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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Domenico Gaglione Leonardo M. Millefiori Paolo Braca Peter Willett Moe Z. Win NATO STO-CMRE La Spezia Italy Electrical and Computer Engineering Dep. University of Connecticut Storrs CT USA Laboratory for Information and Decision Systems MIT Cambridge MA USA

Seabed-to-space situational awareness (S3A) aims to organize, fuse, and synthesize the massive volume of information collected from heterogeneous sensors, i.e., underwater, terrestrial, and space-based sensors, and therefrom extract knowledge thence available to defence operators, enabling informed decision-making. Heterogeneous sensors provide observations of targets in different domains (e.g., air, water surface, undersea), and with different modalities, perspectives, latencies, and update rates. They complement each other, and an effective approach is needed to combine the data they generate. This paper introduces a comprehensive framework for multi-target tracking based on the sum-product algorithm (SPA) that models the different characteristics of the sensors and handles the appearance of targets in a complex multi-domain environment both through spontaneous births and through spawning from existing targets. The efficacy of this approach is demonstrated through a simulated maritime scenario informed by real-world observation streams.

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Solving the vehicle routing problem with deep reinforcement learning

arXiv

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arXiv 2022年

作者： Foà, Simone Coppola, Corrado Grani, Giorgio Palagi, Laura Sapienza University of Rome Dep. of Computer Science Control and Management Engineering Rome Italy Sapienza University of Rome Dep. of Statistical Science Rome Italy

Recently, the applications of the methodologies of Reinforcement Learning (RL) to NP-Hard Combinatorial optimization problems has become a popular topic. This is essentially due to the nature of the traditional combinatorial algorithms, often based on a trial-and-error process. RL aims at automating this process. At this regard, this paper focuses on the application of RL for the Vehicle Routing Problem (VRP), a famous combinatorial problem that belongs to the class of NP-Hard problems. In this work, as first, the problem is modeled as a Markov Decision Process (MDP) and then the PPO method (which belongs to the Actor-Critic class of Reinforcement learning methods) is applied. In a second phase, the neural architecture behind the Actor and Critic has been established, choosing to adopt a neural architecture based on the Convolutional neural networks, both for the Actor and the Critic. This choice resulted effective to address problems of different sizes. Experiments performed on a wide range of instances show that the algorithm has good generalization capabilities and can reach good solutions in a short time. Comparisons between the algorithm proposed and the state-of-the-art solver OR − TOOLS show that the latter still outperforms the Reinforcement learning algorithm. However, there are future research perspectives, that aim to upgrade the current performance of the algorithm *** Codes 90-08 © 2022, CC BY.

关键词： Reinforcement learning

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Experimental Comparison of Global Planners for Trajectory Planning of Mobile Robots in an Unknown Environment with Dynamic Obstacles

Experimental Comparison of Global Planners for Trajectory Pl...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Zehra Tüfekçi Gökhan Erdemir Department of Computer Engineering Istinye University İstanbul Turkiye Department of Computer Science and Engineering Istanbul Sabahattin Zaim University Dep. of Engineering Management & Technology University of Tennessee at Chattanooga Tennessee USA

Interacting with their workspace, detecting obstacles, and real-time path planning in the field to reach the goal in the unknown environment are current working areas of mobile robotics. Working in an unknown environment with dynamic obstacles is a challenging topic for autonomous robots, which need to protect themselves from the dynamic obstacles they encounter, not damage the obstacle, and re-plan their path according to their new position. This study performed an experimental comparison of three different global planners for trajectory planning of mobile robots in an unknown environment with dynamic obstacles. Global Planner, Carrot Planner, and NavfnROS planners in the ROS package were used as global planners in two different mazes with dynamic obstacles. The durations from the start to the goal point in the two designed environments were determined for each global planner.

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Ka-Band Phased Antenna Array Concept for High-EIRP Satellite Connections

Ka-Band Phased Antenna Array Concept for High-EIRP Satellite...

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European Conference on Antennas and Propagation, EuCAP

作者： Stefano Moscato Alessandro Fonte Steven Caicedo Mejillones Alessandro DiCarlofelice Emidio DiGiampaolo Piero Tognolatti Emilio Arnieri Giandomenico Amendola Luigi Boccia Matteo Oldoni Research and Development Dep. SIAE MICROELETTRONICA Cologno Monzese MI Italy Dep. of Industrial and Information Engineering and Economics Univ. of L'Aquila L'Aquila Italy Dep. of Computer Eng. Modeling Electronics and Systems Univ. of Calabria Rende CS Italy Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy

This work deals with an innovative architecture of a phased antenna array operating in the Ka band. It envisages the adoption of custom designed PA together with 64 hollow metallic horns as radiating elements. The proposed architecture is designed to work in the 25.5 to 27 GHz frequency span with a target EIRP higher than 70 dBm, even considering the maximum beam steering configuration of $\pm 20^{\circ}$ . The proposed concept highlights the flexibility in targeting different power levels, antenna gain and steering capabilities to finally address a large number of use-cases. The exploitation of non-uniform array is also taken into account to prevent grating lobes and increasing the steering capabilities of the phased array.

