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Character Recognition Based on Multi-scale Feature Fusion Transfer Learning 6th

Character Recognition Based on Multi-scale Feature Fusion Tr...

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6th conference on signal and Information processing, Networking and Computers, ICSINC 2019

作者： Zhang, Miaohui Pang, Kangning Chen, Yunzhong Zhang, Bo Key Laboratory of Data Analysis and Processing of Henan Province Henan University Kaifeng China

ISBN: (纸本)9789811541629

Video surveillance has been using in daily life widely and has significant impact in security field. Reading time information in video has becoming an essential part. Because of the complex background of timestamp and the high similarity between targets, the features extracted by traditional machine learning algorithm cannot meet the requirements and cannot achieve the required detection speed and accuracy. In this paper, we improved the algorithm with multi-scale features. Features extracted from convolution layers of different scales are used to predict, which could improve the recognition accuracy of small targets. In addition, because the size of the collected dataset is 2860 and the amount of data is not large enough, we use data augmentation and transfer learning to train. Pre-training on the public dataset SVHN firstly, and then trains on the special dataset. Experiments show that the algorithm can recognize timestamps accurately. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Large dataset

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Image Electronic Evidence Screening Based on Improved SSD

Image Electronic Evidence Screening Based on Improved SSD

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Yafei Liu Liehui Jiang Tieming Liu Youwei Zhang Zhongyuan Network Security Research institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

ISBN: (纸本)9781665447300

With the development of information technology, electronic evidence plays an increasingly important role in the judicial trial. In Judicial Forensics, it is difficult to extract effective electronic evidence accurately and quickly from a large number of electronic data. Traditional means generally take artificial identification to collect, which takes a long time and is not efficient. Using SSD target detection and recognition algorithm instead of traditional means can effectively reduce the time of screening evidence, but the basic SSD neural network is prone to miss detection for small targets. To solve the above problems, an improved SSD-based image electronic evidence screening method is proposed. this method optimizes the SSD neural network adaptively, introduces the attention mechanism module in the shallow convolution layer of the network to improve the representation ability of the feature map, and fuses the image features obtained from different convolution layers with multi-scale features to increase the shallow feature information. the experimental data are used to test the improved algorithm and analyze the experimental results. It is found that compared with the original SSD neural network algorithm, the detection mean average precision of the improved algorithm is increased by 4.7%, reaching 84.3%, which shows the feasibility and effectiveness of the improved algorithm.

关键词： Image recognition Convolution Target recognition Fuses Neural networks Stacking signal processing algorithms

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Recognition of Pest Based on Faster RCNN 6th

Recognition of Pest Based on Faster RCNN

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6th conference on signal and Information processing, Networking and Computers, ICSINC 2019

作者： Zhang, Miaohui Chen, Yunzhong Zhang, Bo Pang, Kangning Lv, Bingxue Key Laboratory of Data Analysis and Processing of Henan Province Henan University Kaifeng China Postdoctoral Research Station of Geography Henan University Kaifeng China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China

ISBN: (纸本)9789811541629

Effective prediction of crop pest species and quantities can provide a priori knowledge for pest control. However, there are many kinds of pests and the color and texture of different pests are similar, traditional image processing and recognition methods based on time or frequency domain features and classifiers are hard to meet the needs of high-precision recognition. In order to improve the recognition result of agricultural pests, based on the target detection algorithm Faster RCNN, we put forward a method for the classification and identification of pests on the basis of online hard sample mining and residual network. the main work is as follows: (1) According to the different size of pests, the detection ability of the network for small targets is enhanced by changing the size and number of anchors;(2) In the feature extraction stage, we use ResNet-50 with stronger feature extraction ability rather than the original VGG16 network;(3) In order to solve the imbalance of samples, online hard Sample Mining Strategy is introduced in the training stage. the experimental outcome demonstrate that our method is effective, the improved algorithm achieves about 5% improvement in accuracy, and the network has a stronger ability to detect small targets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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A double-threshold target detection method in detecting low slow small target 8

A double-threshold target detection method in detecting low ...

引用

8th International conference on Identification, Information and Knowledge in the Internet of things, IIKI 2019

作者： Yu, Jie Liu, Yulan Bai, Yang Liu, Feiliang Science and Technology on Electromagnetic Scattering Laboratory Beijing1100854 China Chaohu University Hefei230000 China

With the rapid development of Unmanned Aerial Vehicle (UAV), stealth warship and stealth tank, the mission of radar has become more difficult. the modern radar system must detect and track target through strong clutter from many aspect, such as ground and sea. Radar detection for low slow small （LSS）targets has gradually become a new focus. LSS target has a small Radar Cross Section (RCS) because of its small size, making the SNR of the echo signal very low. At the same time, the Doppler frequency of slow target is very low, making it difficult for conventional signal processing methods to separate the Doppler information from the clutter spectrum. thus, it does make sense to study how to detect the LSS target in strong clutter efficiently. In this paper, the distribution characteristics of radar clutter and coherent accumulation algorithm for LSS target detection in complex electromagnetic environment are studied. On the base of that, a double-threshold target detection method based on coherent accumulation is proposed. To check out the efficiency of the detection method, an experiment has been conducted, and the data collected was used to test the detection algorithm, and the result has been shown in this paper. © 2020 the Authors. Published by Elsevier B.V.

