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{"title":"Advancements in spiking neural network communication and synchronization techniques for event-driven neuromorphic systems","authors":"Mahyar Shahsavari ,&nbsp;David Thomas ,&nbsp;Marcel van Gerven ,&nbsp;Andrew Brown ,&nbsp;Wayne Luk","doi":"10.1016/j.array.2023.100323","DOIUrl":"https://doi.org/10.1016/j.array.2023.100323","url":null,"abstract":"<div><p>Neuromorphic event-driven systems emulate the computational mechanisms of the brain through the utilization of spiking neural networks (SNN). Neuromorphic systems serve two primary application domains: simulating neural information processing in neuroscience and acting as accelerators for cognitive computing in engineering applications. A distinguishing characteristic of neuromorphic systems is their asynchronous or event-driven nature, but even event-driven systems require some synchronous time management of the neuron populations to guarantee sufficient time for the proper delivery of spiking messages. In this study, we assess three distinct algorithms proposed for adding a synchronization capability to asynchronous event-driven compute systems. We run these algorithms on <em>POETS (Partially Ordered Event-Triggered Systems)</em>, a custom-built FPGA-based hardware platform, as a neuromorphic architecture. This study presents the simulation speed of SNNs of various sizes. We explore essential aspects of event-driven neuromorphic system design that contribute to efficient computation and communication. These aspects include varying degrees of connectivity, routing methods, mapping techniques onto hardware components, and firing rates. The hardware mapping and simulation of up to eight million neurons, where each neuron is connected to up to one thousand other neurons, are presented in this work using 3072 reconfigurable processing cores, each of which has 16 hardware threads. Using the best synchronization and communication methods, our architecture design demonstrates 20-fold and 16-fold speedups over the Brian simulator and one 48-chip SpiNNaker node, respectively. We conclude with a brief comparison between our platform and existing large-scale neuromorphic systems in terms of synchronization, routing, and communication methods, to guide the development of future event-driven neuromorphic systems.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"20 ","pages":"Article 100323"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49750415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extractive social media text summarization based on MFMMR-BertSum","authors":"Junqing Fan ,&nbsp;Xiaorong Tian ,&nbsp;Chengyao Lv ,&nbsp;Simin Zhang ,&nbsp;Yuewei Wang ,&nbsp;Junfeng Zhang","doi":"10.1016/j.array.2023.100322","DOIUrl":"https://doi.org/10.1016/j.array.2023.100322","url":null,"abstract":"<div><p>The advancement of computer technology has led to an overwhelming amount of textual information, hindering the efficiency of knowledge intake. To address this issue, various text summarization techniques have been developed, including statistics, graph sorting, machine learning, and deep learning. However, the rich semantic features of text often interfere with the abstract effects and lack effective processing of redundant information. In this paper, we propose the Multi-Features Maximal Marginal Relevance BERT (MFMMR-BertSum) model for Extractive Summarization, which utilizes the pre-trained model BERT to tackle the text summarization task. The model incorporates a classification layer for extractive summarization. Additionally, the Maximal Marginal Relevance (MMR) component is utilized to remove information redundancy and optimize the summary results. The proposed method outperforms other sentence-level extractive summarization baseline methods on the CNN/DailyMail dataset, thus verifying its effectiveness.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"20 ","pages":"Article 100322"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49750419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning remaining useful life with incomplete health information: A case study on battery deterioration assessment","authors":"Luciano Sánchez ,&nbsp;Nahuel Costa ,&nbsp;José Otero ,&nbsp;David Anseán ,&nbsp;Inés Couso","doi":"10.1016/j.array.2023.100321","DOIUrl":"https://doi.org/10.1016/j.array.2023.100321","url":null,"abstract":"<div><p>This study proposes a method for developing equipment lifespan estimators that combine physical information and numerical data, both of which may be incomplete. Physical information may not have a uniform fit to all experimental data, and health information may only be available at the initial and final periods. To address these issues, a procedure is defined to adjust the model to different subsets of available data, constrained by feasible trajectories in the health status space. Additionally, a new health model for rechargeable lithium batteries is proposed, and a use case is presented to demonstrate its efficacy. The optimistic (max–max) strategy is found to be the most suitable for diagnosing battery lifetime, based on the results.