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TechRxiv 2022年

作者： Firew, Hanna Barros, Michael Taynnan Faculty of Medicine and Health Technology Tampere University Finland School of Computer Science and Electronic Engineering University of Essex United Kingdom

Wireless Neural Interfaces are minimally invasive untethered links between the brain tissue and silicon platforms. Even though these interfaces have been envisioned for many biomedical applications, it is unclear how the ultimate technology will be, due to the lack of studies that support spatially distributed networks. In this paper, we contribute to the first step towards a networked wireless neural interfaces scenario by addressing the distributed power allocation through adaptable beamforming by varying the acoustic beam lateral resolution. Our technique provides selectivity and depthness of neural interface nodes inside the Brain’s neocortex. Our results show improvements in average power transfer efficiency for sparser beams compared to narrower ones for a randomly placed network of implantable devices with 15 nodes within a 4mm2 space in the neocortex. Lateral resolution indeed achieves selective of the devices, the average efficiency was non-constant throughout the network allowing devices to operate in different time scales suitable for asynchronous networks. Enabling a functioning network is essential for providing an impactful interface with the Brain to allow future usage of this technology in a diverse set of neurological diseases theranostics. © 2022, CC BY.

关键词： Beamforming

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Improving Referring Expression Comprehension by Suppressing Expression-unrelated Proposals 3

Improving Referring Expression Comprehension by Suppressing ...

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3rd International Conference on electronic Information engineering and computer, EIECT 2023

作者： Wang, Qun Zhu, Feng Li, Xiang Ye, Ruida Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Northeastern University Faculty of Computer Science and Engineering Shenyang110016 China Space Engineering University Department of Aerospace Engineering and Technology Beijing101416 China

ISBN: (纸本)9798350357707

The prevailing referring expression comprehension (REC) method is based on two stages: 1) detecting multiple candidate proposals using an object detector and 2) aligning proposals with text descriptions. Existing two-stage models have achieved significant improvements with excellent alignment modules. However, their alignment modules treat each proposal equally without filtering expression-unrelated proposals. These expression-unrelated proposals may interfere with the alignment modules' ability to accurately reason about the target proposal. These proposals also increase the difficulty of training alignment modules. To solve this problem, we propose a new framework, that can suppress expression-unrelated proposals for alignment modules. Specifically, we first design a suppression module, which calculates the relevance scores between the proposals and the expression by using the semantics in the expression. Then, these scores are integrated into three basic alignment modules, including subject, location, and relationship modules, to guide each module to focus on the expression-related proposals. We conduct extensive experiments on three benchmark datasets, and the results show the effectiveness of our suppression module and performance improvement. © 2023 IEEE.

关键词： Alignment

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Statistical Time Series Forecasting Models for Pandemic Prediction

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computer Systems Science & engineering 2023年第10期47卷 349-374页

作者： Ahmed ElShafee Walid El-Shafai Abeer D.Algarni Naglaa F.Soliman Moustafa H.Aly Department of Electrical Engineering Faculty of EngineeringAhram Canadian University6th October CityGizaEgypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease *** this case,COVID-19 data is a time-series dataset that can be projected using different ***,this work aims to gauge the spread of the outbreak severity over ***,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus *** have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML *** of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive ***,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best ***,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.

关键词： Forecasting COVID-19 predictive models medical viruses mathematical model market research diseases

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CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation

CITR: A Coordinate-Invariant Task Representation for Robotic...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Peter So Rafael I. Cabral Muchacho Robin Jeanne Kirschner Abdalla Swikir Luis Figueredo Fares J. Abu-Dakka Sami Haddadin Munich Institute of Robotics and Machine Intelligence TUM Germany Division of Robotics Perception and Learning KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Omar Al-Mukhtar University (OMU) Libya School of Computer Science University of Nottingham UK Electronic and Informatics Department Faculty of Engineering Mondragon Unibertsitatea Spain

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned *** this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

关键词： Learning systems Coordinate measuring machines Robot kinematics Taxonomy Fingerprint recognition Mathematical models Libraries

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Very Short-Term Chiller Energy Consumption Prediction Based on Simplified Heterogeneous Graph Convolutional Network

