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Adaptation of an Iterative PCA to a Manycore Architecture for Hyperspectral Image Processing

JOURNAL OF SIGNAL PROCESSING systems FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2019年第7期91卷 759-771页

作者： Lazcano, R. Madronal, D. Fabelo, H. Ortega, S. Salvador, R. Callico, G. M. Juarez, E. Sanz, C. UPM Ctr Software Technol & Multimedia Syst CITSEM Madrid Spain ULPGC Res Inst Appl Microelect IUMA Las Palmas Gran Canaria Spain

This paper presents a study of the adaptation of a Non-Linear Iterative Partial Least Squares (NIPALS) algorithm applied to Hyperspectral Imaging to a Massively parallel Processor Array manycore architecture, which assembles 256 cores distributed over 16 clusters. This work aims at optimizing the internal communications of the platform to achieve real-time processing of large data volumes with limited computational resources and memory bandwidth. As hyperspectral images are composed of extensive volumes of spectral information, real-time requirements, which are upper-bounded by the image capture rate of the hyperspectral sensor, are a challenging objective. To address this issue, the image size is usually reduced prior to the processing phase, which is itself a computationally intensive task. Consequently, this paper proposes an analysis of the intrinsic parallelism and the data dependency within the NIPALS algorithm and its subsequent implementation on a manycore architecture. Furthermore, this implementation has been validated against three hyperspectral images extracted from both remote sensing and medical datasets. As a result, an average speedup of 17x has been achieved when compared to the sequential version. Finally, this approach has been compared with other state-of-the-art implementations, outperforming them in terms of performance.

关键词： NIPALS-PCA Hyperspectral imaging Massively parallel processing real-time processing parallel programming

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Apache IoTDB: A time Series Database for Large Scale IoT Applications

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ACM TRANSACTIONS ON DATABASE systems 2025年第2期50卷

作者： Wang, Chen Qiao, Jialin Huang, Xiangdong Song, Shaoxu Hou, Haonan Jiang, Tian Rui, Lei Wang, Jianmin Sun, Jiaguang Tsinghua Univ Beijing Peoples R China Timecho Ltd Beijing Peoples R China

A typical industrial scenario encounters thousands of devices with millions of sensors, consistently generating billions of data points. It poses new requirements of time series data management, not well addressed in existing solutions, including (1) device-defined ever-evolving schema, (2) mostly periodical data collection, (3) strongly correlated series, (4) variously delayed data arrival, and (5) highly concurrent data ingestion. In this paper, we present a time series database management system, Apache IoTDB. It consists of (i) a time series native file format, TsFile, with specially designed data encoding, and (ii) an IoTDB engine for efficiently handling delayed data arrivals and processing queries. We introduce a native distributed solution with distributed queries optimized by parallel operators. We also explore efficient TsFile synchronization mechanisms, ensuring seamless data integration without the need for ETL processes. The system achieves a throughput of 10 million inserted values per second. Queries such as 1-day data selection of 0.1 million points and 3-year data aggregation over 10 million points can be processed in 100 ms. Comparisons with InfluxDB, timescaleDB, KairosDB, Parquet and ORC over real world data loads demonstrate the superiority of IoTDB and TsFile.

关键词： time series data model database engine distributed

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Artificial intelligence for severe speech impairment: Innovative approaches to AAC and communication 2

Artificial intelligence for severe speech impairment: Innova...

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2nd Symposium of Psychology-Based Technologies, PSYCHOBIT 2020

作者： Murero, Monica Vita, Salvatore Mennitto, Andrea D'Ancona, Giuseppe University Federico II Italy Distributed Artificial Intelligence Laboratory Technische Universität Berlin Germany Vivantes Klinikum Group Berlin Germany

This paper aims to analyze how innovative Artificial Intelligence (AI) systems (Voiceitt®) for non-standard speech recognition may revolutionize Augmentative Alternative Communication (AAC) technology for people with severe speech impairments. By using built-in capabilities of portable devices, the AI-based algorithm may "understand" dysarthric speech and "translate" it into a fluid real-time user communication, thanks to a "voice donor" outcome system. The pattern classification algorithm is customized for non-standard speech recognition. The AI based system is personalized for each person unique language production and offers a real step forward in AAC efficiency. Earlier empirical findings show limitations in analogic assistive tools addressing Speech, Language, and Communication Needs (SLCNs). Recently, Speech- Generating Devices (SGDs) have been successfully used to support communication in patients with Autism and Dysarthria. With impressive improvements in recognizing non-standard natural language, AI-based technology (supported by deep learning, big data, and cloud processing) is offering a turning point for personalized Augmentative Alternative Communication (AAC). Upcoming AI-based innovations promise to generate an immense transformative effect on the everyday life of the speech impaired people, their caregivers, significant others, and the entire society. © 2020 CEUR-WS. All rights reserved.

