Applications of Information-Centric Networking (ICN) technology to future internet of things (IoT) and distributed edge/fog computing are widely discussed in various research committees. In this paper, we demonstrate ...
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ISBN:
(纸本)9781728170022
Applications of Information-Centric Networking (ICN) technology to future internet of things (IoT) and distributed edge/fog computing are widely discussed in various research committees. In this paper, we demonstrate a real-time video streaming scenario using CeforeSim, an NS-3 based ICN simulator. CeforeSim is based on Cefore, an open-source implementation of ICN, which is compliant with the CCNx packet format standardized by the IRTF ICN Research Group (ICNRG). The virtual interfaces provisioned in CeforeSim expedite seamless interaction between the simulated nodes and physical nodes that run the Cefore applications, thereby affording performance evaluations in various scenarios, such as handover of mobile nodes, large-scale sensor networks, and distributed edge/fog computing with the real environments.
In a world where nearly everything we do depends on sight; it is ever more challenging for the unsighted to cope with it and lead a normal life without being reliant on the presence of a companion. Finding a mechanism...
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In a world where nearly everything we do depends on sight; it is ever more challenging for the unsighted to cope with it and lead a normal life without being reliant on the presence of a companion. Finding a mechanism to convey visual information to the brain is the most difficult part of the research as well as the development of efficient sensory replacement and augmentation systems. In this paper, we aim to propose Brain Sight as an innovative system to help blind people identifying and discovering things in front of them using vibro-tactile display for vision sensory substitution. Brain Sight is a wearable vest-like fabric containing small vibration motors that displays pulses to map the seen images on the Lower-back of the visually impaired. A deep labeling segmentation and edge detection techniques are applied to the captured image after preprocessing. A simple prototype is implemented and tested with volunteers. Thanks to neuroplasticity and the cognitive cortex inside humans' brain, the implemented vest showed promising results. Without the need for intensive training, Brain Sight enables blind to recognize different shapes and numbers.
Accurately detecting the mobile contexts of public transport vehicles and their passengers is a key requirement of intelligent context-aware services in such systems. A prominent example is in-vehicle presence detecti...
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ISBN:
(纸本)9781665416214
Accurately detecting the mobile contexts of public transport vehicles and their passengers is a key requirement of intelligent context-aware services in such systems. A prominent example is in-vehicle presence detection which can be used to provide various services such as automated ticketing, dynamic vehicle distribution, and live route optimization. To use such services in practice, in-vehicle presence detection needs to be close to infallible. However, most existing solutions in this field suffer from low spatiotemporal accuracy. To address this challenge, we introduce ATARAXIS in this paper-an approach to hardwareless in-vehicle presence detection. In particular, we develop a deep convolutional neural network that can be trained to detect, if a user is inside a public transportation vehicle such as a tram, subway, or bus, from the raw sensor events generated by the sensors in a single ordinary smartphone. We show that this information can be used to infer the in-vehicle presence of users over time when combined with other sources such as the GPS trace of the user and that of the public transport vehicles. ATARAXIS has the capability to distinguish between the four user modes: driving a car, riding a bike, walking, and using public transport with an accuracy of 98.69%. This is higher than the accuracy of existing techniques for transport mode detection. We also made experiments on the battery consumption and CPU overhead. The results show that ATARAXIS incurs a negligible computational overhead and power consumption on smartphones, even though we base our approach on sensor data collection and a deep learning model.
Motion sensing technology is widely used in healthcare, sports, consumer electronics, etc. On the other hand, Artificial intelligence (AI) enables the development of wearable sensors that can recognize and analyze hum...
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ISBN:
(数字)9798350375213
ISBN:
(纸本)9798350375220
Motion sensing technology is widely used in healthcare, sports, consumer electronics, etc. On the other hand, Artificial intelligence (AI) enables the development of wearable sensors that can recognize and analyze human motions. In this study, we developed a wearable wireless motion sensing system with AI-embedded inertial measurement units (AIMUs), combined with visual analysis, to evaluate and optimize athletes’ performance in sports such as gymnastics. In the experiment, a gymnast performed an entire vaulting routine while wearing 11 AIMUs, and the motion data were transmitted to a cloud server through Bluetooth gateways. This system can achieve the segmentation of vaulting phases and the evaluation of detailed movements. The experimental results showed a 4.57% estimation error in flight height. This AI-embedded motion sensor system has the potential to provide an intuitive and easy-to-understand way to present athlete performance to coaches and athletes themselves. Furthermore, its continued development could assist athletes’ training, provide quantitative sports performance indicators, significantly improve elite athletes’ training efficiency, and monitor their health regularly.
The game development industry is among the leading industries globally, and in 2020, gaming emerged as a popular entertainment activity upon the COVID-19 outbreak. Thus, competition among gaming companies is high. Hen...
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Addressing the challenge of roadside litter in the United States, which has traditionally relied on costly and ineffective manual cleanup methods 1 1 https://***/***, this paper presents an autonomous multi-robot syst...
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ISBN:
(数字)9798350307566
ISBN:
(纸本)9798350307573
Addressing the challenge of roadside litter in the United States, which has traditionally relied on costly and ineffective manual cleanup methods
1 1
https://***/***, this paper presents an autonomous multi-robot system for highway litter monitoring and collection. Our solution integrates an aerial vehicle to scan and gather data across highway stretches with a terrestrial robot equipped with a Convolutional Neural Network (CNN) for litter detection and mapping. Upon detecting litter, the ground robot navigates to each pinpointed location, re-assesses the vicinity, and employs a “greedy pickup” approach to address potential mapping inaccuracies or litter misplacements. Through simulation studies and real-world robotic trials, this work highlights the potential of our proposed system for highway cleanliness and management in the context of Robotics, Automation, and Artificial Intelligence.
