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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Saucedo, Mario A.V. Patel, Akash Kanellakis, Christoforos Nikolakopoulos, George Luleå University of Technology Robotics & Ai Team Department of Computer Electrical and Space Engineering LuleåSE-97187 Sweden

ISBN: (纸本)9798350325706

This article presents a novel 2D traversability image estimation for local reactive navigation, that attributes the fusion of a novel Convolutional Neural Network (CNN) for coarse semantic segmentation on terrain roughness, with surface geometric normals. The proposed segmentation model consists of a U-Net based Encoder-Decoder architecture with a MobileNet V3 Large backbone for real-time performance. At the bottom layer, the bottleneck block commonly found in a U-Net has been enhanced with an Atrous Spatial Pyramid Pooling (ASPP) block. In addition, a SEResNet based decoder instead of the classical stacked convolution blocks of U-Net has been implemented, while a concatenation layer has been added at the output. Moreover, the development of a novel memory module to dynamically update the semantic segmentation image based on certainty heat maps is also shown. The efficacy of the proposed scheme has been evaluated in real-life environments such as indoors, outdoors and subterranean (SubT) environments on a Pioneer 3AT mobile robot. © 2023 IEEE.

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A Comprehensive Survey of Blockchain-Powered Cybersecurity- A survey 1

A Comprehensive Survey of Blockchain-Powered Cybersecurity- ...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Altrjman, Chadi Kumar, Sumit Chaudhary, Preeti Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Artificial Intelligence Engineering Dept. Ai and Robotics Institute Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty OfEngineering Kyrenia Turkey Waterloo University Canada InfoVision Pvt. Ltd. Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350338027

Text mining was used to examine the academic publications on blockchain technology and cybersecurity that were published in huge digital libraries. Automated text mining methods like topic modeling and key word extraction are utilized in this literature study to extract topics from a vast corpus of literature. This article emphasizes the cross-disciplinary character of blockchain technology in the context of cybersecurity. The outcomes also show the risks and security holes that emerge as blockchain technology matures. Nevertheless, the investigation this article also outlines research gaps in the area of computer security and recommends research directions for future work that will be necessary to create safe blockchain platforms. © 2023 IEEE.

关键词： Blockchain

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Wi-Fi Attendance System in the IoT Era

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2022年 130卷 19-29页

作者： Abidemi, Sarumi Usman Oghenetega, Omoraka Gershion Daniel, Sekyere-Asiedu Al-Turjman, Fadi Artificial Intelligence Engineering Department Research Center for AI and IoT AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey Department of Computer Information Systems Near East University Mersin 10 Nicosia Turkey

Traditionally, attendance is marked manually by students writing down their name on a paper. This whole process wastes some of lecture time and might even get lost. Maintaining the attendance of the students in an institution is a hefty task. This project aims at designing a smart attendance system that automatically monitors and manages attendance of the students in an institution efficiently. The system is developed with an Arduino ESP8266 Wi-Fi microcontroller deployed in the class room. Further, Wi-Fi communication modules was used to make convenient communication between the student’s devices e.g. phone or laptop and the ESP8266 Wi-Fi microcontroller. Database was created where the attendance submitted will be logged and saved. Internet is utilized to transfer the information collected by the micro controller to the database and from the database to the front-end web page GUI graphical user interface. The students and admin will make one-time registration and subsequently establishing a Wi-Fi connection between the student’s device and the microcontroller in the class to mark an attendance. This system will arrange the attendance in a list with a real time timestamp and save it to database, and also be accessible to the lecturer/Admin over internet any time with uses of web browser e.g. Google chrome through the front end graphic user interface (GUI). This system is useful to reduce manual method of attendance by teachers and to alleviate the spread of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Smart Robot: Artificial Intelligent Cleaning Robot with Wifi Boundary Sensing

Smart Robot: Artificial Intelligent Cleaning Robot with Wifi...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-aioT 2024

作者： Tannous, Gresmarteneanos Ame, Ibrahim Kian, Nafiseh Yousefi Al-Turjman, Fadi Mlimwa, Cliford Peter Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Research Centre for AI and IoT Nicosia Cyprus Research Centre for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Cyprus Department of Computer Engineering Cyprus International University Nicosia Cyprus

