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A Sensor Data processing and Storage Method for Wireless Body Area networks

A Sensor Data Processing and Storage Method for Wireless Bod...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Lu Han Lulu Xun Jinfa Liu Wenjun Yin Xiangwei Zheng Zhuoran Zheng Shandong Normal University Ji’nan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University Ji’nan China

This paper proposes an adaptive processing method and a dynamic storage method of pulsed wave signal based on wireless body area network. Firstly, the ZigBee star network model is adopted as the basic network architecture of the system. Secondly, an adaptive Gaussian filter model is designed to eliminate the influence of environmental and man-made factors. Finally, the dynamic storage algorithm is used to map the pulsed wave signal to a new stored sample, and the regression model is used to restore the new sample to the original signal. Simulation experiments show that: 1) The adaptive Gaussian model minimizes the signal noise and better maintains the characteristics of the pulsed wave signal. 2) On the premise of using ARIMA regression model of neural network, dynamic storage algorithm improves the regression accuracy compared with fixed storage algorithm.

关键词： Information technology Education

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Long Short-Term Memory network for Wireless Channel Prediction 3rd

Long Short-Term Memory Network for Wireless Channel Predicti...

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3rd conference on Signal and Information processing - networking and Computers (ICSINC)

作者： Tong, Xiaoyun Sun, Songlin Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv BUPT Minist Educ Beijing Peoples R China Beijing Univ Posts & Telecommun Natl Engn Lab Mobile Network Secur Beijing Peoples R China

ISBN: (纸本)9789811075216;9789811075209

In modern wireless systems, channel prediction is an effective way to overcome the feedback delay of channel state information (CSI). When the receiver performs adaptive transmission based on the feedback CSI, the channel prediction algorithm can reduce the system overhead by predicting the future CSI. In this paper, we provide a long short-term memory (LSTM) network for wireless channel prediction. This method can get a smaller prediction error than other intelligence methods. Experiments show that the LSTM model has a lower normalized mean square error (NMSE) and less running time than support vector machine, artificial neural network, and recurrent neural network prediction approaches.

关键词： Wireless channel prediction Channel state information Long short-term memory network

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Tensor product generation networks for deep nlp modeling

Tensor product generation networks for deep nlp modeling

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2018 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018

作者： Huang, Qiuyuan Smolensky, Paul He, Xiaodong Deng, Li Wu, Dapeng Microsoft Research WA United States JD AI Research University of Florida FL United States

ISBN: (纸本)9781948087278

We present a new approach to the design of deep networks for natural language processing (NLP), based on the general technique of Tensor Product Representations (TPRs) for encoding and processing symbol structures in distributed neural networks. A network architecture - the Tensor Product Generation network (TPGN) - is proposed which is capable in principle of carrying out TPR computation, but which uses unconstrained deep learning to design its internal representations. Instantiated in a model for image-caption generation, TPGN outperforms LSTM baselines when evaluated on the COCO dataset. The TPR-capable structure enables interpretation of internal representations and operations, which prove to contain considerable grammatical content. Our caption-generation model can be interpreted as generating sequences of grammatical categories and retrieving words by their categories from a plan encoded as a distributed representation. © 2018 The Association for Computational Linguistics.

关键词： network architecture

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Deep Learning for Compressed Sensing Based Channel Estimation in Millimeter Wave Massive MIMO

Deep Learning for Compressed Sensing Based Channel Estimatio...

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International conference on Wireless Communications and Signal processing (WCSP)

作者： Wenyan Ma Chenhao Qi Zaichen Zhang Julian Cheng School of Information Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China School of Engineering University of British Columbia Canada

Channel estimation is considered for multi-user millimeter wave (mmWave) massive multi-input multi-output system. A deep learning compressed sensing (DLCS) channel estimation scheme is proposed, and it consists of beamspace channel amplitude estimation and channel reconstruction. The neural network (NN) for the DLCS scheme is trained offline using simulated environments according to the mmWave channel model. Then the correlation between the received signal vectors and the measurement matrix is input into the trained NN to predict the beamspace channel amplitude. Afterwards, the channel is reconstructed based on the obtained indices of dominant beamspace channel entries. Simulation results demonstrate that the proposed DLCS channel estimation scheme outperforms the existing schemes including the orthogonal matching pursuit and the distributed grid matching pursuit in terms of the normalized mean-squared error and the spectral efficiency.

