Miracle-dz/UniFAR-FTUs huggingface dataset:
Visible light communication-based positioning for indoor environments using supervised learning

This paper studies a novel way to estimate the position of an object in an indoor environment, using the Channel State Information (CSI) that a Visible Light Communication (VLC) system collects to maintain the link-level connectivity. First, supervised learning is applied to characterize, the effect that an object in variable but known positions has on the received optical wireless signal. Second, the trained classifier is used to estimate the new unknown positions that the object may take, making use of the instantaneous CSI that is used to equalize the data-carrying signal samples in reception. The practical validation of the proposed positioning approach was done with the aid of a software-defined VLC link based on OFDM, in which a copy of the intensity modulated signal coming from a Phosphor-converted LED is captured by Photodetectors (PDs) in different room locations. Then, the CSI of the VLC receiver is used to train a Random Forest classifier, which will predict the position of the object during the assessment phase. The performance evaluation of our experimental setting shows that the proposed VLC-based positioning approach can reach a few centimeter accuracy, provided that a proper training is executed, without the necessity of deploying a large number of PDs in the room, or adding a VLC receiver on the object to be tracked.
Age of Information-Aware Resource Management in UAV-Assisted Mobile-Edge Computing Systems

This paper investigates the problem of age of information (AoI)-aware resource awareness in an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, which is deployed by an infrastructure provider (InP). A service provider leases resources from the InP to serve the mobile users (MUs) with sporadic computation requests. Due to the limited number of channels and the finite shared I/O resource of the UAV, the MUs compete to schedule local and remote task computations in accordance with the observations of system dynamics. The aim of each MU is to selfishly maximize the expected long-term computation performance. We formulate the non-cooperative interactions among the MUs as a stochastic game. To approach the Nash equilibrium solutions, we propose a novel online deep reinforcement learning (DRL) scheme, which enables each MU to behave using its local conjectures only. The DRL scheme employs two separate deep Q-networks to approximate the Q-factor and the post-decision Q-factor for each MU. Numerical experiments show the potentials of the online DRL scheme in balancing the tradeoff between AoI and energy consumption.
New Soluble n-Type Conjugated Polymers for Use as Electron Transport Materials in Light-Emitting Diodes

We report the synthesis of a series of six new soluble n-type conjugated copolymers that incorporate bis(phenylquinoline) and regioregular dialkylbithiophene in the backbone, the investigation of their electrochemical, photophysical, and electroluminescent properties, and their use as electron transport materials in light-emitting devices. Variation of the alkyl chain length of the head-to-head dialkylbithiophene linkage from butyl (C4) to dodecyl (C12) had little or no effect on the photophysical or redox properties of the new polyquinolines. However, the glass transition temperature decreased from 256 °C for the butyl derivative to 76 °C for the dodecyl derivative. A single-crystal X-ray structure of a model compound allowed quantification of the intramolecular torsion angles which explain the significant difference in optical band gap and redox properties between the head-to-tail and head-to-head polymers. The high electron affinity (2.88−2.97 eV), robust thermal stability, suitable solution processabi.
On Vulnerability Analysis of Nodes against Cross-domain Cascading Failures Propagation in Active Distribution Network Cyber-physical System

Active distribution network (ADN) is the one of typical cyber-physical system which includes power networks (PNs) and communication networks (CNs), and they are closely interdependent. ADN is exposed to the risk of cross-domain cascading failures (CCFs) propagating between PNs and CNs. In this paper, it analyzes the most likely nodes which failures could cause the propagation of CCFs. Specially, the connectivity of interdependent interconnected subnetwork (IIS) is applied to represent the function of ADN, and a Node Vulnerability Index Evaluation method (shorted as NVIE) based on the shortest-path-function-cycle is proposed to find these vulnerability nodes, and an IIS hypothesis proposed in this paper is better than the giant component hypothesis and the giant efficiency subgraph hypothesis, it also reveals that some non-power generation nodes are more likely to cause the propagation of CCFs among ADN. A group extensive simulation experiments are conducted for verifying the effectiveness of NVIE method.
Incorporating the Dynamic Threshold Voltage Into the SPICE Model of Schottky-Type p-GaN Gate Power HEMTs

