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EgpuIP: An Embedded GPU Accelerated Library for Image processing

EgpuIP: An Embedded GPU Accelerated Library for Image Proces...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Luhan Wang Haipeng Jia Yunquan Zhang Kun Li Cunyang Wei State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Microsoft Research

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. the image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm; 2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm; 3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm; 4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to $\mathbfth\times,\ \mathbf{88}\times$ , and $\mathbfprocessing\times$ speedup respectively.

关键词： Image processing Instruction sets Graphics processing units Optimization methods Programming parallel processing Libraries

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GMLB Filter With Uncorrelated Conversion for Multi Nonlinear Targets Tracking

GMLB Filter With Uncorrelated Conversion for Multi Nonlinear...

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international conference on Systems and Informatics (ICSAI)

作者： Xinghui Wu Min Wang National Laboratory of Radar Signal Processing Xidian University Xian China

ISBN: (纸本)9781665474498

In the process of multi-target tracking, error observation and noise should be excluded and associated with the actual target observation value. Especially in the case of nonlinear motion, the difficulty of correlation rises sharply. To solve the decreasing correlation accuracy in nonlinear motion, a Generalized Labeled Multi-Bernoulli (GLMB) filter based on an Uncorrelated Conversion (UC) named UC-GLMB filter was proposed in this paper. Firstly, this method can effectively obtain more measurement information and is applied to the linear estimator. Secondly, it is an effective solution for multiple nonlinear moving target tracking problems based on random finite sets (RFS). thus, the performance of the UC-GLMB filter may be continually improved. Simulation results demonstrate the effectiveness of the proposed estimator compared with some popular multi-target tracking algorithms.

关键词： Radio frequency Target tracking Correlation Simulation Filtering algorithms Information filters Bayes methods

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Enhancing a Soft GPU IP Reliability Against SEUs in Space: Modelling Approach and Criticality Analysis on a Radiation-Tolerant FPGA

Enhancing a Soft GPU IP Reliability Against SEUs in Space: M...

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international conference on System Reliability and Science (ICSRS)

作者： Matteo Monopoli Matteo Biondi Giovanni Todaro Pietro Nannipieri Silvia Moranti Cinzia Bernardeschi Luca Fanucci Department of Information Engineering University of Pisa Pisa Italy European Space Research and Technology Centre European Space Agency (ESA) Noordwijk The Netherlands IngeniArs S. r.l. Pisa Italy

ISBN: (数字)9798350354508

ISBN: (纸本)9798350354515

Field Programmable Gate Arrays find extensive use across various domains, ranging from telecommunications to Machine Learning tasks. In the context of space applications, they emerge as a highly favourable choice, owing to their high degree of flexibility and their accessibility in different silicon-grade technologies. However, due to the high associated costs of radiation-hardened devices, the prevalence of radiation-tolerant devices is more common in practical deployment scenarios. In this sense, robustness-enhancing techniques can still be applied, thus allowing the employment of these devices in long-duration tasks without the need for a radiation-hardened counterpart. In this paper, we propose a modelling approach for estimating the reliability of a digital design system in space before carrying out fault injection tests and radiation campaigns. For this purpose, we employ a high-level tool called Mobius to obtain a first reliability estimation against Single Event Upsets by means of Fault Tree Models. We also propose a novel methodology for defining which fault mitigation techniques should be applied to each design sub-module, considering the most commonly used in the space environment. In particular, we focus on a specific use case, namely a Soft Graphic processing Unit IP, implemented on the Xilinx RT XQRKU060 FPGA. We analyze the criticality, power and area impact of the most important design parts of the GPU when implemented in hardware, introducing a classification approach for associating an appropriate fault mitigation technique with each of them. Finally, in the last section of the paper, we hint at future development and conclude the work.

关键词： Space vehicles Prevention and mitigation Graphics processing units Single event upsets Logic gates Reliability engineering Hardware IP networks Field programmable gate arrays Fault trees

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Automated Medicinal Plant Identification through Image processing and Machine Learning

Automated Medicinal Plant Identification through Image Proce...