关键词： Channel estimation Artificial neural networks Antenna arrays Matching pursuit algorithms Radio frequency MIMO communication Amplitude estimation

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An Internet of Vehicles Intrusion Detection System Based on a Convolutional neural network

An Internet of Vehicles Intrusion Detection System Based on ...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Ruxiang Peng Weishi Li Tao Yang Kong Huafeng The Third Research Institute of the Ministry of Public Security Beijing University of Posts and Telecommunications Wuhan Business University Wuhan China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

With the continuous development of the Internet of Vehicles, vehicles are no longer isolated nodes, but become a node in the car network. The open Internet will introduce traditional security issues into the Internet of Things. In order to ensure the safety of the networked cars, we hope to set up an intrusion detection system (IDS) on the vehicle terminal to detect and intercept network attacks. In our work, we designed an intrusion detection system for the Internet of Vehicles based on a convolutional neural network, which can run in a low-powered embedded vehicle terminal to monitor the data in the car network in real time. Moreover, for the case of packet encryption in some car networks, we have also designed a separate version for intrusion detection by analyzing the packet header. Experiments have shown that our system can guarantee high accuracy detection at low latency for attack traffic.

关键词： Internet Intrusion detection Wireless sensor networks Convolutional neural networks Feature extraction Convolution Kernel

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A Subword-based Deep Learning Approach for Sentiment Analysis of Political Tweets 32

A Subword-based Deep Learning Approach for Sentiment Analysi...

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32nd IEEE International conference on Advanced Information networking and Applications (IEEE AINA)

作者： Pota, Marco Esposito, Massimo Palomino, Marco A. Masala, Giovanni L. Natl Res Council Italy Inst High Performance Comp & Networking Naples Italy Univ Plymouth Big Data Grp Plymouth Devon England

ISBN: (纸本)9781538653951

The successful use of online material in political campaigns over the past two decades has motivated the inclusion of social media platforms such as Twitter as an integral part of the political apparatus. Political analysts are increasingly turning to Twitter as an indicator of public opinion. We are interested in learning how positive and negative opinions propagate through Twitter and how important events influence public opinion. In this paper, we present a neural network-based approach to analyse the sentiment expressed on political tweets. First, our approach represents the text by dense vectors comprising subword information to better detect word similarities by exploiting both morphology and semantics. Then, a Convolutional neural network is trained to learn how to classify tweets depending on sentiment, based on an available labelled dataset. Finally, the model is applied to perform the sentiment analysis of a collection of tweets retrieved during the days prior to the latest UK General Election. Results are promising and show that the neural network approach represents an improvement over lexicon-based approaches for positive/negative sentence classification.

关键词： sentiment analysis social networks Twitter neural networks deep learning lexicon-based approach

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Fruit Tree Disease Recognition Based on Convolutional neural networks

Fruit Tree Disease Recognition Based on Convolutional Neural...

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IEEE International Symposium on Parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Zechen Zheng Shaowei Pan Yichi Zhang School of Computer Science Xi’an Shiyou University xi’an China China

ISBN: (纸本)9781728152103

In order to realize the rapid and accurate recognition of fruit tree diseases in orchard environment, this paper puts forward a deep learning model based on Convolution neural network to identify fruit tree diseases. In this paper, the data set is processed by the Sobel operator and image enhancement respectively. Then, the network depth, convolution kernel, feature maps, and fully connected layer in the Convolution neural network structure use different parameters and softmax classifier. Differently composition networks are used to train processed dataset. Convolution neural network models are used to predict test sets, and the results show that deeper Convolution neural networks and mean pooling for tiny features in the dataset are more accurate. It can achieve the disease recognition, which includes cab disease, black rot, rust of apple leaves and bacterial spot disease of peach tree leaves. The model has a good recognition function for disease identification of fruit trees and can help real-time monitoring of orchard diseases.