关键词： Deep learning

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Load-Balanced parallel Constraint-Based Causal Structure Learning on Multi-Core systems for High-Dimensional Data

Load-Balanced Parallel Constraint-Based Causal Structure Lea...

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2019 ACM SIGKDD workshop on Causal Discovery, CD 2019

作者： Schmidt, Christopher Huegle, Johannes Bode, Philipp Uflacker, Matthias Enterprise Platform and Integration Concepts Hasso Plattner Institute University of Potsdam Potsdam Germany

In the context of high-dimensional data state-of-the-art methods for constraint-based causal structure learning, such as the PC algorithm, are limited in their application through their worst case exponential computational complexity. To address the resulting long execution time, several parallel extensions have been developed to exploit modern multi-core systems. These extensions apply a static distribution of tasks to the execution units to achieve parallelism, which introduces the problem of load imbalance. In our work, we propose a parallel implementation that follows a dynamic task distribution in order to avoid situations of load imbalance and improve the execution time. On the basis of an experimental evaluation on real-world high dimensional datasets, we show that our implementation has a better load balancing compared to an existing parallel implementation in the context of multivariate normal distributed data. For datasets that introduce load imbalance, our dynamic task distribution approach outperforms existing static approaches by factors up to 2.4. Overall, we increase the speed up from factors of up to 27, for the static approach, to factors of up to 39 for the dynamic approach, when scaling to 80 cores compared to a non-parallel execution. © 2019 ACM SIGKDD workshop on Causal Discovery. All Rights Reserved.

关键词： Learning algorithms

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EdgePVM: A serverless satellite edge computing constellation for changes detection using onboard parallel siamese Vision MAMBA

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Future Generation Computer systems 2025年

作者： Mahdi Amini Sedeh Saeed Sharifian Department of Electrical Engineering Amirkabir University of Technology 15914 Tehran Iran

Nowadays, satellite edge computing attracts more attention specially for remote sensing applications such as geological research, and natural disaster change detection. These applications necessitate high-resolution satellite images processing that requires high computational cost. Traditional satellite systems transmit all raw images data to the ground station for further processing which is a critical issue for realtime operation due to limited bandwidth and limited ground station observation time for transmission. To address realtime issues the processing needs to be performed on the satellites. On the other hand, satellite edge computing characterized by restricted energy for computation and limited network bandwidth to ground station. to cope with these challenges, we proposed a distributed satellite edge computing constellation architecture. Also, we proposed a parallel deep learning model called EdgePVM (parallel Vision Mamba) which is executed on cluster of GPUs in edge satellite. The EdgePVM reduces computational complexity and memory requirements that make realtime operation possible with limited energy. In addition, EdgePVM employs a multi-scale Siamese encoder by an enhanced feature extraction and fusion modules to generate difference maps for better change detection performance. Our approach benefits from an expanded receptive field of transformer architecture compared to the limited local receptive field of convolutional neural networks (CNNs). It also reduces quadratic computational complexity of self-attention in transformers by using MAMBA architecture. Using two standard satellite change detection datasets for evaluation, the proposed EdgePVM model has surpassed state of the art models in accuracy metrics while decreasing model complexity by 54.65%. Also, the evaluation of the EdgePVM on 4 node GPU cluster shows 3.9 speedup demonstrating its suitability for real-time satellite applications.

