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3rd international symposium of Space Optical Instruments and Applications: Beijing, China June 26 - 29th 2016

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2018年

作者： H Paul Urbach Guangjun Zhang

ISBN: (纸本)9783319840987

This volume contains selected and expanded contributions presented at the 3rd symposium on Space Optical Instruments and Applications in Beijing, China June 28 29, 2016. This conference series is organised by the Sino-Holland Space Optical Instruments Laboratory, a cooperation platform between China and the Netherlands. The symposium focused on key technological problems of optical instruments and their applications in a space context. It covered the latest developments, experiments and results regarding theory, instrumentation and applications in space optics. The book issplit acrossfive topical sections. The first section covers space optical remote sensing system design, the second advanced optical system design, the third remote sensor calibration and measurement. Remote sensing data processing and information extraction is then presented, followed by a final section on remote sensing data applications.

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Low Cost Energy Efficient Smart Security System with information Stamping for IoT networks 3

Low Cost Energy Efficient Smart Security System with Informa...

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3rd international Conference On Internet of Things: Smart Innovation and Usages, IoT-SIU 2018

作者： Kumar, Jayendra Ramesh, Paidi Raja Dept. Electronics Communication Engg National Institute of Technology Jamshedpur India

ISBN: (纸本)9781509067855

A security system with CCTV and other network nodes in IoT deals with large amount of data, so the need for devices with larger storage space comes into the picture which is a bit costlier. Traditional security systems are more power consuming as it has to record for 24x7 with more throughput but less efficient. Thus need human in middle to upload into cloud is needed. In this work, we propose a low-cost energy efficient smart security system for CCTV with other network nodes. Whenever motion is detected by passive infrared (PIR) sensor, at that time only the camera and all other security sensor nodes in network are activated (Switched on), the captured video and information (sensor data) are stamped on the image using image processing techniques on Python platform. It will be stored in a local storage device, after certain threshold (based on sensitivity of the location) data will be uploaded into the cloud along with data log created during processing stage by Python programming model. In this way, we can reduce the amount of data to be stored (as no recording when idle), consumed power (as device is switched off when idle), maintenance cost(as fully programmed). No need for human in middle for uploading data into the cloud (as program will upload data in to cloud). Because of time, date and information (data from sensor nodes) stamping on each frame of video using python programming model video access becomes easier, as data log will be created. © 2018 IEEE.

关键词： Security systems

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Neural network based characterization and reliable routing of data in wireless body sensor networks 3rd

Neural network based characterization and reliable routing o...

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3rd international Conference on Cognitive Computing and information processing, CCIP 2017

作者： Shilpa, Biradar Hiremath, S.G. Thippeswamy, G. Dr. AIT Bangalore India EWIT Bangalore India BMSIT Bangalore India

ISBN: (纸本)9789811090585

Health is very important to each one and for the early discovery of different diseases to get timely treatment Wireless Body sensor networks (WBSNs) a subgroup of wireless sensor networks can be used. WBSNs works for collecting the health related data such as ECG, EEG, Glucose values, temperature etc. from human body and route the data towards the destination so that the patients information can be sent to the concerned person. In WBSN the sensor node can either be appropriately located on the body or inserted inside the body. In this work firstly the EEG signals characterization is done with the help of neural networks. EEG signal processing is done with the help of DWT. Then in the secondly routing mechanism used to send the data to the destination. In the health monitoring system routing has an important role. The facts sensed by the sensors need to be routed to the destination effectively. In this work a routing algorithm proposed which can be used to transfer the data to destination efficiently. To route the data to the sink multi hop process is used and the cost function is used to find the node next closest node in the route from source to destination. The experiments are done in MATLAB environment and simulation result shows that efficient classification of EEG signal is done and the proposed scheme provides the less end-to-end as well as good throughput. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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6D Object Pose Estimation for Robot Programming by Demonstration 1

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19th international symposium on Optomechatronic Technology (ISOT)