关键词： agricultural products convolutional neural nets feature extraction image enhancement image recognition learning (artificial intelligence) plant diseases image enhancement Image recognition agricultural products agricultural byproducts fruit trees Feature extraction Sobel operator Recognition (Psychology) Convolution maladjustment Dataset neural network Plant Diseases tree diseases Prunus persica

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A Word Embedding Model Learned from Political Tweets 13

A Word Embedding Model Learned from Political Tweets

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13th International conference on Computer Engineering and Systems (ICCES)

作者： Alnajran, Noufa N. Crockett, Keeley A. McLean, David Latham, Annabel Manchester Metropolitan Univ Dept Comp Math & Digital Technol Manchester Lancs England

ISBN: (纸本)9781538651117

distributed word representations have recently contributed to significant improvements in many natural language processing (NLP) tasks. Distributional semantics have become amongst the important trends in machine learning (ML) applications. Word embeddings are distributed representations of words that learn semantic relationships from a large corpus of text. In the social context, the distributed representation of a word is likely to be different from general text word embeddings. This is relatively due to the unique lexical semantic features and morphological structure of social media text such as tweets, which implies different word vector representations. In this paper, we collect and present a political social dataset that consists of over four million English tweets. An artificial neural network (NN) is trained to learn word co -occurrence and generate word vectors from the political corpus of tweets. The model is 136MB and includes word representations for a vocabulary of over 86K unique words and phrases. The learned model shall contribute to the success of many ML and NLP applications in microblogging Social network Analysis (OSN), such as semantic similarity and cluster analysis tasks.

关键词： Word Embedding Language Modelling Deep Learning Social network Analysis Twitter Analysis

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A distributed Sensor network for Monitoring Noise Level and Noise Sources in Urban Environments 6

A Distributed Sensor Network for Monitoring Noise Level and ...

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6th IEEE International conference on Future Internet of Things and Cloud (FiCloud)

作者： Abesser, Jakob Graefe, Robert Kuehn, Christian Clauss, Tobias Lukashevich, Hanna Goetze, Marco Kuehnlenz, Stephanie Fraunhofer Inst Digital Media Technol IDMT Ilmenau Germany IMMS Inst Mikroelekt & Mechatron Syst Gemeinnutzi Ilmenau Germany Software Serv John GmbH Ilmenau Germany

ISBN: (纸本)9781538675038

Smart city applications for acoustic monitoring become essential to cope with the overall increasing noise pollution in urban environments. This paper gives an overview over a distributed sensor network for noise monitoring in the German city of Jena. Several acoustic sensor units allow for classifying among various acoustic scenes and events using Convolutional neural networks (CNN) and measuring different noise level parameters. Connected by a communication system based on MQTT (Message Queue Telemetry Transport), these sensors communicate measurement data to a central server for data post-processing and storage. Finally, a web-based application allows for various real-time visualizations of noise exposure distributed over the city.

关键词： Smart city acoustic scene classification event detection sensor network noise level measurement TA Larm

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An Integrative Approach to Robust Hand Detection Using CPM-YOLOv3 and RGBD Camera in Real Time

An Integrative Approach to Robust Hand Detection Using CPM-Y...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Muhammad Raza Vachiraporn Ketsoi Haopeng Chen School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Innovative methods to interact with electronic devices are attracting researchers attentions, and many applications of Human Computer Interaction have been developed in recent years. Hand detection under different illuminations and backgrounds is crucial for tasks such as, actions recognition, gestures recognition and hand-based interaction with wearable devices. Many existing methods are able to detect the human hand precisely, but the task still remains challenging due to environment complexity, different invariants, processing time, and accuracy. It is required for an application to recognize the human hand precisely for proper interaction with the wearable devices. This paper proposed a robust and a compact method for hand detection by employing you only look once (YOLO) and convolutional pos machines (CPM). This paper consists of two parts, network training and testing. In the first part, Oxford hand dataset is used to train a neural network. This dataset has various hand images under different invariants, and backgrounds. Afterwards, images are captured using a digital camera in real-time and then human hands are detected using a trained neural network. In addition, detected hands, positions and orientations are calculated accordance with defined active zone parameters and a depth sensor. Our trained neural network is dubbed as HHDNet, an abbreviation for human hand detection network. The HHDNet is tested under different environment conditions, and experimental results showed that the accuracy and the frame rate of the proposed model are superior than others state of the art methods.

关键词： Image color analysis neural networks Feature extraction Cameras Robot sensing systems Estimation Skin

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