关键词： Antennas

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2019 14th IEEE International conference on Electronic Measurement and Instruments, ICEMI 2019

2019 14th IEEE International Conference on Electronic Measur...

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14th IEEE International conference on Electronic Measurement and Instruments, ICEMI 2019

ISBN: (纸本)9781728105093

the proceedings contain 280 papers. the topics discussed include: curve fitting and piecewise linear method for Z-type temperature sensor;(250-600) kv re-filtering narrow-spectrum x-ray half-value layer measurement method;congestion control strategy for power scale-free communication network by link addition;design of data acquisition module for 40GSPS digital oscilloscope;a dynamic Bayesian network approach for electro-optical system performance monitoring digital twin;objective assessment of hearing aid performance based on Gaussian process;novel method to evaluate the effectiveness of UAV navigation spoofing;study on methods of suppressing signal truncation error in digital signal processing;star sensor on-orbit calibration based on multiple calibration targets;and data transmission subsystem interface converter for satellite AIT.

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Control of a Bionic Hand based on Neural Networks and improved Gesture Recognition Techniques using multiple EMG sensors 14

Control of a Bionic Hand based on Neural Networks and improv...

引用

14th Technologies Applied to Electronics Teaching conference (TAEE)

作者： Artal-Sevil, J. S. Perez-Cebolla, F. J. Pascual-Acon, A. Dominguez, J. A. Univ Zaragoza Dept Elect Engn Higher Tech Engn Sch Zaragoza Spain

ISBN: (纸本)9781728167329

this paper presents the development and implementation of a control algorithm, based on neural networks, intended for the control and operation of a small robotic prosthesis. the gesture recognition system is based on multiple surface EMG sensors. In this way, it has been possible to control the developed prototype in a simple and effective way. these EMG sensors serve as a bridge to detect small electrical pulses caused by muscle activity and transform them into an analogue signal that the control module is capable of filtering and interpreting. Final Year Project (TFG) or Final Master Project (TFM), constitutes an academic activity that allows us to evaluate in detail the different competencies acquired by the students during their time at university. In general, the development of robotic systems always supposes an extra motivation in the students. thus, this document presents a novel and current electronic application such as the activation and control strategy of a bionic prosthesis. the purpose has been to simulate the actual movement of the fingers. At the same time, the different concepts and details of their implementation are discussed. the application of neural networks as a control strategy for the bionic hand has allowed the development of real-time control, obtaining very satisfactory results.

关键词： Neural Networks Bionic Hand Robotic Arm Sensor based Medical Application ECG/EMG signals Arduino Surface EMG Sensor Open Source Projects data Acquisition System Biomechanical model Low Cost

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Phase selector for RFID localization system based on RSSI filter 14

Phase selector for RFID localization system based on RSSI fi...

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14th IEEE International conference on Design and Technology of Integrated Systems In Nanoscale Era (DTIS)

作者： Hadj-Mihoub-Sidi-Moussa, H. Tedjini, small Touhami, Rachida USTHB Univ Instrumentat Lab Bab Ezzouar Algeria ESM DCM Harrach Algeria Grenoble Alpes Univ LCIS Lab Valence France USTHB Univ Instrumentat Lab Algiers Algeria

ISBN: (纸本)9781728134246

the Radio Frequency Identification (RFID) localizations systems are actually from the most used in indoor environment. But this kind of closed environment allays presents more perturbation due to walls, roofs, obstacle presence and generally affect the measurements especially sensible parameters like the signal phase which can provide precious additional information for a better accuracy . In this work, we propose an alternative to resolve this problem;we propose a phase cleansing algorithms based on Received signal Strength Indicator (RSSI) selection. Our proposition consists to select just the phases corresponding to the best reads based on RSSI. then, we use the selected phases as a metric for localization without using RSSI. We test our proposed approach on an RFID localization system composed from a set of EPC global Class-1 Gen-2 UHF RFID tags, an UHF RFID reader using a 7dbi antenna and a computer for data acquisition and processing. the system is carried by an operator and measurement zone was placed on a wall of 5.5 mx1.25 m.