作者： Ghahramani, Mohammad Vakanski, Aleksandar Janabi-Sharifi, Farrokh Ryerson Univ Eric Palin Hall87 Gerrard St East Toronto ON M5B 2K3 Canada Univ Idaho 1776 Sci Ctr Dr Idaho Falls ID 83402 USA

ISBN: (数字)9789813296329

ISBN: (纸本)9789813296329;9789813296312

Estimating the position and orientation (pose) of objects in images is a crucial step toward successful robot programming by demonstration using visual task learning. Currently, a number of algorithms exist for detecting and tracking objects in images, including conventional image processing methods and the state-of-the-art methods based on deep learning architectures. However, the problem of accurate estimation of 6D poses of objects in a sequence of video frames still poses challenges. In this paper, we present a novel deep learning method for pose estimation based on data augmentation and nonlinear regression. For training purposes, thousands of images associated with views of different poses of an object are generated based on a known CAD model of the object geometry. The trained deep neural network is employed for accurate and real-time estimation of the orientation of the object. The object position coordinates in the demonstrations are obtained from the depth information of the scene captured by a Microsoft Kinect v2.0 sensor. The resulting 6-dimensional poses are estimated at each time frame and are employed for learning robotic tasks at a trajectory level of abstraction. Robot inverse kinematics is applied to generate a program for robotic task execution. The proposed method is validated for transferring new skills to a robot in a painting application.

关键词： Deep neural networks

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Optimization of Multi-function sensor Placement Satisfying Detection Coverage 3rd

Optimization of Multi-function Sensor Placement Satisfying D...

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3rd European-Alliance-for-Innovation (EAI) international Conference on Industrial networks and Intelligent Systems (INISCOM)

作者： Liang, Qingzhong Fan, Yuanyuan China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9783319741765;9783319741758

Wireless sensor networks (WSNs) have become essential parts in Industrial Internet of Things (IIoT). However, owing to the type associated with data acquisition and the large scale of monitoring, sensors are often installed at a lot of locations and a wide variety of sensors make WSN topology more complex. To address these limitations, a complementary promising solution, known as software defined wireless sensor network (SDWSN), is proposed. SDWSN acquires desired information based on users' demands from large-scale sensor networks by dynamically customizing its function. Thanks to the SDWSN, multi-type data sensing is able to enlarge the sensing scale and reduce the cost. Existing sensor placement techniques are usually focus on simple function sensor or multi-type sensor. Witness the development of SDWSN, it is ideal to explore such abilities such that the multi-type sensing functions can be conducted in a same node. Because each area covered by different multi-function sensor nodes has different detection requirements, multi-function sensor nodes placement faces many challenges. In this paper, based on multi-objective decomposition, we study the number and function redundancy of all nodes minimization problem in multi-function sensor nodes placement. Specially, we propose an improved MOEA/D-DE algorithms based on orthogonal experiment design. Simulation and evaluations validate the efficiency of our proposal.

关键词： WSN Placement Multi-objective Optimization

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VR-DANN: Real-Time Video Recognition via Decoder-Assisted Neural Network Acceleration

VR-DANN: Real-Time Video Recognition via Decoder-Assisted Ne...

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IEEE/ACM international symposium on Microarchitecture (MICRO)

作者： Zhuoran Song Feiyang Wu Xueyuan Liu Jing Ke Naifeng Jing Xiaoyao Liang Shanghai Jiao Tong University China Biren Research China