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"20 ","pages":"Article 100321"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49765441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The study of the hyper-parameter modelling the decision rule of the cautious classifiers based on the Fβ measure","authors":"Abdelhak Imoussaten","doi":"10.1016/j.array.2023.100310","DOIUrl":"https://doi.org/10.1016/j.array.2023.100310","url":null,"abstract":"<div><p>In some sensitive domains where data imperfections are present, standard classification techniques reach their limits. To avoid misclassifications that have serious consequences, recent works propose cautious classification algorithms to handle this problem. Despite of the presence of uncertainty and/or imprecision, a point prediction classifier is forced to bet on a single class. While a cautious classifier proposes the appropriate subset of candidate classes that can be assigned to the sample in the presence of imperfect information. On the other hand, cautiousness should not be at the expense of precision and a trade-off has to be made between these two criteria. Among the existing cautious classifiers, two classifiers propose to manage this trade-off in the decision step by the mean of a parametrized objective function. The first one is the non-deterministic classifier (ndc) proposed within the framework of probability theory and the second one is “evidential classifier based on imprecise relabelling” (eclair) proposed within the framework of belief functions. The theoretical aim of the mentioned hyper-parameters is to control the size of predictions for both classifiers. This paper proposes to study this hyper-parameter in order to select the “best” value in a classification task. First the utility for each candidate subset is studied related to the values of the hyper-parameter and some thresholds are proposed to control the size of the predictions. Then two illustrations are proposed where a method to choose this hyper-parameters based on the calibration data is proposed. The first illustration concerns randomly generated data and the second one concerns the images data of fashion mnist. These illustrations show how to control the size of the predictions and give a comparison between the performances of the two classifiers for a tuning based on our proposition and the one based on grid search method.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"19 ","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SSFuzzyART: A Semi-Supervised Fuzzy ART through seeding initialization and a clustered data generation algorithm to deeply study clustering solutions","authors":"Siwar Jendoubi,&nbsp;Aurélien Baelde,&nbsp;Thong Tran","doi":"10.1016/j.array.2023.100319","DOIUrl":"https://doi.org/10.1016/j.array.2023.100319","url":null,"abstract":"<div><p>Semi-supervised clustering is a machine learning technique that was introduced to boost clustering performance when labeled data is available. Indeed, some labeled data are usually available in real use cases, and can be used to initialize the clustering process to guide it and to make it more efficient. Fuzzy ART is a clustering technique that is proved to be efficient in several real cases, but as an unsupervised algorithm, it cannot use available labeled data. This paper introduces a semi-supervised variant of the FuzzyART clustering algorithm (SSFuzzyART). The proposed solution uses the available labeled data to initialize clusters centers. In another hand, to deeply evaluate the characteristics of the proposed algorithm, a clustered binary data generation algorithm with controlled partitioning is also introduced in this paper. Indeed, the controlled generated clusters allows studying the characteristics of the proposed SSFuzzyART. Furthermore, a set of experiments is carried out on some available benchmarks. SSFuzzyART demonstrated better clustering prediction results than its classic counterpart.