an efficient ASRC in wireless sensor networks (WSNs), and a case study for an audio is presented using different sampling rate converter structures. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Wireless sensor networks

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A framework of optimizing the deployment of IoT for precision agriculture industry 24

A framework of optimizing the deployment of IoT for precisio...

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24th KES International conference on Knowledge-Based and intelligent information and Engineering Systems, KES 2020

作者： Chehri, Abdellah Chaibi, Hasna Saadane, Rachid Hakem, Nadir Wahbi, Mohamed University of Quebec in Chicoutimi QuébecG7H 2B Canada SIRC/LaGeS-EHTP EHTP Km 7 Route El Jadida Oa sis Morocco Université du Québec en Abitibi-Témiscamingue Val d'OrQCJ9P 1Y3 Canada

The massive growth of wireless communications in recent years is mostly due to new connectivity demands and advances in technology development of low power) transceivers. An example of the unique demands is the increasing exchange of data in Internet services, which has led to wireless network deployment for data transmissions. The coordination of the IoT devices, smart systems, and agriculture can contribute directly to the development of the farmer's practices by building their farm more intelligent and digital. However, enhancing farming practices requires inspecting farm equipment and farmer's experiences, which can be analyzed through the interconnectedness of IoT objects to collect farm data over the Internet to launch smart digital agriculture. It is challenging to control all farming processes (especially in real-time), this remaining as the main limitation of traditional farming. In this work, we focus on how wireless sensors can play a vital role in smart farm systems and allow processing the large amount of data generated in batches or real-time to analyze it, retrieve insights from it, and create a Smart Digital Farm. This paper proposes hierarchical-logic mapping and deployment algorithms to tackle the problem of poor network connectivity and sensing coverage in random IoT deployment. © 2020 The Authors. Published by Elsevier B.V.

关键词： Wireless sensor networks

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The vehicle classification based on neural networks in intelligent transport systems 17

The vehicle classification based on neural networks in intel...

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17th IEEE International conference on Emerging eLearning Technologies and Applications, ICETA 2019

作者： Frniak, M. Kamencay, P. Markovic, M. Dubovan, J. Dado, M. University of Zilina Department of Multimedia and Information-communication Technologies Zilina Slovakia

ISBN: (纸本)9781728149677

This article is aimed to new methods of measurement, transfer and processing of data from complex system of FibreBragg Grating (FBG) sensors. State of the art technologies and algorithms for vehicle recognition and categorisation in university campus are used. Content of the Article is divided into two parts. The first part is devoted to fibre optic FBG sensors and their basic properties. The second part ofarticle is dedicated to visual data processing by neural networks for visual vehicle categorisation as visual confirmation of data from the fibre Bragg grating sensor arrays. Experimental results show that using AlexNet neural network can be achieved an accuracy for categorization of vehicles of 87.1%. Research is done under the grant project dealing with coexistence of photonic sensor systems and photonic networks within the Internet of Things. © 2019 IEEE.

关键词： Vehicles

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Forecast of air temperature based on BP neural network

Forecast of air temperature based on BP neural network

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International conference on intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： ZhengCun Jiang WenPing Jiang Electronic and electrical engineering Shanghai Institute of Technology Shanghai china

ISBN: (数字)9781728180229

ISBN: (纸本)9781728180236

The change of temperature is closely related to people's life, and the drastic change of the next day's temperature will affect people's normal life, so it is very important to accurately predict the next day's temperature. information fusion technology is a process of automatic analysis and comprehensive processing of multi-source information in order to complete the required decision-making and evaluation tasks. BP neural network is one of the information fusion algorithms, which can predict the data collected by various sensors. Therefore, the data collected by Canberra sensor, such as maximum temperature, minimum temperature, rainfall and maximum wind speed, are processed, and the BP neural network is constructed to predict the maximum and minimum temperature of the next day. The experimental results show that this method can well predict the maximum and minimum temperature of the next day.

