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作者： Zhou, Rong Wang, Kang Li, Min School of Information Engineering Wuhan University of Technology Wuhan430070 China Key Laboratory of Fiber Optic Sensing Technology and Information Processing Wuhan University of Technology Ministry of Education Wuhan430070 China

ISBN: (纸本)9781538636749

The sEMG signal can directly reflect a variety of information about the activity of specific muscle and nerve. By extracting and studying the characteristics of sEMG signal, the body movements, diagnosis of muscle disease and medical rehabilitation can be effectively identified. Based on these features, a sEMG signal acquisition instrument was designed for body rehabilitation training. The low-pass filter circuit and signal acquisition circuit were designed by ADS1294 physiological signal processing chip, and the acquisition of sEMG signal from 1Hz ~ 500Hz was completed. In order to read and send signals, the software based STM32 master control circuit was designed. The test results show that the SEMG signal acquisition device can collect real-time data of 4-channel EMG signal, and accurately reflect the release of sEMG signal. Therefore, it has great real-time and reliability. © 2017 ieee.

关键词： ADS1294 Rehabilitation sEMG signal acquisition STM32

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A novel image defogging algorithm based on improved bilateral filtering

A novel image defogging algorithm based on improved bilatera...

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作者： Li, Aimin Li, Xiaocong School of Information Qilu University of Technology Jinan China

ISBN: (纸本)9781538636749

As we all know, haze and fog lead to decreasing of differentiation degree and definition in outdoor environment and reduce image processing performance. To recover image with fog, we propose a novel fast defogging approach from a gray or color image. First, we use dark channel statistics prior to illustrate the similar relationship between the dark channel and image with fog. Then, to conquer the shortcomings of the estimation methods of fog concentration, we put forward a new filter algorithm. Smoothing the concentration of mist with bilateral filtering can immensely keep the edge of a sudden mutation in the depth of the image. Using partial median filters to smooth the corners can avoid keeping a certain amount of fog around small objects and keep the edge of the corner of the building, which makes the result more robust. © 2017 ieee.

关键词： Bilateral filter Dark channel prior Defogging

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The Communication Availability of the Cooperative Combat of Manned / Unmanned Aerial Vehicles in Electromagnetic Cyber Confrontation Environment

The Communication Availability of the Cooperative Combat of ...

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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： Lijun Gao Jian Ding Ruina Lian School of Computer Science Shenyang Aerospace University Shenyang China School of Physics and Electronics Henan University Kaifeng China

ISBN: (纸本)9781728152103

The cooperative combat of manned/unmanned aerial vehicle combine the advantages of both sides to enhance the combat capability to the greatest extent. The research results show that the cooperative combat of manned/unmanned aerial vehicle needs to solve the availability of the data link between fighters. In the strong interference environment, frequency hopping communication technology is used to prevent enemy electromagnetic interference. However, the traditional frequency hopping technology requires the communication parties to pre-negotiate the frequency hopping spectrum, and the fighters use the pre-negotiated frequency hopping pattern for communication. However, the research shows that it is difficult to exchange frequency hopping parameters between the two sides of the communication, and the availability of the data link is difficult to guarantee in a strong electromagnetic interference environment. Since the communication sequence of the uncoordinated frequency hopping communication can be predicted, a variable speed uncoordinated frequency hopping technique is proposed in this paper to increase the difficulty of reconnaissance and tracking of frequency hopping parameters. Next, we propose an artificial noise co-interference technology that can interfere with the channel communication quality of the enemy, but our signal reception is not affected. Furthermore, we use the zero-sum game model to analyze the electromagnetic confrontation process between the enemy and us. The experimental results show that the variable speed uncoordinated frequency hopping technique and the artificial noise co-interference technology have good anti-interference effects.

关键词： autonomous aerial vehicles cooperative communication electromagnetic interference frequency hop communication game theory frequency hop communication Electromagnetic interference cooperative communication Game theory Frequency hopping Data links vehicle Communication Research Dyscoordination Fighter signal reception

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CMOS Vision Sensors: Embedding Computer Vision at Imaging Front-Ends

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ieee CIRCUITS AND SYSTEMS MAGAZINE 2018年第2期18卷 90-107页

作者： Rodriguez-Vazquez, A. Fernandez-Berni, J. Lenero-Bardallo, J. A. Vornicu, I. Carmona-Galan, R. Univ Seville CSIC CNM IMSE Seville Spain