The threshold voltage (<italic>V</italic><sub>TH</sub>) of an enhancement-mode Schottky-type <italic>p</italic>-GaN gate high-electron-mobility transistor (HEMT) is found to have a special dependence on the drain bias. The device commonly requires higher gate voltage to switch <sc>on</sc> the transistor from a high-drain-voltage <sc>off</sc>-state than what is expected from the static device characteristics. The reason behind the dynamic <italic>V</italic><sub>TH</sub> has been proved to be the floating <italic>p</italic>-GaN layer, where charges could be stored and further influence <italic>V</italic><sub>TH</sub> under different drain bias. In this article, a SPICE-compatible equivalent circuit model is presented according to the structure of Schottky-type <italic>p</italic>-GaN gate HEMTs. It features a floating node to imitate the charge storage process within the gate stack. Compared to conventional models, the proposed model could accurately predict the dynamic <italic>V</italic><sub>TH</sub> characteristics and switching behaviors of power electronics circuits, where Schottky-type <italic>p</italic>-GaN gate HEMTs are deployed as power transistors. The phenomena related to the dynamic <italic>V</italic><sub>TH</sub>, including the disappearance of Miller plateau, the overestimated false-turn-<sc>on</sc> problem, and the higher reverse conduction loss are evaluated with a half-bridge circuit and the merits of the proposed model are verified.
Multiple Access for Massive MIMO-OTFS Networks over Angle-Delay-Doppler Domain

This paper focuses on a new path division multiple access (PDMA) for both uplink (UL) and downlink (DL) massive multiple-input multiple-output network over a high mobility scenario, where the orthogonal time frequency space (OTFS) is adopted. First, the 3D UL channel model and the received signal model in the angle-delay-Doppler domain are studied. Then, along UL, we design a path scheduling algorithm to properly assign angle-domain resources at user sides. Correspondingly, the signals at desired grids within the 3D resource space of the base station are separately collected to implement the 3D channel estimation and maximal ratio combining-based data detection. Then, we construct a low-complexity beamforming scheme over the angle-delay-Domain domain to achieve interuser interference free DL communication. Simulation results are provided to demonstrate the validity of our proposed unified UL/DL PDMA scheme.
Evaluation of Forwarding Strategies for NDN-based Multi-Access Edge Computing

Named Data Networking (NDN) is a promising future Internet architecture which can overcome challenges in Multi-Access Edge Computing (MEC). In this paper, we focus on the forwarding strategy which is an essential part for realizing an NDN-based MEC architecture. Through extensive experiments on a testbed, we evaluate each default strategy in NDN with regard to three criteria: reliability, throughput, and overhead. We point out pros and cons of each strategy, and find two missing features, in-network recovery and load balancing, for improving the performance of MEC communication. Finally, we design a forwarding strategy with the above features which achieves higher throughput and reliability for end users than existing strategies while also reducing network congestion.
Ergodic Capacity Analysis of Alamouti Coded Cooperative Communication in Downlink NOMA

The demand for massive connectivity and low latency in future wireless networks has outlined the non-orthogonal multiple access (NOMA) scheme a key technology due to its effectiveness to meet the massive capacity requirement of the beyond fifth-generation (B5G) and the sixth-generation (6G) systems. However, with the increase in the total number of users in NOMA, the number of successive interference cancellations (SICs) at each user is also increased, which ultimately incurs high computational complexity and hardware cost of the overall system. Space-time block code (STBC)-based cooperative NOMA (STBC-NOMA) requires a smaller number of SICs as compared to that of conventional cooperative NOMA. However, prior work on STBC-NOMA assumes perfect SIC, which is challenging in practice. Motivated by this fact, in this paper, we study the impact of imperfect SICs on the performance of STBC-NOMA. Further, we derive the closed-form expression for the ergodic capacity of STBC-NOMA for imperfect SICs and use that to draw a comparative analysis between STBC-NOMA, conventional cooperative NOMA, and non-cooperative NOMA. We find the maximum number of users accommodated by STBCNOMA for different levels of imperfect SICs.
Eco-Driving Operation of Connected Vehicle With V2I Communication Among Multiple Signalized Intersections

