7,813 research outputs found

    Fuzzy Natural Logic in IFSA-EUSFLAT 2021

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    The present book contains five papers accepted and published in the Special Issue, “Fuzzy Natural Logic in IFSA-EUSFLAT 2021”, of the journal Mathematics (MDPI). These papers are extended versions of the contributions presented in the conference “The 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology jointly with the AGOP, IJCRS, and FQAS conferences”, which took place in Bratislava (Slovakia) from September 19 to September 24, 2021. Fuzzy Natural Logic (FNL) is a system of mathematical fuzzy logic theories that enables us to model natural language terms and rules while accounting for their inherent vagueness and allows us to reason and argue using the tools developed in them. FNL includes, among others, the theory of evaluative linguistic expressions (e.g., small, very large, etc.), the theory of fuzzy and intermediate quantifiers (e.g., most, few, many, etc.), and the theory of fuzzy/linguistic IF–THEN rules and logical inference. The papers in this Special Issue use the various aspects and concepts of FNL mentioned above and apply them to a wide range of problems both theoretically and practically oriented. This book will be of interest for researchers working in the areas of fuzzy logic, applied linguistics, generalized quantifiers, and their applications

    Soundscape in Urban Forests

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    This Special Issue of Forests explores the role of soundscapes in urban forested areas. It is comprised of 11 papers involving soundscape studies conducted in urban forests from Asia and Africa. This collection contains six research fields: (1) the ecological patterns and processes of forest soundscapes; (2) the boundary effects and perceptual topology; (3) natural soundscapes and human health; (4) the experience of multi-sensory interactions; (5) environmental behavior and cognitive disposition; and (6) soundscape resource management in forests

    Autonomous Radar-based Gait Monitoring System

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    Features related to gait are fundamental metrics of human motion [1]. Human gait has been shown to be a valuable and feasible clinical marker to determine the risk of physical and mental functional decline [2], [3]. Technologies that detect changes in people’s gait patterns, especially older adults, could support the detection, evaluation, and monitoring of parameters related to changes in mobility, cognition, and frailty. Gait assessment has the potential to be leveraged as a clinical measurement as it is not limited to a specific health care discipline and is a consistent and sensitive test [4]. A wireless technology that uses electromagnetic waves (i.e., radar) to continually measure gait parameters at home or in a hospital without a clinician’s participation has been proposed as a suitable solution [3], [5]. This approach is based on the interaction between electromagnetic waves with humans and how their bodies impact the surrounding and scattered wireless signals. Since this approach uses wireless waves, people do not need to wear or carry a device on their bodies. Additionally, an electromagnetic wave wireless sensor has no privacy issues because there is no video-based camera. This thesis presents the design and testing of a radar-based contactless system that can monitor people’s gait patterns and recognize their activities in a range of indoor environments frequently and accurately. In this thesis, the use of commercially available radars for gait monitoring is investigated, which offers opportunities to implement unobtrusive and contactless gait monitoring and activity recognition. A novel fast and easy-to-implement gait extraction algorithm that enables an individual’s spatiotemporal gait parameter extraction at each gait cycle using a single FMCW (Frequency Modulated Continuous Wave) radar is proposed. The proposed system detects changes in gait that may be the signs of changes in mobility, cognition, and frailty, particularly for older adults in individual’s homes, retirement homes and long-term care facilities retirement homes. One of the straightforward applications for gait monitoring using radars is in corridors and hallways, which are commonly available in most residential homes, retirement, and long-term care homes. However, walls in the hallway have a strong “clutter” impact, creating multipath due to the wide beam of commercially available radar antennas. The multipath reflections could result in an inaccurate gait measurement because gait extraction algorithms employ the assumption that the maximum reflected signals come from the torso of the walking person (rather than indirect reflections or multipath) [6]. To address the challenges of hallway gait monitoring, two approaches were used: (1) a novel signal processing method and (2) modifying the radar antenna using a hyperbolic lens. For the first approach, a novel algorithm based on radar signal processing, unsupervised learning, and a subject detection, association and tracking method is proposed. This proposed algorithm could be paired with any type of multiple-input multiple-output (MIMO) or single-input multiple-output (SIMO) FMCW radar to capture human gait in a highly cluttered environment without needing radar antenna alteration. The algorithm functionality was validated by capturing spatiotemporal gait values (e.g., speed, step points, step time, step length, and step count) of people walking in a hallway. The preliminary results demonstrate the promising potential of the algorithm to accurately monitor gait in hallways, which increases opportunities for its applications in institutional and home environments. For the second approach, an in-package hyperbola-based lens antenna was designed that can be integrated with a radar module package empowered by the fast and easy-to-implement gait extraction method. The system functionality was successfully validated by capturing the spatiotemporal gait values of people walking in a hallway filled with metallic cabinets. The results achieved in this work pave the way to explore the use of stand-alone radar-based sensors in long hallways for day-to-day long-term monitoring of gait parameters of older adults or other populations. The possibility of the coexistence of multiple walking subjects is high, especially in long-term care facilities where other people, including older adults, might need assistance during walking. GaitRite and wearables are not able to assess multiple people’s gait at the same time using only one device [7], [8]. In this thesis, a novel radar-based algorithm is proposed that is capable of tracking multiple people or extracting walking speed of a participant with the coexistence of other people. To address the problem of tracking and monitoring multiple walking people in a cluttered environment, a novel iterative framework based on unsupervised learning and advanced signal processing was developed and tested to analyze the reflected radio signals and extract walking movements and trajectories in a hallway environment. Advanced algorithms were developed to remove multipath effects or ghosts created due to the interaction between walking subjects and stationary objects, to identify and separate reflected signals of two participants walking at a close distance, and to track multiple subjects over time. This method allows the extraction of walking speed in multiple closely-spaced subjects simultaneously, which is distinct from previous approaches where the speed of only one subject was obtained. The proposed multiple-people gait monitoring was assessed with 22 participants who participated in a bedrest (BR) study conducted at McGill University Health Centre (MUHC). The system functionality also was assessed for in-home applications. In this regard, a cloud-based system is proposed for non-contact, real-time recognition and monitoring of physical activities and walking periods within a domestic environment. The proposed system employs standalone Internet of Things (IoT)-based millimeter wave radar devices and deep learning models to enable autonomous, free-living activity recognition and gait analysis. Range-Doppler maps generated from a dataset of real-life in-home activities are used to train deep learning models. The performance of several deep learning models was evaluated based on accuracy and prediction time, with the gated recurrent network (GRU) model selected for real-time deployment due to its balance of speed and accuracy compared to 2D Convolutional Neural Network Long Short-Term Memory (2D-CNNLSTM) and Long Short-Term Memory (LSTM) models. In addition to recognizing and differentiating various activities and walking periods, the system also records the subject’s activity level over time, washroom use frequency, sleep/sedentary/active/out-of-home durations, current state, and gait parameters. Importantly, the system maintains privacy by not requiring the subject to wear or carry any additional devices