SSRN

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SSRN 2024年

作者： Zhou, Kate Qi Fernando, K.N. Adeepa Dai, Xilei Liu, Jiuwei Li, Wen-Tai Yuen, Chau Lee, Christopher H.T. Engineering Product Development Pillar Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Department of Computer Engineering Faculty of Engineering University of Peradeniya Galaha Road Peradeniya20400 Sri Lanka School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave 639798 Singapore

The implementation of predictive control within heating, ventilation, and air conditioning (HVAC) systems is imperative for maintaining operational efficacy and concurrently attaining energy efficiency. In the context of HVAC systems, accurate prediction of chiller energy consumption at very short-term, minute-level intervals is crucial for the timely implementation of optimal predictive control strategies. Current research predominantly focuses on short-term prediction at hourly or daily intervals, relying heavily on historical data for predictive insights. However, this approach imposes significant data acquisition burdens and may deviate from practical feasibility for very short-term prediction needs. This study introduces a novel approach employing the graph convolutional network (GCN) model for very short-term energy prediction based on an integrated simple graph (ISG-GCN) leveraging solely antecedent temporal information. The ISG addresses the challenges of determining connection weights between interdependent parameters,thereby eliminating the need for trial-and-error methods or reliance on potentially inaccurate historical correlation coefficients. Conceptualizing the chiller system as a black box, the model necessitates only seven discrete data points, significantly mitigating the data acquisition workload and facilitating seamless integration into buildings devoid of sophisticated sensor infrastructure. Applied to two simulation datasets encompassing large office buildings, the proposed model attains a mean absolute percentage error as minimal as 5.2%. © 2024, The Authors. All rights reserved.

关键词： Data acquisition

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3-sCHSL: Three-Stage Cyclic Hybrid SFS and L-SHADE Algorithm for Single Objective Optimization

3-sCHSL: Three-Stage Cyclic Hybrid SFS and L-SHADE Algorithm...

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Congress on Evolutionary Computation

作者： Heba Abdel-Nabi Mostafa Z. Ali Arafat Awajan Rami Alazrai Mohammad I. Daoud Ponnuthurai N. Suganthan Robert Reynolds Department of Computer Science Princess Sumaya University for Technology Amman Jordan Faculty of Computer & Information Technology Jordan University of Science & Technology Irbid Jordan Computer Science Department Mutah University Karak Jordan Department of Computer Engineering German Jordanian University Amman Jordan School of Electrical & Electronic Engineering Nanyang Technological University Singapore KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar College of Engineering Wayne State University Detroit USA

This paper proposes a novel hybridization of two metaheuristic algorithms to solve the real-parameter single objective numerical optimization problems. The proposed Three-stage Cyclic Hybrid SFS and L-SHADE (3-sCHSL) algorithm integrates the high-level interactions of Stochastic Fractal Search (SFS) and L-SHADE algorithms in a single framework. Furthermore, a guided control population initialization strategy is injected in 3-sCHSL to address the stagnation and diversity loss issues as the evolution process evolves. The performance of the proposed algorithm is tested under different complexity levels of different transformations of the CEC 2021 benchmark suite with dimension 20. The experimental results demonstrated the efficiency and competitiveness of the proposed algorithm against recent state-of-the-art algorithms. In addition, 3-sCHSL achieved a superior performance when evaluating two engineering design problems.

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Modulation Recognition of the OFDM Signals in the UAV-RIS Based AI-enabled 6G Network for Smart Agriculture

Modulation Recognition of the OFDM Signals in the UAV-RIS Ba...

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International Bhurban Conference on Applied Sciences & technology, IBCAST

作者： Adil Sheraz Muhammad Farrukh Shahid Zuhaib Ashfaq Khan Tariq Jamil. S. Khanzada Department of Computer Science FAST-NUCES Karachi Pakistan Department of Electronic and Communication Engineering (ECE) School of Architecture Technology and Engineering (ATE) University of Brighton UK Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Computer Systems Engineering Department Mehran UET Jamshoro Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

