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2nd International Conference on Engineering and Technology for Sustainable Development, ICET4SD 2017

作者： Prasetio, Barlian Henryranu Syauqy, Dahnial Maulana, Rizal Setyawan, Gembong Edhi System and Robotics Lab Faculty of Computer Science University of Brawijaya Indonesia

In this paper, we implement a voice data communications between sender and receiver through Wi-Fi. The system was designed and implemented using network stream protocol which was developed by National Instruments on Lab-VIEW between two devices. The device which acts as the sender was My-RIO board and the receiver device was Personal computer. The voice data communication system was tested by observing the effect of various buffer sizes on the throughput, latency and CPU usage. The result showed that the larger the buffer size being used, the throughput values were also getting bigger. However, particular buffer size made the throughput became saturated. The change on the buffer size also directly affects CPU usage. It seemed that the buffer size did not affect latency significantly. It showed that the latency was stable with an average of 13 milliseconds on voice data transmission. © The Authors, published by EDP sciences, 2018.

关键词： Convolutional codes

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A Data logger for educational purposes of a laboratory chemical reactor: an IoT approach

A Data logger for educational purposes of a laboratory chemi...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： José Lima Thadeu Brito Olga Ferreira Maria J. Afonso Vítor H. Pinto José A. Carvalho Paulo Costa Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Bragança Bragança Portugal Laboratório Para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragança Bragança Portugal Centre for Robotic in Industry and Intelligent Systems Institute for Systems and Computer Engineering Technology and Science (INESC-TEC) Porto Portugal CeDRI SusTEC INESC-TEC Faculty of Engineering of University of Porto (FEUP) Porto Portugal SusTEC ESTiG Instituto Politécnico de Bragança Bragança Portugal ARISE & ECE Dept. Research Center for Systems and Technologies (SYSTEC) FEUP CeDRI SusTEC INESC-TEC FEUP

This paper presents the development of an acquisition system and data logger from an existing set of three continuous stirred-tank reactors in series. The reactors are currently used in chemical engineering educational laboratories to perform kinetic and tracer experiments. In this sense, to accomplish the store data process, the volumetric flow rate and the concentration of tracer, reactants and/or products of the reaction must be acquired as a function of time. In the original experimental setup, only the signal conditioning system was operational, while the acquisition, visualization, and control systems were obsolete and damaged. Thus, a new system composed of an interface and real-time acquisition data is proposed alongside preserving the main reactor structure. A graphical user interface and the automation of the various actuators were developed based on worldwide usage and low cost, respectively. This system, based on a common 8-bit microcontroller and an application developed in Lazarus, allows the storage of the acquired data in a time-series database. In this way, students can analyze the results later or in real time. Moreover, remote access allows controlling the reactor and getting data by the Internet of Things (IoT) resources. Additionally, the proposed system using IoT allows data to be shared with the community as a dataset.

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Parameter Estimation of Nonlinearly Parameterized Regressions: Application to System Identification and Adaptive Control

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IFAC-PapersOnLine 2020年第2期53卷 1206-1212页

作者： Romeo Ortega Vladislav Gromov Emmanuel Nuño Anton Pyrkin Jose Guadalupe Romero Laboratoire des Signaux et Systémes Supélec CNRS-SUPELEC Gif-sur-Yvette France Faculty of Control Systems and Robotics ITMO University Saint Petersburg Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Department of Computer Science CUCEI University of Guadalajara Guadalajara Mexico Department of Digital Systems ITAM Mexico City Mexico

We propose a solution to the problem of parameter estimation of nonlinearly parameterized regressions—continuous or discrete time—and apply it for system identification and adaptive control. We restrict our attention to parameterizations that can be factorized as the product of two functions, a measurable one and a nonlinear function of the parameters to be estimated. Another feature of the proposed estimator is that parameter convergence is ensured without a persistency of excitation assumption. It is assumed that, after a coordinate change, some of the elements of the transformed function satisfy a monotonicity condition. The proposed estimators are applied to design identifiers and adaptive controllers for nonlinearly parameterized systems, which are traditionally tackled using overparameterization and assuming persistency of excitation.