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Using spatiotemporal distribution of geocoded Twitter data to predict US county-level health indices

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 110卷 620-628页

作者： Thin Nguyen Larsen, Mark O'Dea, Bridianne Nguyen, Hung Nguyen, Duc Thanh Yearwood, John Dinh Phung Venkatesh, Svetha Christensen, Helen Deakin Univ Ctr Pattern Recognit & Data Analyt Geelong Vic Australia Deakin Univ Ctr Pattern Recognit & Data Analyt PRaDA Geelong Vic Australia Deakin Univ Geelong Vic Australia Deakin Univ Sch Informat Technol Geelong Vic Australia UNSW Black Dog Inst Sydney NSW Australia

For more than three decades, the US has annually conducted Behavioral Risk Factor Surveillance System (BRFSS) surveys to capture health behavior and health status of its people. Though this kind of information at population level is important for local governments to identify local needs, traditional datasets take several years to collate and to become publicly available. Geocoded social media data can provide an alternative reflection of local health trends. Due to the large scale of data, such as approximately two billions of tweets in this work, aggregating the tweets at a population level is common practice. While alleviating the computational cost, the aggregation operation would result in the loss of information on the distribution of data over the population, and such information may be important for identifying the health behavior and health outcomes of the population. In this work, we propose statistical features constructed on-top of primary features to predict county-level health indices. The primary features include topics and linguistic patterns extracted from tweets with county-decoded information. In addition, tweeting behaviors, particularly tweeting time, are used as a predictor of the health indices. Apache Spark, an advanced cluster computing paradigm, was employed to efficiently process the large corpus of tweets, including geo-decoding the geotags, extracting low-level (primary) features, and computing the statistical features. The results show strong correlations between publicly available health indices and the features extracted from geospatially coded Twitter data. Statistical features gained higher correlation coefficients than did the aggregation ones, suggesting the potential and applicability of the proposed features in a wide spectrum of applications on data analytics at population levels. In addition, the prediction performance was also improved when the temporal information was employed. This demonstrates that the real-time analysis o

关键词： Mining spatial and temporal data Statistical features Spatio-temporal features Cluster computing Large-scale parallel and distributed implementation Apache Spark

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Infracomposer: Policy-driven adaptive and reflective middleware for the cloudification of simulation & optimization workflows

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JOURNAL OF systems ARCHITECTURE 2019年 95卷 36-46页

作者： Beni, Emad Heydari Lagaisse, Bert Joosen, Wouter Katholieke Univ Leuven IMEC DistriNet B-3001 Leuven Belgium

The simulation and optimization of complex engineering designs in automotive or aerospace involves multiple mathematical tools, long-running workflows and resource-intensive computations on distributed infrastructures. Finding the optimal deployment in terms of task distribution, parallelization, collocation and resource assignment for each execution is a step-wise process involving both human input with domain-specific knowledge about the tools as well as the acquisition of new knowledge based on the actual execution history. In this paper, we present a policy-driven adaptive and reflective middleware that supports smart cloud-based deployment and execution of engineering workflows. This middleware supports deep inspection of the workflow task structure and execution, as well as of the very specific mathematical tools, their executions and used parameters. The reflective capabilities are based on multiple meta-models to reflect workflow structure, deployment, execution and resources. Adaptive deployment is driven by both human input as meta-data annotations as well as adaptation policies that reason over the actual execution history of the workflows. We validate and evaluate this middleware in real-life application cases and scenarios in the domain of aeronautics.

关键词： Workflow Cloud computing Middleware Adaptability Reflection parallel and distributed architectures

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Dynamic Multichannel Access for 5G and Beyond with Fast time-Varying Channel

Dynamic Multichannel Access for 5G and Beyond with Fast Time...

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IEEE International Conference on Communications (IEEE ICC) / workshop on NOMA for 5G and Beyond

作者： Wang, Shaoyang Lv, Tiejun Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing 100876 Peoples R China

ISBN: (纸本)9781728150895

In 5G and beyond wireless communication systems, providing satisfactory service to users in high-mobility scenarios becomes very essential. Unstable service supply caused by frequent handover under high-mobility compromises the service experience of users. Moreover, the demanding requirements for processing delays and non-immediate information processing errors are particularly prominent. This paper proposes a novel learning-based approach to solving the dynamic multichannel access (DMCA) problem under fast time-varying channel arising from high-mobility. Specifically, we first propose the subjective experience-based quality of service, and formulate the corresponding optimization problem based on the designed access criterion. Invoking our proposed prediction-based deep deterministic policy gradient algorithm and incremental learning-based online channel prediction model, a novel DMCA scheme, which combines the recurrent neural network with deep reinforcement learning, is proposed. Corroborated by experiments built in real channel data, the performance of proposed learning-based DMCA scheme approaches that derived from the exhaustive search method when making a decision at each time-slot, and is superior to the exhaustive search method when making a decision for every few time-slots. Furthermore, our scheme significantly reduces processing delays and effectively alleviates errors caused by the non-immediacy of information acquisition and processing.

