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Research on Common Tree Species Recognition by Faster R-CNN based on Whole Tree image

Research on Common Tree Species Recognition by Faster R-CNN ...

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IEEE international conference on signal and image Processing (ICSIP)

作者： Yu Li Bo Tang Jinda Li Wei Sun Zhongkang Lin Qiang Luo School of Mechinery and Automation Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665446006

In order to solve the problem of whole tree image recognition and detection, this paper introduces the object detection algorithm into the field of tree species recognition, and studies the Faster R-CNN tree species recognition method based on the whole tree image. images of 10 types of common trees were collected under natural conditions in different weather conditions and made into a dataset containing 1000 pictures. The anchor frame size of the RPN network is adjusted according to the characteristics of the detection target in the dataset image, and the comparative experiments of the three feature extraction networks of VGG16, MobileNet-V2 and ResNet-50 are carried out. It is determined that ResNet-50 is the optimal feature extraction network. ResNet-50 is used as a model for feature extraction network training to recognize and detect 150 new images, and the detection accuracy reached 98%. The detection results show that the method can effectively detect the whole tree image, which can provide a reference for the application of object detection algorithm in the detection of the whole tree image.

关键词： Training image recognition Target recognition Vegetation Object detection Feature extraction Real-time systems

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Design of Multimedia conference Control System based on SIP

Design of Multimedia Conference Control System based on SIP

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Jianyun Ni Jing Luo School of Electrical Engineering Tianjin University of Technology Tianjin China College of Computer Technology and Automation Tianjin Professional University Tianjin China

As various multimedia communication services are increasingly required by internet users, several signaling protocols have been proposed for the efficient control of multimedia communication services. However, the model and architecture of multimedia conferencing are several. In this article, we first introduce some SIP multimedia conferencing architectures, their advantages, disadvantages and performance. According to a centralized multimedia conferencing model, we construct feasible system architecture, which can support middle scale multimedia conference. After that we analysis the key technologies in centralized SIP multimedia conference, like media server design and audio mixing. Then, we presented a designed of multimedia conference system based on SIP. The SIP-based control mechanism for achieving the scalability has been designed in detail. At the end of this paper, the system's performance is evaluated and discussed.

关键词： Multimedia systems Control systems conference management Protocols Streaming media Multimedia computing Multimedia databases Distributed computing signal design Automatic control

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Performance Testbed in IoT Network based on ZigBee and NB-IoT Technologies

Performance Testbed in IoT Network Based on ZigBee and NB-Io...

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international Technical conference o Circuits/systems, Computers and Communications (ITC-CSCC)

作者： Tanakorn Inthasuth Watthanakorn Choterungrote Chatdanai Bandasak Thanawut Banboon Ngoc Thien le Department of Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Srivijaya Songkhla Thailand Department of Urban Engineering University of Architecture Ho Chi Minh City Ho Chi Minh City Vietnam

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

The inter-protocol communication system acts as an interface and is the link where problems of communication may occur. This paper is dedicated to exploring wireless communication through the ZigBee system, which offers short-range communication and supports mesh networks. ZigBee plays a pivotal role in wireless sensor network (WSN) and their related applications. Nevertheless, the WSN can be linked to the internet system through several approaches, but this study focuses on the connection through a gateway (GW) using Narrowband internet of Things (NB-IoT) technology, which is particular to the IoT system. The GW testing has been performed to measure the data transfer rate and received signal strength indicator (RSSI) for detecting the limits of the system. Our WSN consisted of 8 nodes and this was more than twice the number of the XBee buffer that was capable of receiving packets simultaneously. In addition, GW devices have been installed in the different types of enclosures to determine the signal levels for future real installation guidelines.

关键词： Wireless communication Performance evaluation Wireless sensor networks Transmitters Zigbee Received signal strength indicator internet of Things

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An IoT-based Deep Belief Network in Intrusion Detection System using Butterfly Optimization

An IoT-based Deep Belief Network in Intrusion Detection Syst...

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international conference on Communications and signal Processing

