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A Dual-Stream Network with Non-Stationary Characteristics-Enhanced for SST image Prediction

A Dual-Stream Network with Non-Stationary Characteristics-En...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Min Ye Jiahao Du Ning Song Xinyue Liang Bohan Li Jie Nie Fac. of Information Science and Engineering Ocean University of China Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Sea surface temperature (SST) prediction is crucial for understanding global climate and marine ecosystems, and its anomalies can lead to extreme weather events. SST exhibits complex non-stationary over natural spatio-temporal processes. However, most of the existing deep learning methods for SST prediction only extract non-stationary features through the simple state transitions of classic CNNs or RNNs, which are too simplistic to capture higher-order non-stationary trends in complex SST sequences. Therefore, we propose a DSNet_SST network, aiming to enhance the extraction of non-stationary information from the spatio-temporal SST evolution. It incorporates two parallel modules: one for capturing high-order temporal non-stationarity based on stacked memory in memory (MIM) blocks and the other one for extracting spatial correlation non-stationarity by multiscale difference operation. A third module adaptively integrates these features to improve the accuracy and stability of SST prediction. The experimental results, relying on the OISST data obtained from both remote sensing satellites and in situ platforms, demonstrate the advantages of the proposed DSNet_SST over baseline methods in terms of SST prediction accuracy.

关键词： Temperature sensors Accuracy Signal processing Feature extraction Spatiotemporal phenomena Data mining Speech processing Thermal stability Surface treatment Meteorology

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Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time distributed Deep CNNs, RNNs and Attention-Based Mechanisms 5

Analysis of Real-Time Hostile Activitiy Detection from Spati...

引用

5th IEEE International conference on image processing Applications and Systems (IPAS)

作者： Siddique, Labib Ahmed Junhai, Rabita Reza, Tanzim Khan, Salman Sayeed Rahman, Tanvir BRAC Univ Dept Comp Sci & Engn 66 Mohakhali Dhaka 1212 Bangladesh Univ Delaware Coll Engn Dept Comp & Informat Sci Newark DE 19716 USA

ISBN: (纸本)9781665462198

Real-time video surveillance, through CCTV camera systems has become essential for ensuring public safety which is a priority today. Although CCTV cameras help a lot in increasing security, these systems require constant human interaction and monitoring. To eradicate this issue, intelligent surveillance systems can be built using deep learning video classification techniques that can help us automate surveillance systems to detect violence as it happens. In this research, we explore deep learning video classification techniques to detect violence as they are happening. Traditional image classification techniques fall short when it comes to classifying videos as they attempt to classify each frame separately for which the predictions start to flicker. Therefore, many researchers are coming up with video classification techniques that consider spatiotemporal features while classifying. However, deploying these deep learning models with methods such as skeleton points obtained through pose estimation and optical flow obtained through depth sensors, are not always practical in an IoT environment. Although these techniques ensure a higher accuracy score, they are computationally heavier. Keeping these constraints in mind, we experimented with various video classification and action recognition techniques such as ConvLSTM, LRCN (with both custom CNN layers and VGG-16 as feature extractor) CNNTransformer and C3D. We achieved a test accuracy of 80% on ConvLSTM, 83.33% on CNN-BiLSTM, 70% on VGG16-BiLstm,76.76% on CNN-Transformer and 80% on C3D.

关键词： Deep learning Video classification Neural network Attention based encoder Violence detection LRCN ConvLSTM Transformer C3D

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Accelerating the performance of data analytics using network-centric processing 21

Accelerating the performance of data analytics using network...

引用

15th ACM International conference on distributed and Event-Based Systems, DEBS 2021

作者： Boughzala, Bochra Koldehofe, Boris University of Groningen Bernoulli Institute Groningen Netherlands

ISBN: (纸本)9781450385558

distributed execution of real-time data analytics such as event stream processing is the key to scalability, performance and reliable detection of situation changes. Although real-time analytics is highly I/O centric, existing methods supporting the efficient execution of data analytics functions mostly rely on traditional compute models that are available in data centers, e.g., CPU or GPU based processing models, but treat the network mainly as a blackbox. However, with recent advance in software-defined networking (SDN) and the standardization of packet processing pipeline, data analytics functions can be offloaded to programmable switches and benefit from hardware acceleration in an easier and more flexible way than a decade ago. In this paper we focus on the potential of in-network processing to enhance the performance of the overall real-time data analytics application. We aim to contribute to an (i) understanding on how in-network processing can accelerate real-time data analytics and (ii) assess what models of in-network computing can accelerate which event processing functions considering the limitations of network models compared to traditional compute models. We motivate the potential and illustrate the research problems in the context of load balancing which is an important concept in the data-parallel execution of event processing systems. © 2021 ACM.