关键词： Dynamic multichannel access recurrent neural network high-mobility deep reinforcement learning prediction-based deep deterministic policy gradient

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Learn-as-you-go with Megh: Efficient Live Migration of Virtual Machines

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IEEE TRANSACTIONS ON parallel AND distributed systems 2019年第8期30卷 1786-1801页

作者： Basu, Debabrota Wang, Xiayang Hong, Yang Chen, Haibo Bressan, Stephane Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 119077 Singapore Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai 200240 Peoples R China

Cloud providers leverage live migration of virtual machines to reduce energy consumption and allocate resources efficiently in data centers. Each migration decision depends on three questions: when to move a virtual machine, which virtual machine to move and where to move it? Dynamic, uncertain, and heterogeneous workloads running on virtual machines make such decisions difficult. Knowledge-based and heuristics-based algorithms are commonly used to tackle this problem. Knowledge-based algorithms, such as MaxWeight scheduling algorithms, are dependent on the specifics and the dynamics of the targeted Cloud architectures and applications. Heuristics-based algorithms, such as MMT algorithms, suffer from high variance and poor convergence because of their greedy approach. We propose an online reinforcement learning algorithm called Megh. Megh does not require prior knowledge of the workload rather learns the dynamics of workloads as-it-goes. Megh models the problem of energy-and performance-efficient resource management during live migration as a Markov decision process and solves it using a functional approximation scheme. While several reinforcement learning algorithms are proposed to solve this problem, these algorithms remain confined to the academic realm as they face the curse of dimensionality. They are either not scalable in real-time, as it is the case of MadVM, or need an elaborate offline training, as it is the case of Q-learning. These algorithms often incur execution overheads which are comparable with the migration time of a VM. Megh overcomes these deficiencies. Megh uses a novel dimensionality reduction scheme to project the combinatorially explosive state-action space to a polynomial dimensional space with a sparse basis. Megh has the capacity to learn uncertain dynamics and the ability to work in real-time without incurring significant execution overhead. Megh is both scalable and robust. We implement Megh using the CloudSim toolkit and empirically eva

关键词： Cloud computing reinforcement learning virtual machine live migration Markov decision process energy efficiency performance efficiency

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Monitoring Restaurants in real-time 2

Monitoring Restaurants in Real-Time

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2nd IEEE International workshop on Metrology for Industry 4.0 and Internet of Things (IoT) (MetroInd4.0 and IoT)

作者： Santos Batista, Isaac Danilo Sardina, Idalmis Milian Dantas, Rummenigge Rudson Univ Fed Rio Grande do Norte Inst Metropole Digital Natal RN Brazil Univ Fed Rio Grande do Norte Escola Ciencia & Tecnol Natal RN Brazil

ISBN: (纸本)9781728104294

Consumers in developed countries are wasting an estimated 222 million tonnes of food products per year, and most of the waste is produced at restaurants. The brazilian health regulatory agency: ANVISA requires that restaurants are monitored by nutritionists. One of their tasks is to control the temperature of food preservation to verify compliance with ANVISA regulations. For restaurants to avoid food waste and work within these regulatory standards, an efficient management is needed to streamline internal processes and to make more accurate decisions. The objective of this work is to create an intelligent software to monitor the quantity and temperature of a self-service restaurant buffet in order to minimize food waste, improve the handling of nutritional information and optimize the restaurant management. The system should be able to check the temperature and the consumption of food presented in buffet vats (also know as gastronorm) and make data available to relevant restaurant divisions like cooking, nutrition and real-time management. The system is created using resources of IoT, Edge/Fog and parallel/distributed computing. Node-red, ThingSpeak and Arduino IDE compose the technology framework.

关键词： smart monitoring systems Edge/Fog computing IoT integration parallel computing smart network devices

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