Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Partial Shading Effect on the Crystalline Silicon Photovoltaic Module Temperature Distribution

The electrical mismatch occurs in the serially connected solar cells under the partially shaded photovoltaic (PV) modules due to non-uniform illumination. The mismatch leads to a non-uniform distribution of temperature in the PV module, which can have a detrimental effect on long-term reliability and instantaneous output power. The present research aims to study temperature distribution under the active and inactive states of bypass diode in a partially shaded PV module. The distribution of temperature in a shaded PV module is estimated using the proposed electro-thermal PSPICE model. The temperature distribution has been estimated and analyzed for an increasing number of shaded cells and their shaded area. The proposed model is validated with the experiment. Results show that the temperature gradient and its distribution in a shaded PV module changes in the transition of bypass diode state from inactive to active. This change in temperature gradient and its distribution has been quantified in terms of the thermal shade effect factor. A thermal shade effect factor value less than −1 indicates the temperature gradient amplification during the active bypass diode state. This amplification would cause the temperature of the shaded cell to rise manifold. On the other hand, a thermal shade effect factor value greater than −1 indicates the higher temperature gradient during the inactive bypass diode state.
A Deep Learning-Based Detector for IM-MIMO-OFDM

Deep learning (DL) is playing an increasingly important role in the design of next-generation communication systems. In this paper, we apply DL algorithms to enhance signal detection and performance of multiple-input-multiple-output (MIMO) based orthogonal frequency-division multiplexing (OFDM) systems with index modulation (IM). The proposed detector termed DLIM is used as fully connected layers of a deep neural network (DNN) and adopted to achieve minimum bit error rates in IM-MIMO-OFDM over Rayleigh wireless channels. To show the enhancement of the proposed algorithm, the DL model is trained initially offline using data generated from simulation based on common statistical wireless channel models. DLIM is then adopted to recover the online transmitted data. Simulation results confirm that the proposed DLIM can detect the transmitted symbols with a performance comparable to near-optimal BER in a shorter runtime than required by the existing classical detectors.
Slow-Time Waveform Randomization Performance under Incoherent FMCW Radar Interference

With the rising utilization of radars on vehicles, interference among vehicular radars in dense traffic scenarios is an emerging problem awaiting innovative solutions. In this work, we consider incoherent FMCW radar interference cases where different FMCW radars use different chirp slopes. Our simulation shows that under the simplest spectrum access scheme - random frequency division multiplexing (RFDM), the false alarm caused by incoherent interference is a serious issue. We study the interference mitigation performance of three slow-time waveform randomization schemes: frequency hopping (FH), joint RFDM and phase coding (PC), and joint FH and PC. Our comparative study shows that not all slow-time waveform randomization schemes perform well under incoherent interference. FH even achieves worse false alarm performance than RFDM. Joint RFDM and PC achieve the best trade-off between the probability of false alarm and the probability of target detection.
Expression Tree-based Policy Conflict Detection Algorithm

In attribute-based access control services, there are problems such as cumbersome policy control, prone to authorization conflicts, and conflicts caused by complex attribute structures are not easy to identify. Due to the difficulty of the conflict detection problem, most of the detection methods have strict requirements for policy structure. Normally, the priority strategy are chosen uniformly when using the directed acyclic graph approach to disambiguate conflict rule pairs. The present methods are not thorough, flexible and user-friendly for the policy design in practical applications. To address these problems, an access control policy conflict detection algorithm based on intersection of target expression trees under the XACML (eXtensible Access Control Markup Language) specification is proposed. The method efficiently locates the conflict rule pairs based on the index structure through policy tree and rule effects, determines the conflict by expression comparison n, and marks the possible causes of the conflict, provides analysis of the disambiguation scheme, and achieves access control with fine granularity.
How do AI systems fail socially?: an engineering risk analysis approach

