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A Deployment Optimization for Wireless Sensor Networks Based on Stacked Auto Encoder and Probabilistic Neural Network

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Digital Communications and Networks 2024年

作者： Hui Feng Chen Xu Bo Jin Min Zhang School of Intelligent Engineering Technology Jiangsu Vocational College of Finance and Economics Huai'an 223003 China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai 200235 China Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra 3030-290 Portugal

In the field of consumer electronics, Wireless Sensor Networks (WSNs) can be used to build large-scale IoT systems and achieve intelligent data collection and processing. This paper proposes a deep neural network model SAE-PNN by Stacked Auto Encoder (SAE) and Probabilistic Neural Network (PNN) in a stack manner. A comprehensive training algorithm of SAE-PNN based on a group of orthogonal function bases is established by introducing a time-varying input and connection weight function, based on the conventional algorithm of unsupervised layer-by-layer initialization and gradient descent of deep neural network. Our proposed SAE-PNN model is used to predict the distance from unknown nodes to known nodes. Based on the collaborative work of nodes and network coverage, an optimal set of working nodes is established to reduce the energy loss of network nodes. A balance relationship between network coverage and energy consumption has been established to solve the problem of optimizing the balance in location coverage and the contradiction between individual nodes and the overall performance of the network. The experimental results show that the proposed method can improve the coverage of the monitored area and the perceived quality of service, and uses a distributed node synchronous scheduling mechanism to reduce the overall energy consumption of the network.

关键词： Wireless sensor networks Stacked auto encoder Probabilistic neural network Deep neural network Deployment strategy Node energy consumption

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Low-Cost, High Sensitivity Paper-Based Bacteria Impedance Sensor Based on Vertical Flow Assay

SSRN

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SSRN 2022年

作者： Long, Yifan Ai, Zhehong Zhang, Longhan Zhang, Han Jiang, Jing Logan, G. Liu Research Center for Intelligent Sensing Systems Zhejiang Laboratory Zhejiang Hangzhou311100 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Zhejiang Hangzhou310024 China Department of Electrical & Computer Engineering the University of Illinois at Urbana-Champaign UrbanaIL61801 United States

This study proposed a low-cost, portable paper-fluidic vertical flow assay bacteria counter at high accuracy. We designed the sensor at a low fabrication cost based on e-beam evaporation and 3D printing based on the impedance measurement principle. Interdigitated (IDT) electrodes were coated on the filter membrane by e-beam evaporation with a shadow mask. We could print wafer-scale frames with low-melting temperature 3D printing material to confine liquid bacteria samples within the IDT sensing region. This novel fabrication technique significantly reduced the chip cost to less than 1% of silicon-based chips. Two equivalent circuit models were proposed for different concentration ranges to analyze the principle of paper-based impedance bacteria sensors. We proposed an "improved model" based on the Randles model for low concentration by considering the leaky double layer capacitor effect and spherical diffusion from nano-structural electrodes of the gold-coated filter membrane. The hook effect was found and explained at a high concentration range by the pearl chain effect. We modeled the pearl chain effect as a pair of RC parallel to the low concentration model. When users apply both models properly for analysis, this sensor could quantitatively measure cell concentrations from 40 to 400M per milliliter with superior linearity. © 2022, The Authors. All rights reserved.

关键词： Bacteria

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Angel's girl for blind painters: An efficient painting navigation system validated by multimodal evaluation approach

arXiv

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arXiv 2021年

作者： Liu, Hang Hu, Menghan Chen, Yuzhen Li, Qingli Zhai, Guangtao Yang, Simon X. Zhang, Xiao-Ping Yang, Xiaokang The Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University The Key Laboratory of Articial Intelligence Ministry of Education The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph The Department of Electrical Computer and Biomedical Engineering Ryerson University

For people who ardently love painting but unfortunately have visual impairments, holding a paintbrush to create a work is a very difficult task. People in this special group are eager to pick up the paintbrush, like Leonardo da Vinci, to create and make full use of their own talents. Therefore, to maximally bridge this gap, we propose a painting navigation system to assist blind people in painting and artistic creation. The proposed system is composed of cognitive system and guidance system. The system adopts drawing board positioning based on QR code, brush navigation based on target detection and bush real-time positioning. Meanwhile, this paper uses human-computer interaction on the basis of voice and a simple but efficient position information coding rule. In addition, we design a criterion to efficiently judge whether the brush reaches the target or not. According to the experimental results, the thermal curves extracted from the faces of testers show that it is relatively well accepted by blindfolded and even blind testers. With the prompt frequency of 1s, the painting navigation system performs best with the completion degree of 89% ± 8.37% and overflow degree of 347% ± 162.14%. Meanwhile, the excellent and good types of brush tip trajectory account for 74%, and the relative movement distance is 4.21 ± 2.51. This work demonstrates that it is practicable for the blind people to feel the world through the brush in their hands. In the future, we plan to deploy "Angle's Eyes" on the phone to make it more portable. The demo video of the proposed painting navigation system is available at: https://***/10.6084/***.9760004.v1. © 2021, CC BY.