关键词： Adaptive control systems identification nonlinear control

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CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis

arXiv

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arXiv 2025年

作者： Baumann, Alexander Ayala, Leonardo Seidlitz, Silvia Sellner, Jan Studier-Fischer, Alexander Özdemir, Berkin Maier-Hein, Lena Ilic, Slobodan Siemens AG Munich Germany Heidelberg Germany Medical Faculty Faculty of Mathematics and Computer Science Heidelberg University Germany NCT Heidelberg Germany HIDSS4Health Heidelberg Germany Department of Urology and Urosurgery University Medical Center Mannheim Germany Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Germany Division of Intelligent Systems and Robotics in Urology DKFZ Heidelberg Germany DKFZ Hector Cancer Institute University Medical Center Mannheim Germany

Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding, and is already established as a critical modality in remote sensing. However, variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies, leading to camera-specific models with limited generalizability and inadequate cross-camera applicability. To address this bottleneck, we introduce CARL, a model for Camera-Agnostic Representation Learning across RGB, multispectral, and hyperspectral imaging modalities. To enable the conversion of a spectral image with any channel dimensionality to a camera-agnostic embedding, we introduce wavelength positional encoding and a self-attention-cross-attention mechanism to compress spectral information into learned query representations. Spectral-spatial pre-training is achieved with a novel spectral self-supervised JEPA-inspired strategy tailored to CARL. Large-scale experiments across the domains of medical imaging, autonomous driving, and satellite imaging demonstrate our model’s unique robustness to spectral heterogeneity, outperforming on datasets with simulated and real-world cross-camera spectral variations. The scalability and versatility of the proposed approach position our model as a backbone for future spectral foundation models. © 2025, CC BY.

关键词： Image coding

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Deep Learning Techniques for Future Intelligent Cross-Media Retrieval

arXiv

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arXiv 2020年

作者： ur Rehman, Sadaqat Waqas, Muhammad Tu, Shanshan Koubaa, Anis ur Rehman, Obaid Ahmad, Jawad Hanif, Muhammad Han, Zhu Department of Computer Science Namal Institute - an associated college of University of Bradford United Kingdom Faculty of Information Technology Beijing University of Technology Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi23460 Pakistan Robotics and Internet-of-Things research lab Department of Computer Science Prince Sultan University R&D Gai-tech Robotics China CISTER/INESC TEC and ISEP-IPP Porto Portugal Department of EE Sarhad University of Science and IT Pakistan Department of Computer Science Edinburgh Napier University United Kingdom Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention. It plays a significant role in big data applications and consists in searching and finding data from different types of media. In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation. These challenges are evaluated on deep learning (DL) based methods, which are categorized into four main groups: 1) unsupervised methods, 2) supervised methods, 3) pairwise based methods, and 4) rank based methods. Then, we present some well-known cross-media datasets used for retrieval, considering the importance of these datasets in the context in of deep learning based cross-media retrieval approaches. Moreover, we also present an extensive review of the state-of-the-art problems and its corresponding solutions for encouraging deep learning in cross-media retrieval. The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems and the potential solutions of deep learning assisted cross-media retrieval. To the best of our knowledge, this is the first comprehensive survey to address cross-media retrieval under deep learning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Implementation of IoT Concept for Early Diagnostic of Subacute Rumen Acidosis in Cows

Implementation of IoT Concept for Early Diagnostic of Subacu...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Anatolijs Zabasta Nadezda Kunicina Uldis Grunde Janis Judvaitis Ilga Sematovica Institute of Industrial Electronics and Electrical Engineering Riga Technical University Riga Latvia Robotics and Machine Perception laboratory Institute of Electronics and Computer Science Riga Latvia Cyber-Physical Systems laboratory Institute of Electronics and Computer Science Riga Latvia Faculty of Veterinary Medicine of Latvia University of Life Sciences and Technologies Jelgava Latvia

ISBN: (数字)9781728169491

ISBN: (纸本)9781728169507

Highly productive dairy cow' ration under intensive production conditions causes the development of subacute rumen acidosis (SARA). In this paper we discuss a reticulo-ruminal long-acting cyber-physical diagnostic system' prototype, which applies Internet of Things (IoT) for monitoring rumen parameters of cows. The new diagnostic system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes gateways for data collection from boluses, an MQTT (Message Queuing Telemetry Transport) broker, a web server and a database. The diagnostic system' prototype provides timely data on cow health status to the users, so they can evaluate the cow' health status and implement further actions. Therefore, the use of diagnostic system provides opportunity to increase cow productivity, longevity and to save maintenance cost of milk farms. The results of the first stage of the research are discussed in this paper.