ISBN: (纸本)9783031782756

The environment surrounds our daily lives, whether we want to or not. When we have a clean environment, we can enjoy very healthy lifestyles. However, when we have dirty environments, it can cause a myriad of diseases, health risks, and worse, global pandemics like plagues. This became very apparent during the years of the COVID-19 pandemic when we had to sanitize our hands on an hourly basis after touching suspicious items that might have had human contact. This study entails a way to keep our environment clean by picking up trash and items that are left on the floor by other people. A smart robot that uses artificial intelligence to be able to not only move around and understand its surroundings, but also know if there is an object that is identified as trash and needs to be removed from that area. The smart robot is fitted with a high-definition camera for object detection and can see them from 3–4 m. From the image, it will scan the angle direction of the object, change its course, and follow it. After reaching the said location, it will begin its procedure of picking up and storing it in its trash compartment. The robot can be tuned to only clean a certain area and not cross its boundary by comparing fingerprints of registered Wi-Fi RSSIs. This method is most accurate for indoor boundary setting. For outdoors, a GPS method is available to track and bind the robot within a map circumference. Finally, all these metrics and location services can be viewed on a custom-made mobile application that complements the robot's features. What ties all this together is a cloud-based server that feeds the app data from the robot and stores it for future review. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Wi-Fi

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Multi Agent Coordination Strategy for Collaborative Exploration of GPS-denied Environments

Multi Agent Coordination Strategy for Collaborative Explorat...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Patel, Akash Kanellakis, Christoforos Nikolakopoulos, George Luleå University of Technology Robotics & Ai Team Department of Computer Electrical and Space Engineering LuleåSE-97187 Sweden

ISBN: (纸本)9798350325706

Autonomous exploration and mapping of unknown subterranean areas using fleet of Micro Aerial Vehicles (MAVs) have achieved great interest in the past decade. The application of such research direction is extended beyond mining industry for example the exploration of potential lava tubes and caves on Mars using Martian MAVs. In this article we present a Collaborative Exploration and Mapping approach for exploration of large scale multi-branched virtual cave environment using multiple MAVs. An important aspect of such approach is to establish a decent collaboration with minimal information exchange among agents such that the exploration area can be maximized by preventing multiple agents covering overlapping parts of the environment. The collaborative exploration approach in this article is based on sharing local traversable frontier points as information among agents such that the each agent can identify the target areas in order to extend global map in an efficient manner. This work proposes a novel baseline collaborative exploration approach for optimal target area selection with a resource constrained MAVs in order to efficiently as well as rapidly explore subterranean environment. Extensive simulations are performed to validate the proposed collaborative strategy in order to establish a multi agent coordinated exploration-coverage mission. © 2023 IEEE.

关键词： Coordinated Exploration Navigation Unmanned Aerial Vehicles

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Lasso Regression Model of HSV Color Space Masking Vision Based Whole Coffee Bean Processing Agtron Measurement

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Journal of Institute of Control, robotics and Systems 2024年第11期30卷 1245-1253页

作者： Lee, Ye Chan Noh, Yi Hyun Noh, Yun Seo Kim, Ju Song Ryu, Kwangyeol Kim, Byeong Hak Department of Industrial Engineering Pusan National University Korea Republic of Department of Computer Science and Engineering Pusan National University Korea Republic of AI Research Center for Manufacturing Systems Korea Institute of Industrial Technology Korea Republic of

The rapid increase in coffee consumption has led to a significant expansion in production scale and variety within the agricultural regions of the global coffee belt. Recent coffee harvested in varies specious and processed in various ways, and the functionality of processing equipment is advanced to optimize its quality. In particular, coffee roasting equipment has evolved to incorporate complex structures and advanced electronic, mechanical, and heat/air control technologies for processing harvested green coffee beans. One of the most critical quality factors in the roasting process is the degree of roast, which is measured by the bean color (Agtron) value. The conventional method for Agtron measurement involves grinding the beans and using vision sensors. However, the conventional method shows difficult and time-consuming. The method for measuring the color of whole beans without grinding is simpler, but it has limitations in accuracy due to variations in shading and background images. This study proposes a method to accurately measure the Agtron color of whole coffee beans without grinding. The proposed method employs a preprocessing technique that separates the measurement area from the background area through color histogram analysis. To enhance measurement accuracy, we suggest using a Lasso regression model, which mitigates overfitting problems. Experimental results demonstrate that the proposed method achieves an improved accuracy compared with traditional regression methods and neural network-based approaches. © ICROS 2024.

关键词： Grinding (machining)

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Data Augmentation via Face Morphing for Recognizing Intensities of Facial Emotions

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IEEE Transactions on Affective Computing 2023年第2期14卷 1228-1235页

作者： Huang, Tsung-Ren Hsu, Shin-Min Fu, Li-Chen National Taiwan University Department of Psychology Center for Artificial Intelligence & Advanced Robotics Taipei106319 Taiwan National Taiwan University Center for Artificial Intelligence & Advanced Robotics Department of Electrical Engineering Department of Computer Science and Information Engineering Taipei106319 Taiwan

Being able to recognize emotional intensity is a desirable feature for a facial emotional recognition (FER) system. However, the development of such a feature is hindered by the paucity of intensity-labeled data for model training. To ameliorate the situation, the present study proposes using face morphing as a novel way of data augmentation to synthesize faces that express different degrees of a designated emotion. Such an approach has been successfully validated on humans and machines. Specifically, humans indeed perceived different levels of intensified emotions in these parametrically synthesized faces, and FER systems based on neural networks indeed showed improved sensitivities to intensities of different emotions when additionally trained on the synthesized faces. Overall, the proposed data augmentation method is not only simple and effective but also useful for building FER systems that recognize facial expressions of mixed emotions. © 2010-2012 IEEE.