    Line-of-Sight Detection for 5G Wireless Channels

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    With the rapid deployment of 5G wireless networks across the globe, precise positioning has become essential for many vertical industries reliant on 5G. The predominantly non-line-of-sight (NLOS) propagation instigated by the obstacles in the surrounding environment, especially in metro city areas, has made it particularly difficult to achieve high estimation accuracy for positioning algorithms that necessitate direct line-of-sight (LOS) transmission. In this scenario, correctly identifying the line-of-sight condition has become extremely crucial in precise positioning algorithms based on 5G. Even though numerous scientific studies have been conducted on LOS identification in the existing literature, most of these research works are based on either ultra-wideband or Wi-Fi networks. Therefore, this thesis focuses on this hitherto less investigated area of line-of-sight detection for 5G wireless channels. This thesis examines the feasibility of LOS detection using three widely used channel models, the Tapped Delay Line (TDL), the Clustered Delay Line (CDL), and the Winner II channel models. The 5G-based simulation environment was constructed with standard parameters based on 3GPP specifications using MATLAB computational platform for the research. LOS and NLOS channels were defined to transmit random signal samples for each channel model where the received signal was subjected to Additive White Gaussian Noise (AWGN), imitating the authentic propagation environment. Variable channel conditions were simulated by randomly alternating the signal-to-noise ratio (SNR) of the received signal. The research mainly focuses on machine learning (ML) based LOS classification. Additionally, the threshold-based hypothesis was also deployed for the same scenarios as a benchmark. The main objectives of the thesis were to find the statistical features or the combination of statistical features of the channel impulse response (CIR) of the received signal, which provide the best results and to identify the most effective machine learning method for LOS/NLOS classification. Furthermore, the results were verified through actual measurement samples obtained during the NewSense project. The results indicate that the time-correlation feature of the channel impulse response used in isolation would be effective in LOS identification for 5G wireless channels. Additional derived features of the CIR do not significantly increase the classification accuracy. Positioning Reference Signals (PRS) were found to be more appropriate than Sounding Reference Signals (SRS) for LOS/NLOS classification. The study reinforced the significance of selecting the most suitable machine learning algorithm and kernel function as relevant for the task of obtaining the best results. The medium Gaussian support vector machines ML algorithm provided the overall highest precision in LOS classification for simulated data with up to 98% accuracy for the Winner II channel model with PRS. The machine learning algorithms proved to be considerably more effective than conventional threshold-based detection for both simulated and real measurement data. Additionally, the Winner II model with the richest features presented the best results compared with CDL and TDL channel models

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Quantifying the Impact of Label Noise on Federated Learning

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    Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets. While existing studies focus on FL algorithm development to tackle data heterogeneity across clients, the important issue of data quality (e.g., label noise) in FL is overlooked. This paper aims to fill this gap by providing a quantitative study on the impact of label noise on FL. We derive an upper bound for the generalization error that is linear in the clients' label noise level. Then we conduct experiments on MNIST and CIFAR-10 datasets using various FL algorithms. Our empirical results show that the global model accuracy linearly decreases as the noise level increases, which is consistent with our theoretical analysis. We further find that label noise slows down the convergence of FL training, and the global model tends to overfit when the noise level is high.Comment: Accepted by The AAAI 2023 Workshop on Representation Learning for Responsible Human-Centric A