The integration of artificial intelligence (AI) and 6G networks aims to enhance agricultural applications by enabling adaptive and efficient communication systems. Fusing the potential and strengths of machine learning (ML) technology and the high-speed, low-latency communication capabilities of 6G networks, we can create intelligent farming systems that can enhance productivity, reduce resource wastage, and ensure sustainable agricultural practices. To tackle resource wastage and power efficiency of 6G-enabled devices, we present an AI-based solution that utilizes pre-trained Deep Learning (DL) models to classify Orthogonal Frequency Division Multiplexing (OFDM) modulated Quadrature Amplitude Modulation (QAM) signals. The proposed approach involves recognizing signals based on the classification results at the receiving nodes, prompting them to switch their radio modulation schemes to options such as 4-QAM, 16-QAM, and 64-QAM. Taking advantage of DL models for QAM classification and dynamically adjusting modulation schemes, the proposed solution can optimize resource utilization, improve data transmission quality, and enhance overall network performance in agricultural applications. We used pre-trained ResNet50, MobileNetV2, and a scratch CNN model. Our results show that pretrained models performed better than scratch CNN models and demonstrate the feasibility of using pretrained DL models for modulation scheme classification, offering promising opportunities for the development of adaptive and intelligent agricultural systems.

关键词： 6G mobile communication Smart agriculture Adaptation models Adaptive systems Quadrature amplitude modulation OFDM Modulation Switches Resource management Residual neural networks

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Photonic lenses with whispering gallery waves at Janus particles

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Opto-electronic Science 2022年第2期1卷 1-9页

作者： Igor V.Minin Oleg V.Minin Yinghui Cao Bing Yan Zengbo Wang Boris Luk’yanchuk Tomsk Polytechnic University 36 Lenin AvenueTomsk 634050Russia College of Computer Science and Technology Jilin University2699 Qianjin StreetChangchun 130012China School of Computer Science and Electronic Engineering Bangor UniversityDean StreetBangorGwyneddLL571UTUK Faculty of Physics Lomonosov Moscow State UniversityMoscow 119991Russia

We show that electric field on the plane surface of truncated sphere or cylinders(so called Janus particles)have sharp resonances versus the depth of removed segment of a sphere or *** resonances are related to the excited whispering gallery waves caused by *** is a new mechanism of the field *** of this effect for cylinders permits to reach a super resolution in the line thickness,which can be used for contact optical lithography.

关键词： photonics whispering gallery mode whispering gallery waves Janus particles

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A* Algorithm-Based Indoor Navigation System for Multi-Story Building

A* Algorithm-Based Indoor Navigation System for Multi-Story ...

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IEEE Global Conference on Consumer electronics (GCCE)

作者： Muhammad Shazmin Sariman Maisara Othman Sharifah Saon Abd Kadir Mahamad Herdawatie Abdul Kadir Mohd Anuaruddin Ahmadon Shingo Yamaguchi Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia Department of Computer and Information Sciences Universiti Teknologi PETRONAS Perak Malaysia Graduate School of Sciences and Technology for Innovation Yamaguchi University Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Augmented reality (AR)-based indoor guidance is gaining popularity in various industries. With AR's capability to connect to other devices and perform innovative tasks, technological firms are investing heavily in its development. At the beginning of each semester, university students often struggle to navigate the campus, relying on maps or seeking help from others. This project addresses the need for an AR-based indoor navigation app specifically for Block QB, faculty of Electrical and electronic engineering (FKEE), Universiti Tun Hussein Onn Malaysia (UTHM). The proposed method addresses a shortest path planning problem in a multi-story building in a computationally efficient way by adopting an A* algorithm. The app incorporates the A* algorithm to ensure users can find the fastest and most accurate routes. The indoor navigation app was successfully developed for Android users utilizing the 3D Unity software. It gives users with real-time, precise guidance within the three-story Block QB FKEE building.

关键词： Accuracy Three-dimensional displays Indoor navigation Buildings Software algorithms Software Sensor systems Sensors Augmented reality Smart phones

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Leveraging AI and NLP in Chatbot Development: An Experimental Study

Leveraging AI and NLP in Chatbot Development: An Experimenta...

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Frontiers of Information technology (FIT)

作者： Abdul Wahab Paracha Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Faculty of Electronic Systems Engineering University of Regina Regina Canada

In the current era of chatbots, this research delves into the advancements in AI chatbots, drawing on artificial intelligence (AI) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model’s performance indicates a considerable dependence on the dataset’s size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems.

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