关键词： Data Analytics

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SUPERVISED MULTI-VIEW distributed HASHING

SUPERVISED MULTI-VIEW DISTRIBUTED HASHING

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IEEE International conference on image processing (ICIP)

作者： Tang, Yunpeng Shen, Xiaobo Ji, Zexuan Wang, Tao Fu, Peng Sun, Quan-Sen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728163956

Multi-view hashing efficiently integrates multi-view data for learning compact hash codes, and achieves impressive large-scale retrieval performance. In real-world applications, multi-view data are often stored or collected in different locations, where hash code learning is more challenging yet less studied. To fulfill this gap, this paper proposes a novel supervised multi-view distributed hashing (SMvDisH) for hash code learning from multi-view data in a distributed manner. SMvDisH yields the discriminative latent hash codes by joint learning of latent factor model and classifier. With local consistency assumption among neighbor nodes, the distributed learning problem is divided into a set of decentralized subproblems. The subproblems can be solved in parallel, and the computational and communication costs are low. Experimental results on three large-scale image datasets demonstrate that SMvDisH achieves competitive retrieval performance and trains faster than state-of-the-art multi-view hashing methods.

关键词： Hashing distributed learning multi-view data

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Markov Stochastic Processes Application Tools to Improve the Characteristics of distributed Systems Synchronization Procedures

Markov Stochastic Processes Application Tools to Improve the...

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International conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Tatyana Vasilyevna Azarnova Pavel Valerievich Polukhin Irina Leonidovna Kashirina Applied Mathematics Informatics and Mechanics Voronezh State University Voronezh Russia

Appling mathematical modeling methods for analyses synchronization processes of distributed data processing systems makes it possible to significantly optimize these processes and guarantee the required level of consistency. To analyze stochastic distributed synchronization procedures, mathematical modeling tools are needed to adequately reflect their stochastic nature and form a probabilistic time characteristics assessment of the shared resource ownership distributed between the parallel processes. The research carried out within the current paper shows that stochastic modeling tools are able to solve a number of complex problems in this area and in particular to adapt synchronization algorithms for use in heterogeneous computing systems with a different set of hardware components and providing an acceptable synchronization time for a large amount of data. The paper essentially analyzes Markov synchronization models with group maintenance and considers tools for bringing these models to the case of arbitrary service time.

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Performance Analysis and Optimization of distributed Arithmetic-Based Convolutional Algorithms for FIR Filters on FPGA

Performance Analysis and Optimization of Distributed Arithme...

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Irish Signals and Systems conference (ISSC)

作者： Cheng Chen Vladyslav Romashchenko Michael Brutscheck Ingo Chmielewski Hochschule Anhalt Anhalt University of Applied Sciences Köthen Germany

distributed arithmetic (DA) implementation for finite impulse response (FIR) filters on field-programmable gate arrays (FPGAs) is highly desirable in digital signal processing due to its fast computational speed and low power consumption. However, traditional LUT-based DA implementation on FPGAs is challenging because of its high memory space requirements. To overcome this challenge, LUT-partition and MUX-incorporation techniques have been proposed to reduce memory space, but they also increase the FPGA resource utilization. Furthermore, the inherent serial nature of DA computing can limit data throughput. parallel processing of multiple bits can improve computational performance but at the cost of chip area. Therefore, it is beneficial to combine optimization methods to achieve desired performance. This paper proposes a comprehensive approach for optimizing memory space, computational performance, and chip area by analyzing different LUT partitions and incorporating MUX configurations. The proposed method is evaluated on a Xilinx Zynq 7010 FPGA, demonstrating its effectiveness.

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Contrastive Enhanced Filter Model Based on Bidirectional Transformer for Sequential Recommendation

Contrastive Enhanced Filter Model Based on Bidirectional Tra...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Anpeng Sun Jun Lu College of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