作者： Aswathy K Cherian M. Vaidhehi M Arshey Serin V Simpson J Briskilal Astitva Singh Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of AI & DS SCMS School of Engineering and Technology Ernakulam Kerala Department of Computer Science and Technology Madanapalle Institute of Technology & Science Madanapalle Chittoor Andhra Pradesh India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Rapid technological advancements in the communication and internet industries have led to a huge increase in network scale and data volume. Network security is having a hard time identifying attacks as new threats are being created. Additionally, you can’t overlook the possibility of unauthorized users trying to attack the network in various methods. Numerous technologies, such as an intrusion detection system, can be utilized to protect the network from potential threats (IDS). The collecting and dissemination of data by internet-connected devices is referred to as the “internet of Things” (IoT). There are now more privacy and security problems due to the proliferation of internet connections and the introduction of new technologies like the internet of Things (IoT). IoT incursions are becoming increasingly complex and numerous as a result of the proliferation of connected devices. Companies are boosting their investment in research and development in order to better detect these threats. In this research, Deep Belief Network (DBN) and Butterfly Optimization Algorithm (BOA) Intrusion Detection systems (IDS) are proposed for detecting Io T network assaults. In order to minimize information leakage on the test data, the UNSW-NB15 dataset was normalized using the Absolute Maximum Scaling (AMS) approach in the first stage of this study process. This dataset contains a mixture of current assaults and typical network traffic, categorized into nine different attack types. The next step was to use Principal Component Analysis to reduce the dimensionality (PCA). Last but not least, results showed that DBN-BOA was superior to other IoT intrusion detection systems in terms of accuracy and speed.

关键词： Intrusion detection Training data signal processing algorithms Telecommunication traffic Network security Data models internet of Things

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AER SPIKE-PROCESSING FILTER SIMULATOR: Implementation of an AER Simulator based on Cellular Automata

AER SPIKE-PROCESSING FILTER SIMULATOR: Implementation of an ...

引用

international conference on signal Processing and Multimedia Applications

作者： Manuel Rivas-Perez A. Linares-Barranco A. Jimenez-Fernandez A. Civit G. Jimenez Robotic and Technology of Computers Lab. University of Seville

ISBN: (纸本)9789898425720

Spike-based systems are neuro-inspired circuits implementations traditionally used for sensory systems or sensor signal processing. Address-Event-Representation (AER) is a neuromorphic communication protocol for transferring asynchronous events between VLSI spike-based chips. These neuro-inspired implementations allow developing complex, multilayer, multichip neuromorphic systems and have been used to design sensor chips, such as retinas and cochlea, processing chips, e.g. filters, and learning chips. Furthermore, Cellular Automata (CA) is a bio-inspired processing model for problem solving. This approach divides the processing synchronous cells which change their states at the same time in order to get the solution. This paper presents a software simulator able to gather several spike-based elements into the same workspace in order to test a CA architecture based on AER before a hardware implementation. Furthermore this simulator produces VHDL for testing the AER-CA into the FPGA of the USB-AER AER-tool.

关键词： Spiking neurons Address-event-representation Usb-aer Vhdl Fpga image filtering Neuro-inspired Cellular automata

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A Low-Complexity Path Metric for Tree-based Multiple-Antenna Detectors

A Low-Complexity Path Metric for Tree-Based Multiple-Antenna...

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IEEE international conference on Communications (ICC)

作者： C. Kuhn N. Goertz Institute for Communications Engineering Munich University of Technology Munich Germany Institute for Digital Communications Joint Research Institute for Signal & Image Processing University of Edinburgh Edinburgh UK

Traditional multiple-antenna detectors - which search a tree or a lattice structure - typically apply a metric that requires a preprocessing. Contrary to that, we present a straightforward solution for the metric that avoids those additional computations prior to the search. This new metric approach can even be used for the detection in under-determined systems with fewer receive than transmit antennas. Exemplarily, results are presented for turbo detection with the list-sequential (LISS) detector.

关键词： Maximum likelihood decoding Detectors Viterbi algorithm Transmitting antennas Convolutional codes Matrix decomposition Communications Society Digital communication signal processing image processing

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A rule-based approach to stroke lesion analysis from CT brain images

A rule-based approach to stroke lesion analysis from CT brai...

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international Symposium on image and signal Processing and Analysis

作者： M. Matesin S. Loncaric D. Petravic Department of Electronic Systems and Information Processing Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Neurology University Hospital Rebro Zagreb Croatia

ISBN: (纸本)9539676940

This paper presents a method for automatic segmentation and labeling of computerised tomography (CT) head images of stroke lesions. The method is composed of three steps. The first step is automatic determination of head symmetry axis, with the possibility of manual improvement of the result if necessary. Symmetry axis calculation is based on moments. In the second step, the seeded region-growing (SRG) algorithm is used to segment the input image into a number of regions having uniform brightness. Features of these regions, such as brightness, area, neighborhood and relative position to the symmetry axis are used to create facts for a rule-based expert system. based on created facts and pre-defined rules as input, the rule-based expert system is used in the third step to label regions as background, skull, gray/white matter, CSF and stroke. Experimental results have been conducted and have demonstrated the feasibility and accuracy of the proposed method.

关键词： Lesions image analysis Computed tomography Brain image segmentation Labeling Head Expert systems Brightness Medical expert systems

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Research on internet of Things image Compression Method based on Convolutional Self Coding Fusion

Research on Internet of Things Image Compression Method Base...