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Impedance measurements for demineralized tooth lesions assessment

Impedance measurements for demineralized tooth lesions asses...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Sannino, Isabella Iannucci, Leonardo Lombardo, Luca Parvis, Marco Comba, Allegra Arpaia, Pasquale Angelini, Emma Grassini, Sabrina Politecnico di Torino Department of Applied Science and Technology Turin Italy Politecnico di Torino Department of Electronics and Telecommunication Turin Italy Università Degli Studi di Torino Department of Surgical Science Turin Italy Università Degli Studi di Napoli Dep. of Electrical Engineering and Information Technology Federico II Naples Italy

ISBN: (纸本)9781665493840

This work deals with the design and development of a non invasive though rather effective solution to detect carious lesions. The main aim of the study is to develop a simple automatic approach for the assessment of the tooth demineralization, which represents the earlier stage of the dynamic carious process, by means of impedance spectroscopy. In particular, impedance measurements were carried out on 50 extracted human teeth. Teeth were demineralized in-vitro according to a validated protocol;then, a morphological analysis of the tooth surface was performed by scanning electron microscopy to confirm enamel demineralization. The proposed approach tries to take advantage of the change of impedance phase due to the demineralization process. Data analysis confirmed that the best frequency for discriminating between demineralized and non-demineralized teeth is about 15 Hz, and this parameter can be used for building up an automatic classifier based on Multilayer Perceptron (MLP) topology. Impedance data were processed by using a modified single neuron, which allows classifying demineralized and sound teeth with an error rate of about 7%, estimating therefore the presence of carious lesions in progress. The presented work can be considered as a feasibility study with a final goal to conceive a simple and low-cost measurement system to identify caries at an early stage © 2023 IEEE.

关键词： Multilayers

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Intelligent Recognition of Immune Cells: Analysis and Research

Intelligent Recognition of Immune Cells: Analysis and Resear...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Chih-En Li Wei-Lun Liu Kun-Yi Lai Yu-Hsiang Chiang Xiaowei Yan Hao-Xiang Chen Hsu-Ching Li Wan-Shu Cheng Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Institute for Systems Biology Seattle Washington U.S.A Dep. of Computer Science and Information Management Providence University Taichung Taiwan

Immunotherapy utilizes the potential of the patient's immune system to fight disease. T-lymphocytes have become the central focus of the immune system in the fight against cancer. One of the fundamental studies in immunotherapy is how to accurately identify various types of immune cells. Therefore, the study of immune-related cells will be an important reference for the treatment of COVID19 or other related diseases. In this paper, a series of experiments were conducted with COVID-19 patients as the starting point to design a strategy to identify the important subtypes of CD$4^{+}$T cells. In this paper, an intelligent machine learning algorithm is proposed according to the results of different experiments of linear and nonlinear dimension reduction algorithms, integrating the important characteristics of these two algorithms and improving the performance. Our experimental results show that the algorithm designed based on the front-end experiments can effectively distinguish the important lymphocytes. The proposed algorithm not only improves the performance, but also the dimension reduction procedure keeps the important features of both global and regional data. The experimental results of this paper will be beneficial for the subsequent analysis of the clustering in identifying important subtypes of CD$4^{+}$T cells. Most importantly, this wm significantly improve the procedure of manually integrating and labeling the recorded data after several tedious and monotonous analyses with external software.

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Machine Learning Approach to Detection of Offensive Language in Online Communication in Arabic 1

Machine Learning Approach to Detection of Offensive Language...

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1st IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2021

作者： Alakrot, Azalden Fraifer, Muftah Nikolov, Nikola S. The College of Medical Technology Department of Information Technology Yefren Libya The College of Industrial Technologies-CIT Department of Electronic Engineering Misurata Libya University of Limerick Dep. of Computer Science and Information System Limerick Ireland

ISBN: (纸本)9781665418560

This paper presents the results of several machine learning experiments, conducted with a dataset of YouTube comments in Arabic. The experiments aim at studying the impact of various text preprocessing, feature-extraction and feature-selection techniques on the accuracy of a document classifier for detection of offensive language in online communication in Arabic. Regarding data pre-processing, our experiments focus on filtering out noisy characters and normalising inconsistencies present in casual online writing in Arabic. The combined effect of these data preprocessing techniques and a few feature-extraction and feature-selection methods is then evaluated by training document classifiers. Our results give evidence that it is possible to train a classifier for the detection of offensive language on Arabic social media with reasonable overall accuracy of 0.84, and precision, recall and F1-score of 0.89, 0.76 and 0.81, respectively. © 2021 IEEE.