Transformer-based sequential recommendation is highly powerful as it can capture short-term and long-term sequential recommendation. It plays a crucial role in personalized recommendation systems, aiming to extract dynamic and diverse preference information from users’ realtime growing behaviors. This is mainly due to its unique selfattention mechanism, which utilizes item-item interactions within the sequences. However, in online platforms, due to the interweaving of items in user behavior, user behavior data inevitably contains noise. Currently, most methods model user preferences in the time domain, overlooking the influence of frequency patterns on user behavior. To address this issue, this paper shifts the perspective to the frequency domain and proposes a novel contrast-enhanced filtering model for sequential recommendation based on the bidirectional Transformer, called CEFRec. Specifically, the paper designs a filtering layer on top of the bidirectional Transformer, allowing learnable filters to operate on different layers’ spectra. This enables the model to capture various frequency patterns and adaptively attenuate noise information within the frequency domain. Next, the model combines the cloze task mask and dropout masks to generate high-quality positive samples and utilizes contrastive learning to enhance the quality of user embeddings learned from the frequency domain. Finally, experimental results on three public benchmark datasets demonstrate that the CEFRec model outperforms state-of-theart sequential recommendation models.

关键词： Adaptation models Time-frequency analysis Electric potential Filters image processing Noise Contrastive learning Transformers Time-domain analysis Recommender systems

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FPGA-based Moving Object Detection Leveraging Frame Difference and Morphological Filtering

FPGA-based Moving Object Detection Leveraging Frame Differen...

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Computer, Vision and Intelligent Technology (ICCVIT), International conference on

作者： Yuexin Wang Xiaochen Jing Wenhao Wang Mengli Zhu Dongchen Zhu Bionic Vision System Laboratory Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Moving object detection plays a significant role in video surveillance. However, existing moving object detection methods often rely on software implementations, which means low real-time performance and high power consumption. This paper's core detection algorithm employs a frame difference method that is enhanced by morphological filtering. Additionally, we propose an architecture that integrates FPGA(Field Programmable Gate Array) and ARM(Advanced RISC Machine), fully leveraging the parallel computing advantages of FPGA and the high processing efficiency of ARM. The system utilizes a ZYNQ7000 SoC, coupled with an OV7725 camera for image capture and DDR3 SDRAM for data caching, to address the challenges of high-speed data processing and low power consumption. Experimental results show that the system meets the requirements for high real-time performance and low power consumption with a frame rate of 85.9375 frames per second and a total power consumption of 1.101 W.

关键词： Power demand Reduced instruction set computing Program processors Filtering Object detection parallel processing Video surveillance Real-time systems Software Field programmable gate arrays

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Weighted Node Mapping and Localisation on a Pixel Processor Array

Weighted Node Mapping and Localisation on a Pixel Processor ...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Castillo-Elizalde, Hector Liu, Yanan Bose, Laurie Mayol-Cuevas, Walterio Univ Bristol Bristol Robot Lab Bristol BS34 8QZ Avon England Univ Bristol Visual Informat Lab Bristol BS1 5DD Avon England

ISBN: (纸本)9781728190778

This paper implements and demonstrates visual route mapping and localisation upon a Pixel Processor Array (PPA). The PPA sensor comprises of an array of processing Elements (PEs), each of which can capture and process visual information directly. This provides significant parallel processing power allowing novel ways in which information can be processed on-sensor. Our method predicts the correct node within a topological map generated from an image sequence by measuring image similarities, spatial coherence, and exploiting the parallel nature of the PPA. Our implementation runs at +300Hz on large public datasets with +2K locations requiring 2.5W at 500 GOPS/W. We compare vs traditionally implemented methods demonstrating better F-1 performance even on simulation. As far as we are aware, we present the first on-sensor mapping and localisation system running entirely on-sensor.

关键词： Visualization Spatial coherence Prototypes parallel processing Robot sensing systems Hardware Computational efficiency

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Joint DBF and SAO parallel filtering based on multithread load balancing

Joint DBF and SAO parallel filtering based on multithread lo...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Ma, Hao Hu, Dong Li, Yi Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China

This paper presents a joint parallel loop filtering algorithm based on multi-thread load balancing in HEVC decoding, which implements the parallel processing of deblocking filtering (DBF) and sample adaptive compensation (SAO). Because of the diversity of video, the texture of different regions in an image is also different, which leads to various CTU partition methods. Therefore, the number of the boundary to be filtered is greatly different, resulting the computation load among multiple threads unbalanced in parallel processing. To solve this problem, an area division scheme is proposed, which divides the image into multiple areas, and the number of boundaries to be filtered in each area is similar. Then, the mapping relationship table is used to allocate these areas to multiple threads for parallel processing, so as to achieve the load balancing among the filtering threads. Finally, the cache technology is used to combine DBF and SAO to reduce the delay between them and improve the overall parallelism of the loop filter. Experimental results show that the performance of the proposed load balancing joint filtering algorithm is 8.15% higher than the previous scheme. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Textures

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