引用

Industrial IoT, Big Data and Supply Chain (IIoTBDSC), international conference on

作者： Shuchang Chen Xinli Li College of Information Technology Changchun AutoMobile Industry Institute Changchun China College of Instrumentation & Electrical Engineering Jilin University Changchun China

ISBN: (数字)9798350341690

ISBN: (纸本)9798350341706

Objective In order to solve the problem that the cloud platform occupies too much data storage space of the internet of Things in the fields of intelligent manufacturing, intelligent medical treatment and intelligent automobile, and the amount of image data is too large, this paper proposes an internet of Things image compression method based on convolutional self-coding fusion, and researches the compression method of the collected internet of Things images. Method Firstly, the collected internet of things images were grayed to obtain grayscale images. Secondly, the improved convolutional autoencoder (GCNN) model was used to encode, binarize and decode the original color image. Finally, the image decoded by the autoencoder and the gray image were fused and enhanced by the DHSV fusion reconstruction method. Result The proposed method uses the BSDS500 dataset, in which the training set consists of 200 images, the test set consists of 200 images, and the validation set consists of 100 images. The network structure was built according to the way in Section 2.2, the learning rate was set to 0.01 and 0.1 respectively, and the training was performed 100 times with 10 iterations per epoch. Finally, Set8(8 images) is used for testing. The color image is used to evaluate the reconstruction results in visual effect, and the Peak signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) are used to evaluate the reconstruction results. The experimental results show that the compression ratio can reach 2.28, and the compressed space is reduced by nearly 1/2. At the same time, the fusion reconstruction image can reduce the distortion, and maintain a good level of clarity, contrast and information richness. Conclusion The network model and fusion reconstruction method proposed in this paper can quickly extract image features while reducing network parameters, and can fuse the high-resolution information of grayscale images to achieve good reconstruction results,

关键词： Convolutional codes Cloud computing image coding Convolution Color Gray-scale Reconstruction algorithms

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A Multi-Model Fusion Framework to Enhance sEMG-based Gesture Recognition Assisted by Visual Features

A Multi-Model Fusion Framework to Enhance sEMG-Based Gesture...

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IEEE international conference on Robotics and Biomimetics

作者： Longxiao Gong Yue Cheng Hao Zhang Yang Wang Ping Zhao School of Mechanical Engineering Hefei University of Technology Hefei Anhui China Department of Automation University of Science and Technology of China Hefei Anhui China Department of Mechanical Engineering Hefei University of Technology Hefei Anhui China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Gesture recognition plays an increasingly important role in fields such as human-computer inter-action and rehabilitation robots and therapy. However, most current gesture recognition systems are based on a single type of sensor, which limits the feature sources for gesture recognition. Therefore, this paper proposes a time-frequency and visual feature fusion multimodal recognition framework (TFVF -CNN). It combines the deep features extracted from a small amount of image data with the time-frequency fea-tures of surface electromyogram (sEMG) signals to improve the accuracy of gesture recognition based on sEMG. Additionally, this paper introduces a multi-modal dataset (MEVD) containing raw sEMG signal data, sEMG signal time-frequency data, and image data, providing a reliable data source for experiments. Finally, the experimental results show that gesture recognition accuracy with multimodal fusion features and time-frequency features based on sEMG signals is 93.88 % and 95.9 %, respectively. Furthermore, we verified the performance of gesture recognition under partial image occlusion and found that TFVF -CNN still exhibits superior performance.

关键词： Time-frequency analysis Visualization Accuracy Soft sensors Medical treatment Gesture recognition Feature extraction Robot sensing systems Reliability Data mining

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A Hybrid Blockchain-based Authentication Scheme for Smart Home

A Hybrid Blockchain-Based Authentication Scheme for Smart Ho...

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IEEE international conference on signal and image Processing (ICSIP)

作者： Xia Yang Bing Li Yan Zhang Jiaxin Wu Pengwei Yuan School of Cyber Science and Engineering Southeast University School of Microelectronics Southeast University Shenzhen Research Institute Southeast University China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

With the development of the internet of Things technology, the use of smart devices is ubiquitous, thereby making the smart home become an innovative IoT application to change our lives. However, the application of smart home should serve thousands of families and the number of edge servers and terminal devices s huge. Thus, it is difficult for a single central server or cluster to manage such large-scale devices with high efficiency. In addition, the structure of IoT is flexible and the edge or terminal devices can join a certain IoT system without strict restriction. The system may suffer from unauthorized access from malicious devices. In this paper, a physically secure authentication scheme for smart home based on a hybrid blockchain is proposed. A novel structure of the blockchain is leveraged to relieve the stress of the central server and storage device's information permanently. Physical unclonable functions are used to provide identities for authorized devices. The security and evaluation analysis shows that our scheme has reliable security features and is the potential to improve the efficiency of the smart home system.

关键词： Authentication Smart homes Blockchain internet of Things Servers Security Smart devices

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