关键词： data cleansing phase selection RFID localization RSSI filtering

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Logistic regression prediction based on fractal characteristics of sea surface targets 14

Logistic regression prediction based on fractal characterist...

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14th IEEE International conference on Electronic Measurement and Instruments (ICEMI)

作者： Shao Fuchi Xing Hongyan Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Key Lab Aerosol Cloud Precipitat China Meteorol A Nanjing 210044 Peoples R China

ISBN: (数字)9781728105109

ISBN: (纸本)9781728105109

Sea clutter is a kind of signal with low signal-to-noise ratio. Fractional Fourier transform is used to gather energy. Fractional Brownian motion model is introduced to model sea clutter. Based on the measured data of IPIX radar, high-order multi-fractal parameters are calculated. Fitting the multi-fractal parameters, choosing the steepest interval and calculating its slope. At the same time, choosing the fractal parameters with scale -30 under HH and VV polarization. the small targets are 0 and 1, respectively. the slope and high-scale fractal parameters of different sea conditions are normalized, and the normalized data are predicted by logistic regression. the simulation results show that FRFT can aggregate the energy of sea clutter signal. Logistic regression model can predict the fractal parameter data of sea clutter FRFT domain, and the prediction accuracy reaches 83.42%.

关键词： Sea Clutter Fractional Fourier Transform Multi-fractal Detrended Fluctuation Anaysis Logistic Regression

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the Resolution Enhancement in the Distributed Temperature Sensor with the Extremal Filtration Method 14

The Resolution Enhancement in the Distributed Temperature Se...

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14th IEEE International Scientific and Technical conference on Dynamics of Systems, Mechanisms and Machines (Dynamics)

作者： Ershov, Ivan A. Stukach, Oleg, V Myasnikova, Nina, V Tsydenzhapov, Igor B. Sychev, Igor, V Novosibirsk State Tech Univ Novosibirsk Russia Natl Res Univ Higher Sch Econ Moscow Russia Penza State Univ Penza Russia KEEPLINE R&D Novosibirsk Russia

ISBN: (纸本)9781728180960

High resolution DTS research has sufficiently advanced. However further progress will be achieved due to mathematical techniques and algorithms. Novel mathematical algorithms can expand measurement traceability. Outcomes from discrete wavelet transformations are not sufficient for many practical applications. We propose the extremal filtration method as an analog of the empirical mode decomposition (EMD) approach. Advantage of the extremal filtration is the simplification of mathematical calculations. the essence of the method is finding the moving average value of extremums with the continued removing of the highest frequency components from the signal, smoothing of the curve, and subsequent transformations. Difference between the modeling reference signal and signal with Gaussian noise as error of the method is very small: most of the samples (90 %) are within an interval of +/- 0.003. It is an excellent result for the low signal-to-noise ratio (SNR). However, significant short-term splashes of error (which can reach the value of 0.038) occurred during the transition process. the method can filter low-SNR DTS signals. We expect that the extremal filtration of one target signal is much more effective than a simple averaging of many target OTDR pulses often used in practice. Also, this method can be customized for specific problems connected with subtracting the high-frequency components from the signal. this expands the field of use for the method.

关键词： fiber optical temperature sensor DTS spatial resolution measurement accuracy EMD data processing Raman scattering observation of unsafe production

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Identifying Disease Modules Based on Connectivity and Semantic Similarities 14th

Identifying Disease Modules Based on Connectivity and Semant...

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14th International conference on Bio-inspired Computing: theories and Applications, BIC-TA 2019

作者： Su, Yansen Zhu, Huole Zhang, Lei Zhang, Xingyi Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9789811534140

the identification of disease modules has attracted increasing attention due to the importance in comprehending pathogenesis of complex diseases. Most of the existing methods were based on the protein-protein interaction (PPI) networks with the incompleteness and incorrectness of the interactome, which results in many disease-related proteins and pathways not within their disease modules. In this paper, we propose a method named IDMCSS to effectively identify disease modules by adding some potential interactions and removing some incorrect interactions in the existing human PPI network. Firstly, the connective similarity and semantic similarity are calculated between the known disease proteins and each of their neighbors. then, the network is adjusted by adding the interactions with large connective and semantic similarity, and removing those with small connective and semantic similarity. Further, the proteins are sorted in the descending order according to the similarity to the known disease proteins, and the sorted proteins are added into a candidate node set one by one until a certain biological information is not enriched in the candidate node set. Finally, the connected subnetwork with the largest number of nodes in the candidate node set is selected as the disease module. the disease modules identified by IDMCSS involve crucial biological processes of related diseases and can predict 12 targets for drug intervention. the experimental results on asthma demonstrate the effectiveness of the proposed method in comparison to existing algorithms for disease module identification. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Proteins

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