关键词： Cognitive systems

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Multimodal Information-Based Broad and Deep Learning Model for Emotion Understanding

Multimodal Information-Based Broad and Deep Learning Model f...

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第40届中国控制会议

作者： Min Li Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

Multimodal information-based broad and deep learning model（MIBDL） for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using different processing methods of deep network and broad network, which obtains the features of depth and width dimensions. Moreover, random mapping in the initial broad learning network could cause information loss and its shallow layer network is difficult to cope with complex tasks. To address this problem, we use principal component analysis to generate the nodes of the broad learning, and the stacked broad learning network is adapted to make it easier for the existing broad learning networks to cope with complex tasks by creating deep variations of the existing network. To verify the effectiveness of the proposal, experiments completed on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-theart methods. According to the simulation experiments on the FABO database, by using the proposed method, the multimodal recognition rate is 17,54%, 1.24%, and 0.23% higher than those of the temporal normalized motion and appearance features（TN）,the multi-channel CNN（MCCNN）, and the hierarchical classification fusion strategy（HCFS）, respectively.

关键词： Convolution neural network broad learning emotion understanding facial expression body gesture

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Extracting the fundamental diagram from aerial footage

arXiv

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arXiv 2020年

作者： Makrigiorgis, R. Kolios, P. Timotheou, S. Theocharides, T. Panayiotou, C.G. KIOS Research Center for Intelligent Systems and Networks Department of Electrical and Computer Engineering University of Cyprus

Efficient traffic monitoring is playing a fundamental role in successfully tackling congestion in transportation networks. Congestion is strongly correlated with two measurable characteristics, the demand and the network density that impact the overall system behavior. At large, this system behaviour is characterized through the fundamental diagram of a road segment, a region or the network. In this paper we devise an innovative way to obtain the fundamental diagram through aerial footage obtained from drone platforms. The derived methodology consists of 3 phases: vehicle detection, vehicle tracking and traffic state estimation. We elaborate on the algorithms developed for each of the 3 phases and demonstrate the applicability of the results in a real-world setting. Copyright © 2020, The Authors. All rights reserved.

关键词： Antennas

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Assessing patient similarity through representation learning on medical records

arXiv

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arXiv 2021年

作者： Memarzadeh, Hoda Ghadiri, Nasser Samwald, Matthias Shahreza, Maryam Lotfi Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria Department of Computer Engineering University of Isfahan Shahreza Campus Iran

MSC Codes 68P20Patient similarity assessments, which identify patients similar to a given patient, can help improve medical care. They can be performed using electronic medical records (EMRs). This makes it necessary to convert heterogeneous EMRs into comparable formats to calculate their distance. While versatile document representation learning methods were developed in recent years, it is still unclear how complex EMR data should be processed to create the most useful patient representations. This study presents a new data representation method for EMRs that accounts for information in clinical narratives. To address the limitations of previous approaches, we propose an unsupervised method for building a patient representation that utilizes unstructured data integrated with structured data extracted from patients' EMR. For modeling extracted data, we employ a tree structure that captures the temporal relations of multiple medical events from EMR. We process clinical notes to extract symptoms, signs, and diseases using different tools such as Medspacy, MetaMap, and Scispacy and map entities to UMLS. After creating a tree data structure, we utilize two novel relabeling methods for the non-leaf nodes of the tree to capture two kinds of temporal aspects in extracted events. By traversing the tree, we generate a sequence that can be used to create an embedding vector for each patient. We extensively evaluate the proposed method across patient similarity and prediction tasks, and demonstrate that our methodology leads to lower mean squared error (MSE) and higher precisions and normalized discounted cumulative gain (NDCG) relative to baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Medical computing

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Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation

Enhanced Subspace Distribution Matching for Fast Visual Doma...