ISBN: (数字)9781728173832

ISBN: (纸本)9781728173849

Nowadays, high-definition video object recognition (segmentation and detection) is not within the easy reach of a real-time task in a consumer SoC due to the limited on-chip computing power for neural network (NN) processing. Although many accelerators have been optimized heavily, they are still isolated from the intrinsic video compression expertise in a decoder. Given the fact that a great portion of frames can be dynamically reconstructed by a few key frames with high fidelity in a video, we envision that the recognition can also be reconstructed in a similar way so as to save a large amount of NN computing power. In this paper, we study the feasibility and efficiency of a novel decoder-assisted NN accelerator architecture for video recognition (VR-DANN) in a conventional SoC-styled design, which for the first time tightly couples the working principle of a video decoder with the NN accelerator to provide smooth high-definition video recognition experience. We leverage motion vectors, the simple tempo-spatial information already available in the decoding process to facilitate the recognition process, and propose a lightweight NN-based refinement scheme to suppress the non-pixel recognition noise. We also propose the corresponding microarchitecture design, which can be built upon any existing commercial IPs with minimal hardware overhead but significant speedup. Our experimental results show that the VR-DANN-parallel architecture achieves 2.9× performance improvement with less than 1% accuracy loss compared with the state-of-the-art "FAVOS" scheme widely used for video recognition. Compared with optical flow assisted "DFF" scheme, it can achieve 2.2× performance gain and 3% accuracy improvement. As to another "Euphrates" scheme, VR-DANN can achieve 40% performance gain and comparable accuracy.

关键词： Artificial neural networks Streaming media Video compression Performance gain Real-time systems Decoding Image reconstruction

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Quantle: Fair and Honest Presentation Coach in Your pocket

Quantle: Fair and Honest Presentation Coach in Your pocket

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international symposium on information processing in sensor networks (IPSN)

作者： Olga Saukh Balz Maag TU Graz / CSH Vienna Institute of Technical Informatics Austria ETH Zurich Computer Engineering and Networks Lab Switzerland

ISBN: (纸本)9781728107271

Great public speakers are made, not born. Practicing a presentation in front of colleagues is common practice and results in a set of subjective judgements what could be improved. In this paper we describe the design and implementation of a mobile app which estimates the quality of speaker's delivery in real time in a fair, repeatable and privacy-preserving way. Quantle estimates the speaker's pace in terms of the number of syllables, words and clauses, computes pitch and duration of pauses. The basic parameters are then used to estimate the talk complexity based on readability scores from the literature to help the speaker adjust his delivery to the target audience. In contrast to speech-to-text-based methods used to implement a digital presentation coach, Quantle does processing locally in real time and works in the flight mode. This design has three implications: (1) Quantle does not interfere with the surrounding hardware, (2) it is power-aware, since 95.2 % of the energy used by the app on iPhone 6 is spent to operate the built-in microphone and the screen, and (3) audio data and processing results are not shared with a third party therewith preserving speaker's privacy. We evaluate Quantle on artificial, online and live data. We artificially modify an audio sample by changing the volume, speed, tempo, pitch and noise level to test robustness of Quantle and its performance limits. We then test Quantle on 1017 TED talks held in English and compare computed features to those extracted from the available transcript processed by online text evaluation services. Quantle estimates of syllable and word counts are 85.4 % and 82.8 % accurate, and pitch is over 90 % accurate. We use the outcome of this study to extract typical ranges for each vocal characteristic. We then use Quantle on live data at a social event, and as a tool for speakers to track their delivery when rehearsing a talk. Our results confirm that Quantle is robust to different noise levels, varying dista

关键词： Real-time systems Tools Feature extraction Estimation Complexity theory Microphones information processing

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An agent-based approach for dynamic big data processing in a smart city environment 3

An agent-based approach for dynamic big data processing in a...

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3rd international Conference on Advanced Aspects of Software Engineering, ICAASE 2018

作者： Elaggoune, Zakarya Maamri, Ramdane Boussebough, Imane LIRE Laboratory Constantine 2 University Constantine25000 Algeria

The big data era brought us new processing and information management challenges to face. The existing tools managed to control the ongoing challenges, and the current architectures are close to meeting the needs of the users. But the volume rate at which new data is generated leads to new rising challenges. This is especially true in the context of smart cities, where gathering information in an energy-efficient manner to prolong the lifetime of Wireless sensor networks (WSNs);and adapting the analytical mechanism to support the speed at which new data is generated to deliver real-time results dynamically are the two key rising challenges. This article aims at exploring and describing how Multi-Agent Systems (MAS) can handle a large amount of data with a dynamic analytics capabilities and in an energy-efficient manner. Copyright © by the paper’s authors. Copying permitted for private and academic purposes.