Failure Mode and Effect Analysis (FMEA) has been used as an engineering risk assessment tool since 1949. FMEAs are effective in preemptively identifying and addressing how a device or process might fail in operation and are often used in the design of high-risk technology applications such as military, automotive industry and medical devices. In this work, we explore whether FMEAs can serve as a risk assessment tool for machine learning practitioners, especially in deploying systems for high-risk applications (e.g. algorithms for recidivism assessment). In particular, we discuss how FMEAs can be used to identify social and ethical failures of Artificial Intelligent Systemss (AISs), recognizing that FMEAs have the potential to uncover a broader range of failures. We first propose a process for developing a Social FMEAs (So-FMEAs) by building on the existing FMEAs framework and a recently published definition of Social Failure Modes by Millar. We then demonstrate a simple proof-of-concept, So-FMEAs for the COMPAS algorithm, a risk assessment tool used by judges to make recidivism-related decisions for convicted individuals. Through this preliminary investigation, we illustrate how a traditional engineering risk management tool could be adapted for analyzing social and ethical failures of AIS. Engineers and designers of AISs can use this new approach to improve their system's design and perform due diligence with respect to potential ethical and social failures.
A sEMG Recording System

The sEMG (surface electromyographic) plays a significant role in the rehabilitation medicine, particularly in exoskeleton robotic treatment. A sEMG recording system using STM32F407 DISCOVERY development board and Labview software, combined with multiple signal acquisition sensors was proposed in this paper. The UC/OS-II operating system was embedded in the STM32F407 micro controller so that multiple channels can be picked up from different sensors at the same time. Due to the characteristics of STM32F407 microcontroller; the largest acquisition channels can be up to 16, and multiple signals can be collected such as the sEMG, angle trajectory, acceleration, torque etc. The use of DISCOVERY development board and Labview has reduced development cycle to a greater extent and resulted large improvement in work efficiency.
DeepQSMSeg: A Deep Learning-based Sub-cortical Nucleus Segmentation Tool for Quantitative Susceptibility Mapping

Deep brain nuclei are closely related to the pathogenesis of neurodegenerative diseases. Automatic segmentation for brain nuclei plays a significant role in aging and disease-related assessment. Quantitative susceptibility mapping (QSM), as a novel MRI imaging technique, attracts increasing attention in deep gray matter (DGM) nuclei-related research and diagnosis. This paper proposes DeepQSMSeg, a deep learning-based end-to-end tool, to segment five pairs of DGM structures from QSM images. The proposed model is based on a 3D encoder-decoder fully convolutional neural network. For concentrating network on the target regions, spatial and channel attention modules are adopted in both encoder and decoder stages. Dice loss is combined with focal loss to alleviate the imbalance of ROI classes. The result shows that our method can segment DGM structures from QSM images precisely, rapidly and reliably. Comparing with ground truth, the average Dice coefficient for all ROIs in the test dataset achieved 0.872±0.053, and Hausdorff distance was 2.644±2.917 mm. Finally, an age-related susceptibility development model was used to confirm the reliability of DeepQSMSeg in aging and disease-related studies.Clinical Relevance—Accurate and automatic segmentation tool for sub-cortical regions in QSM can significantly alleviate the pressure of radiologists. It can also accelerate the progress of related research and clinical translation.
A personalised bayesian approach for early intervention in gestational weight gain management towards pregnancy care

Pre-pregnancy body mass index and weight gain management are associated with pregnancy outcomes in expecting women. Poor gestational weight gain (GWG) management could increase the risk of adverse complications. These risks can be alleviated by lifestyle based interventions if undesired GWG trend is detected early on in the pregnancy. Current literature lacks analysis of gestational weight gain data and tracking the pregnancy over time. In this work, we collected longitudinal gestational weight gain data from women during their pregnancy and model their weight measurements to predict the end-of-pregnancy weight gain and classify it in accordance with the medically recommended guidelines. The measurement frequency of the weights is often very variable such that segments of data can be missing and the need to predict early utilising few data points complicates data modelling. We propose a Bayesian approach to forecast weight gain while effectively dealing with the limited data availability for early prediction. We validate on diverse populations from Europe and China. We show that utilising individual’s data only up to mid-way through the pregnancy, our approach produces mean absolute errors of 2.45 kgs and 2.82 kgs in forecasting end-of-pregnancy weight gain on these populations respectively, whereas the best of state-of- the-art yields 8.17 and 6.60 kgs on respective populations. The proposed method can serve as a tool to keep track of individual’s pregnancy and achieve GWG goals, thus supporting the prevention of excessive or insufficient weight gain during pregnancy.
A computational simulation of the motion of floating wind turbine platforms