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作者： Kang, Qi Yao, Siya Zhou, Mengchu Zhang, Kai Abusorrah, Abdullah Department of Control Science and Engineering Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Department of Electrical and Computer Engineering Faculty of Engineering and Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah21481 Saudi Arabia Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or marginal distribution differences between domains but entirely ignoring label dependence (LD) information of source data in subspace. This article proposes a novel approach of domain adaptation, called enhanced subspace distribution matching (ESDM), which makes good use of label information to enhance the distribution matching between the source and target domains in a shared subspace. It reduces both conditional and marginal distributions in a shared subspace during a procedure of kernel principal dimensionality reduction and also preserves source data LD information to the maximum extent, thereby significantly improving cross domain subspace distribution matching. We also provide a learning algorithm with highly affordable computation, which solves the ESDM optimization problem without using time-consuming iterations. Results confirm that it can well outperform several recent domain adaptation methods on image classification tasks in terms of classification accuracy and running time. The results can be used in social cognition, person reidentification, and human-machine interactions. © 2014 IEEE.

关键词： Image classification

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models*

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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Data Science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Konstantina-Helen Tsarapatsani Antonis Sakellarios Vasilis D Tsakanikas Henrik Rudolf Hans J. Trampisch Vasileios C. Pezoulas George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas (FORTH) Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina GR 45110 Ioannina Greece University Medical Center Rostock Institute for Biostatistics and Informatics in Medicine and Ageing Research Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis’s results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided.

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Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks

arXiv

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arXiv 2022年

作者： Kleyko, Denis Karunaratne, Geethan Rabaey, Jan M. Sebastian, Abu Rahimi, Abbas The Redwood Center for Theoretical Neuroscience The University of California BerkeleyCA94720 United States The Intelligent Systems Lab Research Institutes of Sweden Kista16440 Sweden IBM Research - Zurich Rüschlikon8803 Switzerland The Department of Electrical Engineering and Computer Sciences The University of California BerkeleyCA94720 United States

Memory-augmented neural networks enhance a neural network with an external key-value memory whose complexity is typically dominated by the number of support vectors in the key memory. We propose a generalized key-value memory that decouples its dimension from the number of support vectors by introducing a free parameter that can arbitrarily add or remove redundancy to the key memory representation. In effect, it provides an additional degree of freedom to flexibly control the trade-off between robustness and the resources required to store and compute the generalized key-value memory. This is particularly useful for realizing the key memory on in-memory computing hardware where it exploits nonideal, but extremely efficient non-volatile memory devices for dense storage and computation. Experimental results show that adapting this parameter on demand effectively mitigates up to 44% nonidealities, at equal accuracy and number of devices, without any need for neural network retraining. © 2022, CC BY.

关键词： Degrees of freedom (mechanics)

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Mindset-Shaping Solutions with Innovative IT and PM Technologies in Higher Education

Mindset-Shaping Solutions with Innovative IT and PM Technolo...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Mariann Vàraljai Enikő Nagy Bàlint Nagy Zoltàn Balogh György Molnàr Department of Software Development and Application University of Dunaújváros Dunaújváros Hungary John von Neumann Faculty Óbuda University Antal Bejczy Center for Intelligent Robotics Budapest Hungary Department of Mathematics Institute of Computer Engineering University of Dunaújváros Dunaújváros Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary Department of Natural Sciences Faculty of Electrical Engineering Institute of Electrophysics Kandó Kálmán Óbuda University Budapest Hungary Department of Informatics Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Nitra Slovakia Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary Apáczai Csere János Faculty of Humanities Education and Social Sciences Széchenyi University Győr Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

Every society has its own characteristics, shaped from time to time by the various innovative, disruptive, but in any case, dynamically changing mechanisms that affect it most. In today's society, technological innovation plays a major role in the development of individuals and their abilities. Education is the most typical and obvious arena for shaping, developing, and preparing for the challenges of the future. As in the leading sectors, in the world of education, rapid responses and immediate reactions to change significantly increase the ability to achieve and maintain success and thus marketability. As a sector responsible for developing the competences of individuals, building knowledge and training professionals, education should seek to keep pace with trends and break with traditional approaches and methods, so that, in addition to incorporating technological developments, a renewed approach to organizational development and methodology is an important means of achieving the desired success and effectiveness. In the course of our work, we provide a summary insight into the project work of student groups who require up-to-date IT skills, use advanced IT tools, plan, and implement their web development workflows supported by database management, front-end planning and design, and back-end programming with a project management approach according to the latest trends, interpreting the advantages and disadvantages of working methods for success.

关键词： Training Knowledge engineering Technological innovation Shape Project management Market research Planning

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