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international conference on Computing Communication Control and Automation (ICCUBEA)

作者： Yash Kadam Ajay Yache Pavankumar Solunke Ramakant Tati Renu Kachhoria Amar Buchade Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

this research addresses the pressing challenge of accurately identifying medicinal plants in India's Ayurvedic Pharmaceutics, aiming to mitigate issues stemming from morphological variability and market adulteration. Traditional methods of plant identification are hindered by laborious processes and the potential for error, underscoring the need for an automated solution. Leveraging the power of advanced image processing and machine learning, our proposed software framework integrates the Xception model and early stopping fine-tuning to automate plant identification based on plant images. the methodology involves curating a comprehensive dataset of labelled plant images representing diverse medicinal plants, subsequently employing image preprocessing techniques to extract salient features like color, texture, and shape. the Xception model, renowned for its prowess in image recognition tasks, serves as the backbone architecture for both feature extraction and classification. To further optimize model performance and mitigate overfitting, early stopping fine-tuning techniques are applied. through rigorous experimentation and validation, our research demonstrates the efficacy of this approach in accurately identifying medicinal plants, thereby fostering confidence and trust within the herbal medicine supply chain. By seamlessly integrating modern technological advancements with traditional botanical knowledge, our framework not only streamlines the identification process but also contributes to the sustainable utilization of herbal medicine resources. Ultimately, this research represents a pivotal step towards establishing a robust and reliable system for identifying medicinal plants, facilitating informed decision-making, and promoting the long-term viability of herbal medicine practices in India and beyond.

关键词： Medicinal plants Automation Shape Scalability Supply chains Feature extraction Software Robustness Stakeholders Biomedical imaging

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Particle Weight Redistribution in Particle Filtering to Improve Nonlinear State Estimation

Particle Weight Redistribution in Particle Filtering to Impr...

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international conference on Information Technology (INCIT)

作者： May Htet Htet Khine Nattapol Aunsri School of Applied Digital Technology Mae Fah Luang University Chiang Rai Thailand Computer and Communication Engineering for Capacity Building Research Center (CCC) Chiang Rai Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

Particle filters are used to estimate hidden state parameters from the noisy signals. they are based on sequential Monte Carlo theory, where they randomly draw samples from all possible hidden states and then use the previous information of estimation to proceed to the next step. Particle filters are also known for their degeneracy issues where the particles are separated into high probability and low probability regions. therefore, resampling is incorporated into particle filters. However, resampling brings up another issue of particle impoverishment and consequently reduce particle diversity. this paper proposes an improved resampling process for a standard particle filters to address particle impoverishment in standard sampling importance resampling particle filters (SIR-PF). B y introducing a weight redistribution of particles before resampling, the algorithm expects to mitigate error values across varying signal-to-noise ratios, potentially outperforming conventional SIR-PF. the experiment is validated using a simulated signal, resulting in overall lower RMSE and MAE at various noise levels. the proposed method is a computationally efficient and straight forward process compared to more complex alternatives.

关键词： Monte Carlo methods Accuracy Systematics Signal processing algorithms Particle filters Computational efficiency State estimation Standards Optimization Signal to noise ratio

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parallel-Friendly and Work-Efficient Single Source Shortest Path Algorithm on Single-Node System

Parallel-Friendly and Work-Efficient Single Source Shortest ...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Jie Zhang Yuan Zhang Yiming Sun Huawei Cao Xuejun An Xiaochun Ye State Key Lab of Processors Institute of Computing Technology CAS Beijing China School of Computer and Control Engineering UCAS Beijing China Nanjing Institute of Information Superbahn CAS Nanjing China

the Single-Source Shortest Path (SSSP) algorithm is one of the most important kernels used by a variety of high-level graph processing applications. Although having been extensively studied for single-node scenarios, SSSP algorithm still faces difficulties when being deployed on scale-free graph, which brings about redundant edge traversals, expensive atomic operations and random memory access. In this paper, three parallel-friendly and work-efficient opti-mizations are introduced to improve the performance of parallel SSSP algorithm with shared memory on the single-node system. First, a label-aware pruning technique is proposed to reduce the redundant edge traversals. then, atomic operations caused by race conditions are eliminated by using destination-driven sub graph partitioning. Finally, we propose a double-ordered bucket strategy to further improve the efficiency of SSSP. the experiment demonstrates that the average speedup reaches to 27.58x compared with original implement for R-MAT graphs. Meanwhile, our method achieves up to 2.12x for R-MAT graphs and 4.45 x for real-world graphs on average compared to previous state-of-the-art parallel implementation.

关键词： Partitioning algorithms Kernel Optimization Faces

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Fuzzy Alternate Control Delayed Systems

Fuzzy Alternate Control Delayed Systems

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international conference on Computer and Communications (ICCC)

作者： Ning Tang Yuming Feng B. O. Onasanya Wei Zhang School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China Department of Mathematics University of Ibadan Ibadan Oyo Nigeria Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing China

this paper studies the asymptotic stability of nonlin-ear systems which is regulated by using fuzzy impulsive control. First of all, the system was formulated, the Lyapunov function was constructed, and then the fuzzy control component required for the nonlinear system to exhibit global asymptotic exponential stability under the regulation of the impulsive controller was developed. A unique fuzzy concept was introduced herein, which, compared to the conventional precise counterpart, was defined to perform only scalar approximations. Subsequently, a set of conditions sufficient for global asymptotic exponential stability was proposed for the impulsive controller. Halany inequality and its key generalizations were used while working out the mathematical proofs. Finally, examples were given to verify the general results obtained and their applicability.