The dynamic motion of floating wind turbines is studied using numerical simulations. The full Navier-Stokes equations are solved on a regular structured grid, using a level set method for the free surface and an immersed boundary method to model the turbine platform. The response of a tension leg platform to moderate amplitude waves is examined and the effect of a key design parameter is quantified in this initial study. It is shown that the pitch and surge motion amplitudes are larger than heave motions, and that increased ballast weight helps to stabilize the platform motions and reduce tether tensions.
Visual comfort in the daylit luminous environment: Structural model for evaluation

In order to create a comfortable daylit luminous environment which can also contribute to conservation of fossil fuel, a hypothetical model structured in terms of physical quantities representing stimuli, sensory responses, and an overall evaluation is presented. This hierarchical model was tested by a pilot experiment conducted in a side-lit classroom with 147 subjects. A questionnaire was carefully prepared according to the hierarchical model, so that the overall evaluation could be expressed in terms of comfort sensation related to the sensory responses to brightness on the desk and to the veiling reflection on the sheet of paper on the desk. The brightness sensation on the desk was expressed by a function not only of the desk illuminance but also of the vertical illuminance. Deficiencies of the model on the basis of the experimental results and a modification of the model are also discussed.
Distributed Channel Estimation Algorithm for mmWave Massive MIMO Communication Systems

Channel estimation in millimeter-wave (mmWave) massive MIMO systems is challenging, owing to the large number of antennas and the sparsity of mmWave channels. In this paper, we apply a distributed computing technique and design a channel estimation algorithm inspired by accelerated projection-based consensus (APC). The algorithm can thus run in parallel fashion. Simulation results show that the distributed algorithm has high reliability and high convergence rate under different noise levels.
An Adaptive OCV-SOC Curve Selection Classifier for Battery State-of-Charge Estimation

The State-of-Charge (SOC) estimation of lithium-ion batteries is crucial in battery management systems (BMS) for energy storage power stations. The open-circuit voltage (OCV)-SOC curve and the SOC estimation algorithm are important in SOC estimation. There exist two common OCV tests, including the low current OCV test and the incremental OCV test, for OCV-SOC curve acquirement, whose performances vary with different working conditions under $25^{\circ}{C}$. To make SOC estimation more effective, the least squares support vector machines (LS-SVM) method is presented as an adaptive classifier to decide which OCV test should be applied. Besides, an adaptive square-root unscented Kalman filter (ASRUKF) algorithm is proposed to improve square-root unscented Kalman filter (SRUKF) algorithm by updating the noise covariance matrixes in real-time. Based on the Center for Advanced Life Cycle Engineering (CALCE) public data set of University of Maryland of the 18650 LFP battery under $0^{\circ}{C}, 25^{\circ}{C}$ and $45^{\circ}{C}$, the SOC estimation algorithm based on adaptive OCV-SOC curve selection classifier is demonstrated to be precise, quick, robust and adaptive.
Robust Sybil Attack Detection in Vehicular Networks

The broadcast nature of the vehicular networks makes them vulnerable to Sybil attacks, where an attacker illegitimately claims multiple identities and undermines the networks. We propose a non-cryptographic attack detection approach that is based on signal-level wireless measurements. Our approach exploits the spatial signal variation of wireless channels to detect Sybil attacks. The performance of our approach is verified via extensive simulations and DSRC-based experiments in a real vehicular network. The results show that we achieve the detection rates of 95% in simulations and 99% in real-world experiments. The proposed approach can be deployed on the existing systems without a need for additional hardware or infrastructure.
The Synchronized Enhancement Effect of Rhythmic Visual Stimulation of 40 Hz on Selective Attention