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"19 ","pages":"Article 100319"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance evaluation on work-stealing featured parallel programs on asymmetric performance multicore processors","authors":"Adnan","doi":"10.1016/j.array.2023.100311","DOIUrl":"https://doi.org/10.1016/j.array.2023.100311","url":null,"abstract":"<div><p>The speed difference between high-performance CPUs and energy-efficient CPUs, which are found in asymmetric performance multicore processors, affects the current form of Amdahl’s law equation. This paper proposes two updates to that equation based on the performance evaluation results of a simple parallel pi program written with OpenCilk. Performance evaluation was done by measuring execution time and instructions per cycle (IPC). The performance evaluation of the parallel program executed on the Intel Core i5 1240P processor did not indicate decreased performance due to asymmetric performance. Instead, the program with efficient work-stealing advantages from OpenCilk performed well. In the case of using the execution time of the P-CPU as a reference to obtain speedup, the evaluation results in a sublinear speedup. Conversely, in the case of using the execution time of the E-CPU as a reference, the evaluation results in a superlinear speedup. This paper proposes two updates to Amdahl’s law equation based on these two evaluation results.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"19 ","pages":"Article 100311"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49759091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple robust approaches for EEG-based driving fatigue detection and classification","authors":"Sunil Kumar Prabhakar,&nbsp;Dong-Ok Won","doi":"10.1016/j.array.2023.100320","DOIUrl":"https://doi.org/10.1016/j.array.2023.100320","url":null,"abstract":"<div><p>Electroencephalography (EEG) signals are used to evaluate the activities of the brain. For the accidents occurring on the road, one of the primary reasons is driver fatigueness and it can be easily identified by the EEG. In this work, five efficient and robust approaches for the EEG-based driving fatigue detection and classification are proposed. In the first proposed strategy, the concept of Multi-Dimensional Scaling (MDS) and Singular Value Decomposition (SVD) are merged and then the Fuzzy C Means based Support Vector Regression (FCM-SVR) classification module is utilized to get the output. In the second proposed strategy, the Marginal Fisher Analysis (MFA) is implemented and the concepts of conditional feature mapping and cross domain transfer learning are implemented and classified with machine learning classifiers. In the third proposed strategy, the concepts of Flexible Analytic Wavelet Transform (FAWT) and Tunable Q Wavelet Transform (TQWT) are implemented and merged and then it is classified with Extreme Learning Machine (ELM), Kernel ELM and Adaptive Neuro Fuzzy Inference System (ANFIS) classifiers. In the fourth proposed strategy, the concepts of Correntropy spectral density and Lyapunov exponent with Rosenstein algorithm is implemented and then the multi distance signal level difference is computed followed by the calculation of the Geodesic minimum distance to the Riemannian means and finally tangent space mapping is implemented to it before feeding it to classification. In the fifth or final proposed strategy, the Hilbert Huang Transform (HHT) is implemented and then the Hilbert marginal spectrum is computed. Then using the Blackhole optimization algorithm, the features are selected and finally it is classified with Cascade Adaboost classifier. The proposed techniques are applied on publicly available EEG datasets and the best result of 99.13% is obtained when the proposed Correntropy spectral density and Lyapunov exponent with Rosenstein algorithm is implemented with the multi distance signal level difference followed by the calculation of the Geodesic minimum distance to the Riemannian means and finally tangent space mapping is implemented with Support Vector Machine (SVM) classifier.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"19 ","pages":"Article 100320"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49749362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correspondenceless scan-to-map-scan matching of 2D panoramic range scans","authors":"Alexandros Filotheou,&nbsp;Andreas L. Symeonidis,&nbsp;Georgios D. Sergiadis,&nbsp;Antonis G. Dimitriou","doi":"10.1016/j.array.2023.100288","DOIUrl":"10.1016/j.array.2023.100288","url":null,"abstract":"<div><p>In this article a real-time method is proposed that reduces the pose estimate error for robots capable of motion on the 2D plane. The solution that the method provides addresses the recent introduction of low-cost panoramic range scanners (2D LIDAR range sensors whose field of view is 360<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>), whose use in robot localisation induces elevated pose uncertainty due to their significantly increased measurement noise compared to prior, costlier sensors. The solution employs scan-to-map-scan matching and, in contrast to prior art, its novelty lies in that matching is performed without establishing correspondences between the two input scans; rather, the matching problem is solved in closed form by virtue of exploiting the periodicity of the input signals. The correspondence-free nature of