关键词： Support vector machines

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Machine-Learning-Assisted Optimization of Type-II Superlattices for Enhanced Vertical Hole Mobility

Machine-Learning-Assisted Optimization of Type-II Superlatti...

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International Conference on Numerical Simulation of Optoelectronic Devices, (NUSOD)

作者： John Glennon Enrico Bellotti Div. of Material Science and Engineering Boston University Boston Massachusetts USA Dep. of Electrical and Computer Engineering Div. of Material Science and Engineering Boston University Boston Massachusetts USA

Gaussian process regression is used to develop a model for predicting the superlattice (SL) structures grown on a 6.3 Å lattice. This model is used to search for optimized hole transport properties. Nonequilibrium Green’s functions calculations are used to determine the vertical hole mobility of the structure which is compared with both the calculated and experimental results for an experimental SL grown on a GaSb substrate. Ultimately, a simple analytical model is used to approximate and compare the quantum efficiency of a photodiode containing each structure demonstrating an improvement in device performance using the optimized SL structure.

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Investigation on Readout Strategy for Aqueous NH3Sensor Developed by Additive Technology 6

Investigation on Readout Strategy for Aqueous NH3Sensor Deve...

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6th IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd4.0 and IoT 2023

作者： Ando, Bruno Baglio, Salvatore Castorina, Salvatore Graziani, Salvatore Petralia, Salvatore Messina, Maria Anna Maugeri, Ludovica Sardini, Emilio Serpelloni, Mauro Bellitti, Paolo Neri, Giovanni Ferlazzo, Angelo Dieei University of Catania Catania Italy University of Catania Dep. of Drug Science and Health Catania Italy University-Polyclinic of Catania Exp. Newborn Screening Lab Catania Italy University of Brescia Department of Information Engineering Brescia Italy University of Messina Dipartimento di Ingegneria Messina Italy

ISBN: (纸本)9798350396577

Low-cost solutions for the detection of ammonia and its related compounds are of deep interest, due to several application contexts requiring suitable approaches for its detection, both as a direct product of industrial or biological processes, as well as a byproduct of complex reactions. This paper addresses the need of ammonia detection for the indirect estimation of phenylalanine, which is of interest in the context of Phenylketonuria disorder. The main idea behind the proposed approach is to exploit the ammonia produced by the enzymatic reaction phenylalanine-phenylalanine ammonia-lyase. The sensing methodology investigated through this paper is based on a chemo-capacitive readout strategy, implemented by a stack structure using interdigited electrodes and a functional layer of Yttria-Stabilized Zirconia. The sensor behavior is addressed, along with a dedicated signal processing allowing to retrieve a reliable information on the target media from the sensor output. The system shows a responsivity of 0.077 pF/μM, a resolution of 2.5 μM and an accuracy of 22.5 μM in the 3σ level. © 2023 IEEE.

关键词： Ammonia

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Real-time System for Translating American Sign Language to Text Using Robust Techniques

Real-time System for Translating American Sign Language to T...

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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： Basel A. Dabwan Mukti E. Jadhav Hamad A. Abosaq Fekry A. Olayah Mohammed Al Yami Yahya A. Abdelrahman Department of Information System AlBaydha University AlBaydha Yemen Department of Computer Science Shri Shivaji Science & Art College India Computer Science Dep. Najran University Kingdom of Saudi Arabia Department of Information System Najran University Kingdom of Saudi Arabia Department of Information Systems Faculty of Computer Science and Information Systems Najran University Kingdom of Saudi Arabia Department of Information System College of Computer Science and Information Systems Najran University Kingdom of Saudi Arabia

Sign language serves as a vital medium of communication for the Deaf and dumb people. We present a real-time system to translate ASL into text, addressing the communication gap between deaf and dumb people and normal people. Our system leverages computer vision and machine learning techniques to accurately recognize ASL gestures. In this study, we introduce a convolutional neural network (CNN) and (LSTM) approaches designed to identify hand gestures associated with human actions as captured through images from cameras. By utilizing the hand's spatial positioning and orientation, we construct the training and testing datasets for the CNN-LSTM model, we were used 4680 skeleton images of the ASL alphabet, from A to Z as a dataset. The hand image undergoes an initial filtering process followed by classification, where a classifier determines the classification of the hand gesture. Subsequently, the refined images are harnessed to train the CNN-LSTM model. Our real-time system provides accurate and reliable results in real-time 99.9%, in addition to generating new datasets that are processed and can be trained on any model.

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