Rhythmic visual stimulation (RVS) has been demonstrated to modulate ongoing neuronal oscillations which might be greatly involved in attention processes and thus bring some behavioral consequences. However, there was little knowledge about the effective frequency parameter of RVS which could impact task performance in visuo-spatial selective attention. Thus, here, we addressed this question by investigating the modulating effects of RVSs in different attention-related frequency bands, i.e., alpha (10 Hz) and gamma band (40 Hz). Sixteen participants were recruited to perform a modified visuo-spatial selective attention task. They were required to identify the orientation of target-triangle in visual search arrays while undergoing different RVS backgrounds. By analyzing the acquired behavioral and EEG data, we observed that, compared with control group (no RVS), 40 Hz RVS led to significantly shorter reaction time (RT) while 10 Hz RVS did not bring obvious behavioral consequences. In addition, although both 10 and 40 Hz RVS led to a global enhancement of SSVEP spectrum in the gamma band, 40 Hz RVS led to even larger 40 Hz SSVEP spectrum in prefrontal cortex. Our findings indicate that 40 Hz RVS has an effectively enhancing effect on selective attention and support the crucial role of prefrontal area in selective attention.
Socially-Aware V2X QoS for NOMA Dual-Connectivity

Vehicle-to-everything (V2X) is one of the core technologies associated with 5G. It enables vehicles to communicate with other vehicles, networks, and infrastructure. Despite the promise V2X offers, i.e. ubiquitous vehicular connectivity, an often overlooked aspect of V2X is the impact bursty data, such as that encountered when a vehicle accident occurs, has on the network's Quality of Service (QoS). In this work, we study both 4G V2X and 5G V2X utilizing Evolved Universal Terrestrial Radio Access New Radio (E-UTRA-NR) Dual Connectivity (en-DC) Road Side Units (RSUs). By using social groups to determine QoS, versus statically defining QoS, we find that we can leverage 5G RSUs to minimize the impact of localized bursty traffic events on the overall network's QoS, while at the same time enabling regional awareness of localized road traffic conditions via a socially-aware Road Condition Warning Systems (RCWS). We demonstrate that our proposed socially-aware QoS management methodology outperforms legacy socially-unaware 4G V2X and can prove beneficial for 5G V2X and beyond.
Performance Comparison of GDMA, LDS-CDMA, and SCMA for Transmissions over Rayleigh Fading Channels

Gain division multiple access (GDMA), low density spreading code division multiple access (LDS-CDMA) and Sparse code multiple access (SCMA) are non-orthogonal multiple access methods, where SCMA is a method derived from LDS-CDMA with a special design of signal constellation. We compare their performances for transmission over Rayleigh fading channels. Simulation results show that the special constellation design of SCMA does not provide any gain in Rayleigh fading channels. We show that the error performances of a 6-user LDS-CDMA scheme based on 4 resources can be evaluated by applying diversity-2 concept to the 3-user GDMA based on a single resource. However, by using more resources and error-correcting codes, GDMA can also achieve diversity and hence provide error rates similar to LDS-CDMA. In addition, GDMA has the advantage of low complexity.
Assistive Sliding Mode Control of a Rehabilitation Robot with Automatic Weight Adjustment

There are approximately 13 million new stroke cases worldwide each year. Research has shown that robotics can provide practical and efficient solutions for expediting post-stroke patient recovery. This simulation study aimed to design a sliding mode controller (SMC) for an end-effector-based rehabilitation robot. A genetic algorithm (GA) was designed for automatic controller weight adjustment. The optimal weights were obtained by minimizing a cost function comprising the end-effector position error, robot input, robot input-rate, and patient input. To promote safe tuner optimization, a model of the human arm was incorporated to generate the human joint torque. A computed-torque proportional derivative controller (CTPD) was designed for the human arm to approximate the central nervous system (CNS) motor control. This controller was adjusted to simulate rehabilitation effects and patient adaptation. The tuner was optimized for a trajectory tracking task with an assistive high-level control scheme. The simulation results showed lower cost compared to seven manual weight settings. The optimal weights provided good tracking performance and suitable robot inputs. This research provides a framework to conduct various simulations before testing our controller on human subjects. The preliminary results of this study will be used as the starting point for online adaptive controller tuning, which will be examined in our future research.
Interferometric crosstalk-free optical add/drop multiplexer using Mach-Zehnder-based fiber gratings