关键词： Smart city

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3rd international Conference on Signal and information processing, Networking and Computers, ICSINC 2017

3rd International Conference on Signal and Information Proce...

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3rd international Conference on Signal and information processing, Networking and Computers, ICSINC 2017

ISBN: (纸本)9789811075209

The proceedings contain 61 papers. The special focus in this conference is on Signal, information processing, Networking and Computers. The topics include: An Energy Detection Based on Coefficient of Variation for Spectrum Sensing in Cognitive Radio;performance Analysis for User-Centric Cloud Radio Access Network in Millimeter Wave;millimeter Wave Cloud Radio Access Network Coverage and Capacity;Optimal Downtilts for 3D Beamforming Based on Greedy Algorithm in Massive MIMO networks with Imperfect CSI;Dynamic User Scheduling Algorithms for Massive MIMO Multicast System;a Bat Algorithm Based on Centroid Strategy;an Optimized Daisy-chain Topology for Multi-load Interconnection in High–speed and High–density Electronic Systems;Realization and Optimization of Pulse Compression Algorithm on OpenCL-Based FPGA Heterogeneous Computing Platform;The Research of SAR processing Performance Based on Multi-core GPU;A PHY-Based Secret Sharing Scheme in MIMO Systems;ship Detection in Optical Satellite Images Based on Sparse Representation;application of Back Propagation Neural Network with Simulated Annealing Algorithm in Network Intrusion Detection Systems;an Improved Binary Bee Colony Algorithm for Satellite Resource Scheduling Method;Automatic Liver Segmentation on CT Images;An Improved Blind Spectrum Sensing Algorithm Based on QR Decomposition and SVM;Sentiment Analysis Using Modified LDA;co-training Based on Multi-type Text Features;GNSS Spoofing Jamming Recognition Based on Machine Learning;TinyPEP: Tiny Pairwise-Key Establishment Protocol for Wireless sensor networks;An Innovative Indoor Location Algorithm Based on Supervised Learning and WIFI Fingerprint Classification;long Short-Term Memory Network for Wireless Channel Prediction;deep Learning and Machine Learning for Object Detection in Remote Sensing Images.

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3rd international Conference on Cognitive Computing and information processing, CCIP 2017

3rd International Conference on Cognitive Computing and Info...

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3rd international Conference on Cognitive Computing and information processing, CCIP 2017

ISBN: (纸本)9789811090585

The proceedings contain 43 papers. The special focus in this conference is on Cognitive Computing and information processing. The topics include: Influence of health service infrastructure on the infant mortality rate: An econometric analysis of Indian States;Eyes open and eyes close activity recognition using EEG signals;health and nutritional status of children: Survey, challenges and directions;an empirical analysis of machine learning classifiers for clinical decision making in Asthma;Web based blood donation management system (BDMS) and notifications;silent speech recognition;swarm intelligent approaches for solving shortest path problems with multiple objectives;improved directionally driven self-regulating particle swarm optimizer;judgement of learning for metacognitive type-2 fuzzy inference system;inter intensity and color channel co-occurrence histogram for color texture classification;LDA based discriminant features for texture classification using WT and PDE approach;Position error analysis of IRNSS data using big data analytics;analysis of breast thermal images using anisotropic diffusion filter based modified level sets and efficient fractal algorithm;cluster representation and discrimination based on regression line;recognition of traffic sign based on support vector machine and creation of the Indian traffic sign recognition benchmark;neural network based characterization and reliable routing of data in wireless body sensor networks;automata approach to reduce power consumption in smart grid cloud data center;Hybrid optimization in big data: Error detection and data repairing by big data cleaning using CSO-GSA;Application of optimization technique for performance and emission characteristics of a CNG-diesel dual fuel engine: A comparison study.

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