Public Blockchains (BC) in support of Smart Contracts (SC), e.g., Ethereum, enable everyone to coordinate in a decentralized model to manage scarce and valuable resources,e.g., cryptocurrencies. Such BCs allow for the...
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ISBN:
(纸本)9781665435789
Public Blockchains (BC) in support of Smart Contracts (SC), e.g., Ethereum, enable everyone to coordinate in a decentralized model to manage scarce and valuable resources,e.g., cryptocurrencies. Such BCs allow for the building of SCs that own resources and manage a set of permissions describing who is allowed to interact with these resources and what actions they can apply to them. However, Programming Languages (PL) and run-time systems used in current BCs lack a secure, flexible, and straightforward way to implement permissions within their SCs, leading to erroneous implementations that allow unauthorized access. The best-known incident related to a permission problem was the "Parity Hack", which led to the "loss" of tokens, valued at approximately 31 M USD. A better and secure SC access control concept provides an improved path to managing permissions. Thus, this paper presents a novel concept for handling permissions compatible with functional SC languages leveraging opaque and substructural data types to provide capability-based permission management. The opaque data types enforce that only designated functions can create permission carrying capabilities. Substructural data types prevent an unpermitted duplication of capabilities.
Over the past few years, the adoption of distributed generation (DG) units at low and medium voltage levels in distribution networks has surged globally. DG units offer numerous advantages to electric power systems, i...
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ISBN:
(数字)9798350354171
ISBN:
(纸本)9798350354188
Over the past few years, the adoption of distributed generation (DG) units at low and medium voltage levels in distribution networks has surged globally. DG units offer numerous advantages to electric power systems, including decreased transmission and distribution (T&D) losses, absence of emissions, enhanced efficiency, and improved reliability. The interconnection of DGs with the power system Mains is a necessary for their mutual benefits. The interconnection also brings several negative impacts such as power quality deterioration and unintentional loss of Mains. Loss of Mains is the state of power distribution system in which a DG powers a location though the Mains power or grid is not available. Loss of Mains poses a significant risk to utility workers, who might be unaware that a circuit remains energized, potentially leading to dangerous situations. Additionally, it can hinder the automatic re-connection of devices, disrupting the continuity of power supply. Identification of loss of Mains condition need to be done under 2 seconds according to ieee standard 1547. An assessment of a hybrid method for the identification of loss of Mains in DGs is taken into account for analysis. The performance evaluation of the proposed scheme is carried out using MATLAB/SIMULINK 2024a software. In this proposed method, over/under voltage measurement is used as passive method. The active method uses sending second order harmonics into the PCC (Point of Common Coupling) and measuring the voltage unbalance level or the presence of negative sequence components in the voltage. Results are shown to validate the method.
Time-Sensitive Networking (TSN) is becoming increasingly important. Especially in the field of industrial applications, the demand for uniform, converged real-time networks is continuously increasing. Furthermore, the...
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ISBN:
(数字)9798350319347
ISBN:
(纸本)9798350319354
Time-Sensitive Networking (TSN) is becoming increasingly important. Especially in the field of industrial applications, the demand for uniform, converged real-time networks is continuously increasing. Furthermore, the request to integrate wireless, mobile, and real-time capable network elements is getting more and more relevant to industrial automation use cases. To address these requests, the 3rd Generation Partnership Project (3GPP) has extended their specifications for mobile telecommunication protocols by descriptions to integrate 5G mobile networks into TSN starting from Release 16 onwards. While the specifications provide a good theoretical overview, there is still a lack of real implementations or even proof of concepts. Therefore, we started an implementation of a 5G network that is ready to be integrated into existing TSN. This work gives an overview of the current work in progress, mainly focusing on the implementation of the TSN Application Function (TSN AF) and the time synchronization features within the TSN Translators (DS-TT and NW-TT). It also shows current limitations and difficulties and how we have overcome them with our setup.
Respiratory monitoring has important applications in healthcare by enabling early disease detection. However, designing humidity sensors for integration into smart masks poses unique challenges. The sensor must have f...
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ISBN:
(数字)9798350375213
ISBN:
(纸本)9798350375220
Respiratory monitoring has important applications in healthcare by enabling early disease detection. However, designing humidity sensors for integration into smart masks poses unique challenges. The sensor must have fast response and recovery times faster than normal breathing rates. It must also maintain stability when exposed to atmospheric conditions. In this study, we developed gold nanoparticle-based humidity sensors for smart masks. We tested the sensors’ I–V curves under varying humidity levels. Remarkably, the sensors achieved the fastest response time of 0.2 seconds and recovery unaffected by ambient airflow. When integrated into smart masks, the sensors successfully discriminated and recorded different breathing patterns, including oral, nasal, deep, fast, breath-hold, cough, and restorative signals. Analysis of the normalized amplitudes and frequencies of patterns using continuous wavelet transform revealed comprehensive respiratory monitoring capabilities. Overall, this research provides a stable, low-cost smart mask platform for respiratory monitoring. Moving forward, such sensors show promise for non-invasive disease screening and personalized respiratory care.
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