关键词： Face recognition

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Heat transfer simulation of optogenetics-LED based on microchannel heat dissipation

Heat transfer simulation of optogenetics-LED based on microc...

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2024 International Conference on Optoelectronic Information and computer engineering, OICE 2024

作者： Liu, Lusheng Le, Song Zhang, Yuan Zhang, Jing Wang, aiping Bin, Jianxiong Niu, Lan Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering & Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9781510682504

Optogenetics technology has greatly promoted the development of neuroscience. Flexible gene manipulation tools and sensitive light activation methods have provided convenience for neural function research. However, the thermal effect generated in the process of light stimulation affects the activation of neurons by optogenetic technology, which makes the results of light-activated light-sensitive proteins to generate neural currents inaccurate. Here, we established an in vivo activation model of LED-based optogenetic technology through simulation and analyzed the thermal effect produced by it. We propose to use microchannels to dissipate heat from implanted devices, reducing the thermal effect of light stimulation for neural activation. Through simulation, we propose the use of the vortex-like and cobweb-like microchannels for heat dissipation, which can control the temperature rise within 0.28K and 0.31K, respectively. © 2024 SPIE.

关键词： Heat losses

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Sub-Optimal Decentralized Navigation of Multiple Holonomic Agents in Simply-Connected Workspaces 63

Sub-Optimal Decentralized Navigation of Multiple Holonomic A...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Kotsinis, Dimitrios Bechlioulis, Charalampos P. Robotics Institute Maroussi Greece University of Patras Athena Research Center Department of Electrical and Computer Engineering Greece

ISBN: (纸本)9798350316339

In this paper, we propose a sub-optimal approach for the multi-agent navigation problem in simply-connected workspaces. We design a decentralized control law exhibiting the following three properties: (1) navigation of each agent with the optimal policy towards its destination, (2) avoidance of collision with other nearby agents and the workspace boundary, and (3) knowledge about the current position and not the destination of nearby agents. Moreover, we refer to the sub-optimal approach because the computational complexity and time needed to calculate the global optimal solution become unrealistic as the number of agents increases. In our case, each agent has a predetermined optimal policy calculated by a novel off-policy iterative method, to go towards its destination and it deviates from it in order to avoid collisions. The purpose of the simulation study is to examine how much the sub-optimal greedy trajectory of each agent deviates from the optimal one. © 2024 IEEE.

关键词： Decentralized control

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A Survey on Machine Learning Approaches in Water Analysis 13th

A Survey on Machine Learning Approaches in Water Analysis

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on ai in Applications for Achieving the Green Deal Targets, ai4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, aiai 2024

作者： Tsimpidi, Ilektra Sartjärvi, Rosa Juntunen, Petri Nikolakopoulos, George Robotics and AI Team Department of Computer Science Space and Electrical Engineering Luleå University of Technology Luleå Sweden Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9783031632266

The aim of this article is to present a survey on Machine Learning approaches for performing water analysis as in general integrating Artificial Intelligence in water analysis has a transformative potential for optimizing and sustaining water resources. Recent trends in water treatment and desalination applications effectively address pollution and scarcity challenges, while by combining ai with technologies like data analytics, water management complexities are simplified, ensuring sustainability and cost-effectiveness through predictive data assessment. This survey presents examples of the ML’s significance in water treatment and in water quality analysis, such as for enhancing accuracy in water quality index (WQI) assessments and examples of exploring and predicting pollutants in water processes, and aiding in the early detection and categorization of contaminants. Furthermore, this survey will reveal the immense potential in leveraging ML algorithms to enhance water analysis accuracy, speed, and efficiency, paving the way for reduced chemical usage and improved understanding of microbiological processes. As it will be presented, ML approaches offer valuable tools across various stages of water analysis, from identifying critical indicators to accurately measuring and predicting water parameters. The survey concludes by emphasizing the potential of ai and particularly ML to revolutionize water resource management, offering precision, efficiency, and foresight in addressing challenges of water scarcity and sustainable resource utilization. © IFIP International Federation for Information Processing 2024.

关键词： Machine learning

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