关键词： Temperature sensors Temperature distribution Neurons sensor fusion Predictive models Data models Biological neural networks

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Under-determined Blind Speech Separation via the Convolutive Transfer Function and lp Regularization

Under-determined Blind Speech Separation via the Convolutive...

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International conference on Mobile Ad-hoc and sensor networks, MSN

作者： Liu Yang Junjie Yang Yi Guo School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT (GDUT) Guangzhou China Centre for Research in Mathematics School of Computing Engineering and Mathematics Western Sydney University Parramatta Australia

ISBN: (纸本)9781665406697

Convolutive blind speech separation (CBSS) attempts to recover speech sources from recorded mixtures transmitted by room impulse response filters (RIRs). The existing BSS methods are mainly based on a multiplicative narrowband approximation (MNA) model in the short-time Fourier transform (STFT) domain. However, the MNA model may result in serious system transformation error under the situation of a strongly reverberant environment due to its sensitivity to RIRs with large lengths. Moreover, the solution of source reconstruction is not unique when the system is underdetermined even if the RIRs are given in advance. To tackle these problems, a convolutive narrowband approximation (CNA) model is explored in the proposed approach to constrain the system transformation error. Then an $l p(0\lt p\leq 1)$ regularization is provided to reconstruct the sparse sources in which p can be dynamically selected to achieve the best performance. The experimental results demonstrate the robustness of the proposed method to room reverberation under various speech separation cases in comparison to conventional methods.

关键词： Sensitivity Fourier transforms Transfer functions Reconstruction algorithms Robustness sensors Reverberation

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Solving the multiple charging vehicles scheduling problem for wireless rechargeable sensor networks using cuckoo search approach 14th

Solving the multiple charging vehicles scheduling problem fo...

引用

14th International conference on intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2018

作者： Cheng, Rei-Heng Chen, Shang-Kuan Information Engineering College Yango University Fuzhou350015 China

ISBN: (纸本)9783030037475

Wireless rechargeable sensor networks (WRSNs) get the focus of attention recently due to the rapid progress in wireless charging technology. Since the loading of each sensor is different, sensors request for charging in different frequencies. Also, sensors may deplete their energy quickly and need to be charged urgently under some circumstances. Therefore, a good charging route should not only minimize the moving distance of the charging device to save its energy but also charge all the sensors in time to keep the entire network working properly. In this paper, a cuckoo search approach is proposed to solve this complex problem. Based on the K-center concept, all the recharging tasks are divided into groups according to the location of sensors waiting to be charged. Preliminary simulation results show that the pre-grouping strategy can further improve the performance of the proposed cuckoo search approach. © Springer Nature Switzerland AG 2019.

关键词： Scheduling

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Supporting Data Stream Analytical processing in Vehicular sensor networks

Supporting Data Stream Analytical Processing in Vehicular Se...

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IEEE intelligent Transportation Systems conference (IEEE-ITSC)

作者： Nie, Wendi Duan, Yaoxin Lee, Victor C. S. Liu, Kai Chongqing Univ Sch Automat Chongqing Peoples R China Minist Educ Key Lab Ind Internet Things & Networked Control Chongqing Peoples R China Chongqing Univ Posts & Telecommun Dept Automat Chongqing Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Chongqing Univ Dept Comp Sci Chongqing Peoples R China