Mach-Zehnder interferometers with fiber Bragg gratings (MZ-FG) are investigated as promising devices for wavelength-selectable optical add-and-drop multiplexers (OADM). A wavelength reused OADM performance is demonstrated for the first time to our knowledge in a six-channel, 10 Gb/s WDM experiment using a single-stage MZ-FG. We discuss both theoretically and experimentally the interferometric crosstalk induced by imperfect Bragg-reflectivity. Two methods are proposed to solve the interferometric crosstalk issues. The first one is wavelength offset scheme. It is experimentally confirmed that there is no interference when the wavelength of crosstalk is away from that of the signal at least two times of bit-rate. In order to separate each wavelength, the master-slave wavelength control method is proposed with Fabry-Perot interferometer made of a set of fiber gratings. The second one is a cascaded MZ-FG scheme eliminating the crosstalk itself. The calculation indicates that a Bragg-reflectivity of only 93% can suppress the crosstalk to be -35 dB. Cascaded MZ-FG's have been fabricated to show that the interferometric crosstalk can be successfully reduced to -50 dB for the add signal, and -71 dB for the drop signal, respectively. The eight-wavelength 10 Gb/s OADM experiment is carried out to demonstrate the low interferometric crosstalk performance.
Machine Learning Based mmWave Orchestration for Edge Gaming QoE Enhancement

Millimeter wave (mmWave) is a crucial component in 5G and beyond 5G communications. However, the dense deployment of mmWave transceivers imposes a heavy burden on the management of radio access network (RAN). This challenge increases the need for autonomous network management methods leveraging machine learning (ML) techniques. In particular, mmWave beam selection is a critical issue for the management of RAN due to the large training overhead on mmWave transceivers. To this end, a new beam tracking method based on sequence-to-sequence (Seq2Seq) learning is proposed. Besides, thanks to edge computing technologies, network management algorithms and delay-sensitive user applications can be hosted on edge servers in close proximity. Due to limited resources on the edge server, the resource allocation problem for beam tracking and edge gaming is investigated with the aim of maximizing game quality of experience (QoE). Simulation results verify the effectiveness of the proposed orchestration scheme.
Self-Paced Learning and Privileged Information based Cascaded Multi-column Classification algorithm for ASD diagnosis

Autism spectrum disorder (ASD) is one of the most serious mental disorder in children. Machine learning based computer aided diagnosis (CAD) on resting-state functional magnetic resonance imaging (rs-fMRI) for ASD has attracted widespread attention. In recent years, learning using privileged information (LUPI), a supervised transfer learning method, has been generally used on multi-modality cases, which can transfer knowledge from source domain to target domain in order to improve the prediction capability on the target domain. However, multi-modality data is difficult to collect in clinical cases. LUPI method without introducing additional imaging modality images is worth further study. Random vector function link network plus (RVFL+) is a LUPI diagnosis algorithm, which has been proven to be effective for classification tasks. In this work, we proposed a self-paced learning based cascaded multi-column RVFL+ algorithm (SPL-cmcRVFL+) for ASD diagnosis. Initial classification model is trained using RVFL on the single-modal data (e.g. rs-fMRI). The output of the initial layer is then sent as privileged information (PI) to train the next layer of classification model. During this process, samples are selected using self-paced learning (SPL), which can adaptively select simple to difficult samples according to the loss value. The procedure is repeated until all samples are included. Experimental results show that our proposed method can accurately identify ASD and normal control, and outperforms other methods by a relatively higher classification accuracy.