CMOS image Sensors (CIS) are key for imaging technologies. These chips are conceived for capturing optical scenes focused on their surface, and for delivering electrical images, commonly in digital format. CISs may incorporate intelligence;however, their smartness basically concerns calibration, error correction and other similar tasks. The term CVISs (CMOS VIsion Sensors) defines other class of sensor front-ends which are aimed at performing vision tasks right at the focal plane. They have been running under names such as computational image sensors, vision sensors and silicon retinas, among others. CVIS and CISs are similar regarding physical implementation. However, while inputs of both CIS and CVIS are images captured by photo-sensors placed at the focal-plane, CVISs primary outputs may not be images but either image features or even decisions based on the spatial-temporal analysis of the scenes. We may hence state that CVISs are more "intelligent" than CISs as they focus on information instead of on raw data. Actually, CVIS architectures capable of extracting and interpreting the information contained in images, and prompting reaction commands thereof, have been explored for years in academia, and industrial applications are recently ramping up. One of the challenges of CVISs architects is incorporating computer vision concepts into the design flow. The endeavor is ambitious because imaging and computer vision communities are rather disjoint groups talking different languages. The Cellular Nonlinear Network Universal Machine (CNNUM) paradigm, proposed by Profs. Chua and Roska, defined an adequate framework for such conciliation as it is particularly well suited for hardware-software co-design [1]-[4]. This paper overviews CVISs chips that were conceived and prototyped at IMS E Vision Lab over the past twenty years. Some of them fit the CNNUM paradigm while others are tangential to it. All of them employ per-pixel mixed-signal processing circuitry to achieve sen

关键词： Cameras Machine vision Task analysis image sensors Circuits and systems Feature extraction

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3D Point Cloud Enhancement using Unsupervised Anomaly Detection

3D Point Cloud Enhancement using Unsupervised Anomaly Detect...

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ieee International symposium on Systems Engineering (ISSE)

作者： Yousra Regaya Fodil Fadli Abbes Amira Department of Computer Science and Engineering Qatar University Doha Qatar Department of Architecture and Urban Planning Qatar University Doha Qatar Faculty of Computing De Montfort University Leicester UK

3D point cloud is increasingly getting attention for perceiving 3D environment which is needed in many emerging applications. This data structure is challenging due to its characteristics and the limitation of the acquisition step which adds a considerable amount of noise. Therefore, enhancing 3D point clouds is a very crucial and critical step. In this paper, we investigate two promising unsupervised techniques which are One-Class SVM (OCSVM) and Isolation Forest (IF). These two techniques optimize the separation between relevant/normal points and irrelevant/noisy points. For evaluation, three metrics are computed, which are the processing time, the number of detected noisy points, and Peak signal-to-Noise (PSNR) in order to compare the both proposed techniques with one of the recommended filters in the literature which is Moving Least Square (MLS) filter. The obtained results reveal promising capability in terms of effectiveness. However, OCSVM technique suffers from high computational time; therefore, its efficiency is enhanced using modern Graphics processing Unit (GPU) with an average rate improvement of 1.8.

关键词： graphics processing units image enhancement least squares approximations object detection support vector machines

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Autoencoder Based Dimensionality Reduction of Feature Vectors for Object Recognition

Autoencoder Based Dimensionality Reduction of Feature Vector...

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International ieee Conference on signal-image Technologies and Internet-Based System

作者： Reyhan Kevser Keser Behçet Ugur Töreyin Signal Processing for Computational Intelligence Group Informatics Institute Istanbul Technical University Istanbul Turkey Istanbul Technical University Istanbul Turkey

ISBN: (数字)9781728156866

ISBN: (纸本)9781728156873

Object recognition can be performed with high accuracy thanks to the robust feature descriptors defining the significant areas in images. However, these features suffer from high dimensional structure, in other words "curse of dimensionality" for further processes. Autoencoders (AE) are proposed in this study to solve the dimensionality reduction problem of visual features. To assess the efficacy, object recognition is performed using reduced dimensional visual features. For this purpose, dimensionalities of three well-known feature vectors, namely, HOG, SIFT and SURF, are reduced to half. Moreover, deep learning based features are also reduced. Then, reduced vectors, which are called as AE-HOG, AE-SIFT, AE-SURF and AE-DEEP are fed to object recognition task. Also, dimensionality reduction is implemented by a variant of AE, variational autoencoder (VAE) and PCA, which is the most studied unsupervised method for these features, and the results are compared. Furthermore, all experiments are repeated on noisy images. Results suggest that dimensionality reduction of these feature vectors can be accomplished successfully owing to the proposed method.

关键词： Object recognition Dimensionality reduction Feature extraction Principal component analysis Visualization Robustness

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A novel quadtree-salient image decomposition for color Doppler flow imaging

A novel quadtree-salient image decomposition for color Doppl...