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Detecting layered structures of partially occluded objects for bin picking

Detecting layered structures of partially occluded objects f...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yusuke Inagaki Ryosuke Araki Takayoshi Yamashita Hironobu Fujiyoshi Dept. of Robotics Science and Technology Grad Chubu Univ Aichi JP Dept. of Computer Science Grad Chubu Univ Aichi JP Faculty of Computer Science Chubu Univ Aichi JP Faculty of Robotics Science and Technology Chubu Univ Aichi JP

When robots engage in bin picking of multiple objects, a failure in grasping partially occluded objects may occur because other objects may overlap the desired ones. Therefore, the layered structure of objects needs to be detected, and the picking order needs to be established. In this paper, we propose a new dataset that evaluates not only the area of objects but also the layered structures of objects. In this dataset, three tasks are targeted: object detection, semantic segmentation, and segmentation of occluded areas for bin picking of multiple objects. The dataset, called “the Amazon robotics Challenge (ARC) Multi-task Dataset” contains 1,500 RGB images and depth images, including all scenes containing bounding box labels, semantic segmentation labels, and occluded area labels. This enables representing the layered structure of overlapped objects with a tree structure. A benchmark of the ARC multi-task dataset demonstrated that occluded areas could be segmented using a Mask regional convolutional neural network (R-CNN) and that layered structures of objects could be predicted. Our dataset is available at the following URL:http://***/research/arc_dataset_2017_e.

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Assisted Living System Architecture Based on Bluetooth low Energy and Wearable Sensors

Assisted Living System Architecture Based on Bluetooth low E...

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International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH)

作者： Marcin Kajor Dominik Grochala Michał Grochala Marek Iwaniec Justyna Lemejda Faculty of Electical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Kraków Poland Nordic Semiconductor Poland sp. z o.o. Kraków Poland Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Kraków Poland Department of Prosthodontics Jagiellonian University Kraków Poland

ISBN: (数字)9781728171807

ISBN: (纸本)9781728171814

The design of wearable sensors and development of novel wireless radio protocols drive advances in the assisted living technology which is a promising approach in terms of future domestic healthcare. In this paper we propose the system architecture for vital signs monitoring in home conditions. The prototype device is based on the microcontroller supporting Bluetooth Low Energy which may be used for medical data transfer in assisted living applications. We developed a respiration rate sensor and thermometer pickup to provide basic measurements of physiological signals. We tested a range of proposed wireless system in urban conditions. The temperature and respiration rate was then recorded to verify the system operation. We measured the power consumption to evaluate the low energy performance of the device which is vital aspect of the telemedical wearable system.

关键词： assisted living systems Bluetooth Low Energy wearable sensors telemedicine Wearable sensors Telerehabilitation Building systems Operating systems LOW ENERGY Bluetooth CARRIER SYSTEM Organism Power consumption Respiratory Rate

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PettingZoo: Gym for Multi-Agent Reinforcement Learning

arXiv

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arXiv 2020年

作者： Terry, Justin K. Black, Benjamin Grammel, Nathaniel Jayakumar, Mario Hari, Ananth Sullivan, Ryan Santos, Luis Perez, Rodrigo Horsch, Caroline Dieffendahl, Clemens Williams, Niall L. Lokesh, Yashas Ravi, Praveen Swarm Labs Department of Computer Science University of Maryland College Park United States Department of Electrical and Computer Engineering University of Maryland College Park United States Department of Mechanical Engineering University of Maryland College Park United States Maryland Robotics Center University of Maryland College Park United States Faculty of Electrical Engineering and Computer Science Technical University of Berlin

This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model. PettingZoo is a library of diverse sets of multi-agent environments with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Reinforcement Learning ("MARL"), by making work more interchangeable, accessible and reproducible akin to what OpenAI's Gym library did for single-agent reinforcement learning. PettingZoo's API, while inheriting many features of Gym, is unique amongst MARL APIs in that it's based around the novel AEC games model. We argue, in part through case studies on major problems in popular MARL environments, that the popular game models are poor conceptual models of games commonly used in MARL and accordingly can promote confusing bugs that are hard to detect, and that the AEC games model addresses these problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Fertilizers

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Study of maximum electric power consumption to produce hydrogen from urine and normal water

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AIP Conference Proceedings 2023年第1期2845卷

作者： Bhishma Karki Saddam Husain Dhobi Jeevan Jyoti Nakarmi Digvijay Pandey Sahar R. Abdul Kadeem Ahmed J. Obaid Trichandra Multiple Campus Tribhuvan University Kathmandu Nepal Patan Multiple Campus Lalitpur-44600 Tribhuvan University Kathmandu Nepal Robotics Academy of Nepal Lalitpur-44700 Nepal A.P.J. Abdul Kalam Technical University Uttar Pradesh India National University of Science and Technology Dhi Qar Iraq Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq

The production of hydrogen from urine as an electrolyte is about 6 to 9times faster than water as electrolyte measured at constant voltage 10V, 15V, 19V, 23V, 27V, and 30V per minute. The power consumption for urine electrolytes is higher than water-electrolyte was observed. Such a rate producesan increase in the efficiency of hydrogen production which is future energy. The utilization of urine rather than water as an electrolyte control soil and water pollution. Urine electrolyte has more beneficial than water because water-electrolyte needed chemical (acid or base) to increase the efficiency of hydrogen production, have to buy and transport while but urine electrolyte has no such problem.

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