ISBN: (纸本)9781538670248

In the past decade, the Vehicular sensor Network (VSN) technology has emerged as a promising technique to support intelligent Transportation Systems (ITSs). Utilizing the information collection and communication capabilities provided by VSN, data can be firstly collected by in-vehicle sensors, and then uploaded to the infrastructure to support ITS applications. However, to get a global view of the status of the road, many ITS applications adopt a centralized approach, which requires to collect data from VSNs to a central server. In addition, to provide timely services, data has to be updated by vehicles continuously. As a result, massive amount of data could be generated by vehicles and transmitted in the network, which may exhaust the limited wireless communication bandwidth. In this work, we propose a data stream analytical processing framework for VSN named Streaming Vehdoop (SVehdoop). To reduce the bandwidth consumption, SVehdoop schedules part of data processing tasks to where the data is located. Specifically, SVehdoop utilizes the computing capability of vehicles to efficiently process the collected data over a large number of vehicles in a distributed manner. A dynamic clustering algorithm, named Streaming Vehdoop Clustering (SVC) algorithm, is tailor-designed for SVehdoop to not only consider vehicle mobility to form stable clusters, but also to take account of data aggregation and data parallelism. Comprehensive experiments have been conducted to demonstrate the efficiency of SVehdoop and the proposed SVC algorithm.

关键词： sensor networks

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Shape reconstruction of CCD camera-based soft tactile sensors

Shape reconstruction of CCD camera-based soft tactile sensor...

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2020 IEEE/RSJ International conference on intelligent Robots and Systems (IROS)

作者： Gabor Soter Helmut Hauser Andrew Conn Jonathan Rossiter Kohei Nakajima University of Bristol United Kingdom Bristol Robotics Laboratory United Kingdom Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

CCD camera-based tactile sensors provide high-resolution information about the deformation of soft and elastic interfaces. However, they have poor scalibility as it is difficult to sense a large surface area without increasing the distance between the camera and the interface or using multiple processing chips. For example, using such tactile sensors for a whole robotic arm is not yet possible. In this work, we demonstrate a data driven method that can reconstruct the high-resolution information about deformation of the soft interface while keeping the space requirements and power consumption relatively low. Our modified tactile sensor incorporates two independent sensing techniques, one low- and one high-resolution, and we learn to map to the latter from the former. As a low-resolution sensor, we use liquid-filled channels that transmit the information from the location of the tactile interaction to a rigid display, where the liquid displacements are tracked by a CCD camera. Simultaneously, the same interaction is measured by tracking the markers on the bottom of the sensor using a second CCD camera. After data collection, we train two different machine learning models to reconstruct the time series of the high-resolution sensor. By training a convolutional autoencoder (CAE) and attaching it to the recurrent neural network (RNN), we demonstrate the reconstruction of high-resolution video frames using only the time series of the low-resolution sensor.

关键词： Charge coupled devices Recurrent neural networks Time series analysis Tactile sensors Robot sensing systems Cameras Strain

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A Spiking Network for Inference of Relations Trained with Neuromorphic Backpropagation

A Spiking Network for Inference of Relations Trained with Ne...

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International Joint conference on Neural networks (IJCNN)

作者： Thiele, Johannes C. Bichler, Olivier Dupret, Antoine Solinas, Sergio Indiveri, Giacomo CEA LIST Gif Sur Yvette France Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781728119854

The increasing need for intelligent sensors in a wide range of everyday objects requires the existence of low power information processing systems which can operate autonomously in their environment. In particular, merging and processing the outputs of different sensors efficiently is a necessary requirement for mobile agents with cognitive abilities. In this work, we present a multi-layer spiking neural network for inference of relations between stimuli patterns in dedicated neuromorphic systems. The system is trained with a new version of the backpropagation algorithm adapted to on-chip learning in neuromorphic hardware: Error gradients are encoded as spike signals which are propagated through symmetric synapses, using the same integrate-and-fire hardware infrastructure as used during forward propagation. We demonstrate the strength of the approach on an arithmetic relation inference task and on visual XOR on the MNIST dataset. Compared to previous, biologically-inspired implementations of networks for learning and inference of relations, our approach is able to achieve better performance with less neurons. Our architecture is the first spiking neural network architecture with on-chip learning capabilities, which is able to perform relational inference on complex visual stimuli. These features make our system interesting for sensor fusion applications and embedded learning in autonomous neuromorphic agents.

关键词： Neurons Neuromorphics Biological neural networks Backpropagation Synapses Visualization

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