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作者： Zhang, Yi Wan, Nan Wang, Jun Tao, Wan College of Computer and Information Science Anhui Polytechnic University Wuhu Anhui241000 China College of Medical Information Wannan Medical College Wuhu Anhui241000 China

ISBN: (纸本)9781538636749

Color Doppler flow imaging analysis is one of those difficult problems in medical image decomposition fields. To address the key problem, we propose a new bottom-up segmentation method - quadtree-salient decomposition which is suitable for machine vision applications in complex environments. In our proposed method, a LMS perception space converting processing will firstly be carried on and the three independent channel could be extracted as the cornerstone of decomposition. After this, an integrated visual salient model which includes Itti, GBVS and image signature models is proposed. At last, we give the detailed quadtree-salient decomposition algorithm which is fitted for human visual system. Experimental results on the real CDFI dataset demonstrate that the proposed method has advantages over LoG, Canny and the classical quadtree decomposition. © 2017 ieee.

关键词： Color space Feature extraction Quadtree decomposition Saliency

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Energy and computational Resources Optimization in a Mobile Edge Computing Node 9

Energy and Computational Resources Optimization in a Mobile ...

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9th International symposium on signal, image, Video and Communications, ISIVC 2018

作者： El Ghmary, Mohamed Malki, Mohammed Oucamah Cherkaoui Hmimz, Youssef Chanyour, Tarik Sidi Mohamed Ben Abdellah University LIIAN Labo FSDM Fez Morocco

ISBN: (纸本)9781538681732

Mobile edge computing most of time deals with constrained mobile devices. Especially for their limited processing capacity and available battery power, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstance, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, it must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and equipped with a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate and compare this solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. In addition, our solution performs the offloading decisions within an acceptable and feasible timeframes. © 2018 ieee.

关键词： computation offloading

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Comparative Study of image processing Performance of Camera-Based Visible Light Communication Using Android Acceleration Frameworks 11

Comparative Study of Image Processing Performance of Camera-...

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11th International symposium on Communication Systems, Networks and Digital signal processing, CSNDSP 2018

作者： Dzieciol, Hubert Minh, Hoa Le Ghassemlooy, Zabih Dat, Pham Tien Tran, Son The Optical Communications Research Group Northumbria University Newcastle-upon-TyneNE1 8ST United Kingdom Tokyo Japan Korea-Vietnam Friendship Information Technology College Da Nang Viet Nam

ISBN: (纸本)9781538613351

The asynchronous nature of smartphone-to-smartphone (S2S) based on visible light communication (VLC) imposes a significant challenge on the speed of camera-based receiver processing time and algorithm. Recent improvements on the smartphone camera hardware and the current release of the highly customised camera2 application programming interface (Camera2-API) have increased the smartphone's computational capability. This paper presents a comparative study of the acceleration frameworks, which can be used for image processing on Android device to maximize the code performance, thus reducing the computational time of data frame detection. An experimental S2S VLC system is developed for evaluation of the graphical processing unit acceleration (GPU), Android runtime (ART) and native development kit (NDK) based algorithms for processing the captured data. In addition, we determine the total number of processed pixels for multiple frames with the maximum possible detection frequency for S2S VLC. Using the additive property of RGB colour space, two sets of experiments are implemented: firstly the conversion from YUV to RGBA (Red Green Blue Alpha) using all of the available colour-based data, which leads to 500% of improvement in colour conversion time using NDK compared to ART. A gain of 200% is also achieved compared to GPU-based algorithms. Secondly, the grayscale filtered YUV to RGBA conversion shows that NDK processing time is 200% faster than the direct ART, which outperforms GPU conversion at lower frame sizes. From the results findings, we propose an optimal approach for camera-based VLC application development using Android smartphones. © 2018 ieee.

关键词： Cameras

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A multi-objective deep reinforcement learning approach for stock index futures’s intraday trading

A multi-objective deep reinforcement learning approach for s...

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作者： Si, Weiyu Li, Jinke Rao, Ruonan Ding, Peng School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China China Quantitative Investment Association Shanghai China

ISBN: (纸本)9781538636749

Modern artificial intelligence has been widely discussed to practice in automated financial asserts trading. Automated intraday trading means that the agent can react to the market conditions automatically, while simultaneously make the right decisions. Besides, the profits will be made within a day considering transaction cost charged by the brokerage company. In this paper, we introduce a multi-objective deep reinforcement learning approach for intraday financial signal representation and trading. We design the deep neural networks to automatically discover the dynamic market features, then a reinforcement learning method implemented by a special kind of recurrent neural network (LSTM) is applied to make continuous trading decisions. In terms of balancing the profit and risk, we implement a multi-objective structure which includes two objectives with different weights. We conduct experiments on stock index futures data, and our analysis and experiments not only offer insights into financial market features mining, but also provide a straightforward and reliable method to make profits, which sheds light on its wide application on automated financial trading. © 2017 ieee.

关键词： Deep learning Financial signal processing Intraday trading Reinforcement learning

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