    Analysing the Movement and Behaviour of Housed Dairy Cows

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    Cows in modern dairy systems are at risk of comprised health and welfare, and monitoring changes in behaviour can help identify early-warning signs. This thesis uses a local positioning system to detect changes in group-level behaviour. The proximity interaction network structure and consistency of a herd housed in a closed barn on a commercial farm in Essex is explored. Next, the network structure, alongside group-level space-use patterns, on the commercial farm in Essex are compared to those of a second dairy cow herd housed in an open barn (RVC Research farm). In the subsequent chapters, the relationship between barn temperature and bunching behaviour, a potentially maladaptive response to warmer than average temperatures, was investigated in both herds, through various bunching metrics: range size, inter-cow distance and nearest neighbour distance. The herd on the commercial farm in Essex was highly connected and temporally unstable, with inter-individual variation in interactions in the non-feeding zone, and social differentiation across functional zones. No social assortment by parity, days in milk or lameness state was detected. The herd on the RVC Research farm were less connected than the herd on the commercial farm in Essex. Inter-individual variation in proximity interactions was found in the feeding zoneof the RVC Research farm, alongside social differentiation across functional zones. Cows showed preferences for specific areas of the non-feeding zones, more so on the commercial farm in Essex than on the RVC Research farm. Cows increased their bunching behaviour ≥ 20°C in terms of all bunching metrics on the commercial farm in Essex. This pattern was observed for nearest neighbour distance on the RVC Research farm ≥ 15.91°C. This thesis demonstrates the use of precision livestock farming to monitor changes in group-level behaviour to improve the health and welfare of livestock

    Adaptive vehicular networking with Deep Learning

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    Vehicular networks have been identified as a key enabler for future smart traffic applications aiming to improve on-road safety, increase road traffic efficiency, or provide advanced infotainment services to improve on-board comfort. However, the requirements of smart traffic applications also place demands on vehicular networks’ quality in terms of high data rates, low latency, and reliability, while simultaneously meeting the challenges of sustainability, green network development goals and energy efficiency. The advances in vehicular communication technologies combined with the peculiar characteristics of vehicular networks have brought challenges to traditional networking solutions designed around fixed parameters using complex mathematical optimisation. These challenges necessitate greater intelligence to be embedded in vehicular networks to realise adaptive network optimisation. As such, one promising solution is the use of Machine Learning (ML) algorithms to extract hidden patterns from collected data thus formulating adaptive network optimisation solutions with strong generalisation capabilities. In this thesis, an overview of the underlying technologies, applications, and characteristics of vehicular networks is presented, followed by the motivation of using ML and a general introduction of ML background. Additionally, a literature review of ML applications in vehicular networks is also presented drawing on the state-of-the-art of ML technology adoption. Three key challenging research topics have been identified centred around network optimisation and ML deployment aspects. The first research question and contribution focus on mobile Handover (HO) optimisation as vehicles pass between base stations; a Deep Reinforcement Learning (DRL) handover algorithm is proposed and evaluated against the currently deployed method. Simulation results suggest that the proposed algorithm can guarantee optimal HO decision in a realistic simulation setup. The second contribution explores distributed radio resource management optimisation. Two versions of a Federated Learning (FL) enhanced DRL algorithm are proposed and evaluated against other state-of-the-art ML solutions. Simulation results suggest that the proposed solution outperformed other benchmarks in overall resource utilisation efficiency, especially in generalisation scenarios. The third contribution looks at energy efficiency optimisation on the network side considering a backdrop of sustainability and green networking. A cell switching algorithm was developed based on a Graph Neural Network (GNN) model and the proposed energy efficiency scheme is able to achieve almost 95% of the metric normalised energy efficiency compared against the “ideal” optimal energy efficiency benchmark and is capable of being applied in many more general network configurations compared with the state-of-the-art ML benchmark

    Architecture and Advanced Electronics Pathways Toward Highly Adaptive Energy- Efficient Computing

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    With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose to revolutionize computing electronics by realizing embedded optical waveguides for onboard networking and wireless chip-to-chip links at 200-GHz carrier frequency connecting neighboring boards in a rack. The control of novel rate-adaptive optical and mm-wave transceivers needs tight interlinking with the system software for runtime resource management

    Advanced Nanomaterials for Electrochemical Energy Conversion and Storage

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    This book focuses on advanced nanomaterials for energy conversion and storage, covering their design, synthesis, properties and applications in various fields. Developing advanced nanomaterials for high-performance and low-cost energy conversion and storage devices and technologies is of great significance in order to solve the issues of energy crisis and environmental pollution. In this book, various advanced nanomaterials for batteries, capacitors, electrocatalysis, nanogenerators, and magnetic nanomaterials are presente
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