关键词： Fuzzy control Protocols Stability Control systems Regulation Delays Control theory

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Physics-Inspired Heuristics for Soft MIMO Detection in 5G New Radio and Beyond 21

Physics-Inspired Heuristics for Soft MIMO Detection in 5G Ne...

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27th ACM Annual international conference on Mobile Computing and Networking (MobiCom)

作者： Kim, Minsung Mandra, Salvatore Venturelli, Davide Jamieson, Kyle Princeton Univ Princeton NJ 08544 USA NASA QuAIL Ames Res Ctr Washington DC 20546 USA USRA Res Inst Adv Comp Sci Mountain View CA 94043 USA KBR Inc Houston TX USA

ISBN: (纸本)9781450383424

Overcoming the conventional trade-off between throughput and bit error rate (BER) performance, versus computational complexity is a long-term challenge for uplink Multiple-Input Multiple-Output (MIMO) detection in base station design for the cellular 5G New Radio roadmap, as well as in next generation wireless local area networks. In this work, we present ParaMax, a MIMO detector architecture that for the first time brings to bear physics-inspired parallel tempering algorithmic techniques [28, 50, 67] on this class of problems. ParaMax can achieve near optimal maximum-likelihood (ML) throughput performance in the Large MIMO regime, Massive MIMO systems where the base station has additional RF chains, to approach the number of base station antennas, in order to support even more parallel spatial streams. ParaMax is able to achieve a near ML-BER performance up to 160 x 160 and 80 x 80 Large MIMO for low-order modulations such as BPSK and QPSK, respectively, only requiring less than tens of processing elements. With respect to Massive MIMO systems, in 12 x 24 MIMO with 16-QAM at SNR 16 dB, ParaMax achieves 330 Mbits/s near-optimal system throughput with 4-8 processing elements per subcarrier, which is approximately 1.4x throughput than linear detector-based Massive MIMO systems.

关键词： parallel Tempering Massive MIMO MU-MIMO Detection 5G

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Proceedings - 16th IEEE international Symposium on parallel and Distributed processing with Applications, 17th IEEE international conference on Ubiquitous Computing and Communications, 8th IEEE international conference on Big Data and Cloud Computing, 11th IEEE international conference on Social Computing and Networking and 8th IEEE international conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018

Proceedings - 16th IEEE International Symposium on Parallel ...

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16th IEEE international Symposium on parallel and Distributed processing with Applications, 17th IEEE international conference on Ubiquitous Computing and Communications, 8th IEEE international conference on Big Data and Cloud Computing, 11th IEEE international conference on Social Computing and Networking and 8th IEEE international conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018

ISBN: (纸本)9781728111414

the proceedings contain 161 papers. the topics discussed include: deep learning for phishing detection;a partition matching method for optimal attack path analysis;an energy and robustness adjustable optimization method of file distribution services;deriving the political affinity of twitter users from their followers;on the usability of big (social) data;re-running large-scale parallel programs using two nodes;predicting hacker adoption on darkweb forums using sequential rule mining;an on-the-fly scheduling strategy for distributed stream processing platform;deadlock-free adaptive routing based on the repetitive turn model for 3D network-on-chip;and radix: enabling high-throughput georeferencing for phenotype monitoring over voluminous observational data.

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Radar Waveform Recognition Based on Time-frequency Images Under Deteriorating Channel Conditions

Radar Waveform Recognition Based on Time-frequency Images Un...

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international conference on Computer and Communications (ICCC)

作者： Lai Shen Daying Quan Xiaofeng Wang Xiaoping Jin Ning Jin dept. of college of information Engineering China Jiliang University Hangzhou China

In the process of electronic countermeasures, most studies of radar signal recognition focus on Additive White Gaussian Noise (AGWN) channel scenarios, while pay less attention to the influence of channel deterioration on radar signal recognition. therefore, we propose a low interception rate radar signal recognition method based on time-frequency images under channel impairment. First, we obtain shallow features of LPI radar signals in fading channels by Choi-Williams distribution (CWD) time-frequency transform that converts the signals to time-frequency images. Second, we design a parallel residual network model for feature learning and signal classification to realize the automatic modulation recognition of radar waveforms under channel fading conditions. Simulation results show that the proposed method effectively achieves the recognition of 12 kinds of radar waveforms both in AGWN channels and in multipath channels, which demonstrates the superiority of this model.

关键词： Time-frequency analysis Image recognition Computational modeling Modulation Radar Radar imaging Radar signal processing

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