the solution allows for dispensing with the calculation of correspondences between the input range scans, which (a) becomes non-trivial and more error-prone with increasing input noise, and (b) involves the setting of parameters whose output effects are sensitive to the parameters’ correct configuration, and which does not hold universal or predictive validity. The efficacy of the proposed method is illustrated through extensive experiments on public domain data and over various measurement noise levels exhibited by the aforementioned class of sensors. Through these experiments we show that the proposed method exhibits (a) lower pose errors compared to state of the art methods, and (b) more robust pose error reduction rates compared to those which are capable of real-time execution. The source code of its implementation is available for download.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"18 ","pages":"Article 100288"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45249262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive review on 5G-based Smart Healthcare Network Security: Taxonomy, Issues, Solutions and Future research directions","authors":"Abdul Ahad ,&nbsp;Zahra Ali ,&nbsp;Abdul Mateen ,&nbsp;Mohammad Tahir ,&nbsp;Abdul Hannan ,&nbsp;Nuno M. Garcia ,&nbsp;Ivan Miguel Pires","doi":"10.1016/j.array.2023.100290","DOIUrl":"10.1016/j.array.2023.100290","url":null,"abstract":"<div><p>Healthcare is experiencing a fast change from a hospital-centric and specialist-focused model to one that is dispersed and patient-centric. Numerous technological advancements are driving this fast evolution of the healthcare sector. Communication technologies, among others, have permitted the delivery of customized and distant healthcare services. The present 4G networks and other wireless communication technologies are being utilized by the healthcare industry to create smart healthcare applications. These technologies are continuously evolving to meet the expectations and requirements of future smart healthcare applications. At the moment, current communication technologies are incapable of meeting the dynamic and complex demands of smart healthcare applications. Thus, the future 5G and beyond 5G networks are expected to support smart healthcare applications such as remote surgery, tactile internet and Brain-computer Interfaces. Future smart healthcare networks will combine IoT and advanced wireless communication technologies that will address current limitations related to coverage, network performance and security issues. This paper presents 5G-based smart healthcare architecture, key enabling technologies and a deep examination of the threats and solutions for maintaining the security and privacy of 5G-based smart healthcare networks.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"18 ","pages":"Article 100290"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42634701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved VIDAR and machine learning-based road obstacle detection method","authors":"Yuqiong Wang,&nbsp;Ruoyu Zhu,&nbsp;Liming Wang,&nbsp;Yi Xu,&nbsp;Dong Guo,&nbsp;Song Gao","doi":"10.1016/j.array.2023.100283","DOIUrl":"10.1016/j.array.2023.100283","url":null,"abstract":"<div><p>There are various types of obstacles in an emergency, and the traffic environment is complicated. It is critical to detect obstacles accurately and quickly in order to improve traffic safety. The obstacle detection algorithm based on deep learning cannot detect all types of obstacles because it requires pre-training. The VIDAR (Vision-IMU-based Detection and Range method) can detect any three-dimensional obstacles, but at a slow rate. In this paper, an improved VIDAR and machine learning-based obstacle detection method (hereinafter referred to as the IVM) is proposed. In the proposed method, morphological closing operation and normalized cross-correlation are used to improve VIDAR. Then, the improved VIDAR is used to quickly match and remove the detected unknown types of obstacles in the image, and the machine learning algorithm is used to detect specific types of obstacles to increase the speed of detection with the average detection time of 0.316s. Finally, the VIDAR is used to detect regions belonging to unknown types of obstacles in the remaining regions, improving detection performance with the accuracy of 92.7%. The flow of the proposed method is illustrated by the indoor simulation test. Moreover, the results of outdoor real-world vehicle tests demonstrate that the method proposed in this paper can quickly detect obstacles in real-world environments and improve detection accuracy.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"18 ","pages":"Article 100283"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43302968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}