Eco-driving through multiple intersections has significant fuel benefit for road transportation. Most existing studies assume that vehicles travel at a constant speed between intersections, which naturally leads to large errors in fuel prediction and trajectory planning. This article focuses on eco-driving operation through multiple signalized intersections considering more realistic powertrain dynamics, which directly contain the engine and the transmission as well as aerodynamic drag, rolling resistance, and so on. This feature enables explicit descriptions of dynamic behaviors and fuel characteristics in acceleration, deceleration, and constant speed driving. An open-loop optimal control problem (OCP) is formulated to minimize fuel consumption between two red-signalized intersections, and the two- or three-stage operation rules are then proposed to approximate its optimal solution. The eco-driving of passing multiple intersections is then numerically solved by combining the two- or three-stage driving rule and the Dijkstra algorithm. This method can consider the acceleration and deceleration processes at each intersection and achieve improved fuel economy.
Total-Ionizing-Dose Tolerance of the configuration function of MAX3000A CPLDs

Recently, space-grade radiation-hardened SRAM-based field programmable gate arrays (FPGAs) have become available. However, since the configuration memory of SRAM-based FPGAs is sensitive to damage by radiation, flash-based FP-GAs and complex programmable logic devices (CPLDs) are frequently used for space systems instead of SRAM-based FPGAs. Although flash-based FPGAs and CPLDs are programmable devices, the configuration memory of them is somewhat robust against radiation. This paper presents experimentally obtained result of the total-ionizing-dose tolerance of the configuration function of a CPLD or MAX3000A (EPM3128ATC100-10; Intel Corp.). The total-ionizing-dose tolerances of the configuration function of six MAX3000A chips have been measured using an approximately 30 TBq 60Co gamma radiation source with emissions up to 50 krad.
Practical Full Chip Clock Distribution Design With a Flexible Topology and Hybrid Metaheuristic Technique

This article recommends a practical technique to design full chip (FC) clock tree of a complex system-on-chip (SoC). In the new business environment, the market prefers a highly integrated but low power SoC with fast design productivity and low development cost. We observed that many techniques proposed in the prior arts are no longer practical or enough. With that, we introduce a flexible FC Clock network topology and a synthesis algorithm that utilize a hybrid meta-heuristic technique to search for near optimum solution in shorter turn-around time. Our work on a 10nm SoC product showed that the topology and algorithm managed to produce averagely 16.98% better global skew, 42.75% less divergence on critical clock paths and with 64.5% shorter clock balancing phase compared to a conventional ASIC methodology.
An Arbitrarily Reconfigurable Extreme Learning Machine Inference Engine for Robust ECG Anomaly Detection

Extreme learning machine (ELM) has shown to be an effective and low-power approach for real-time electrocardiography (ECG) anomaly detection. However, prior ELM inference chips are noise-prone and lacking in reconfigurability. In this article, we present an arbitrarily reconfigurable ELM inference engine fabricated in 40-nm CMOS technology for robust ECG anomaly detection. By combining Adaptive boosting (Adaboost) and Eigenspace denoising with ELM (AE-ELM), robust classification under noisy conditions is achieved and saves the number of required multiplications by 95.9%. For chip implementation, a reconfigurable VLSI architecture is designed to support arbitrary complexity of AE-ELM, accounting for dynamic change in application requirements. On the other hand, we propose to construct the input weight matrix of ELM as a Bernoulli random matrix, which further reduces the number of multiplications by 55.2%. For real-time detection, parallel computing is exploited to reduce the latency by up to 86.8%. Overall, the 0.21-mm2 AE-ELM inference engine shows its robustness against noisy signals and achieves $1.83\times$ AEE compared with the state-of-the-art ELM design.
Aggregated-Impedance-Based Stability Analysis for a Parallel-Converter System Considering the Coupling Effect of Voltage Feedforward Control and Reactive Power Injection

In this article, the sequence impedance of a single converter is separated into a fixed impedance term and an output power dependent term. Then, an aggregated impedance model of identical parallel converters is established to evaluate the stability performance. It is found that the internal power allocation among parallel converters has no impact on the system stability. Further analysis has indicated that the total reactive power injection and the type of voltage feedforward control have highly coupled impact on the system stability. Specifically, increasing the reactive power can always help to stabilize the system when no voltage feedforward loop or voltage feedforward with a low-pass filter is adopted. However, when the direct point of common coupling (PCC) voltage feedforward control is applied to converters, reactive power injection within a certain range can improve system stability. Comprehensive verification results are provided to validate the correctness of conclusion and the reactive power injection approach.
On Improved Training of CNN for Acoustic Source Localisation

Convolutional Neural Networks (CNNs) are a popular choice for estimating Direction of Arrival (DoA) without explicitly estimating delays between multiple microphones. The CNN method first optimises unknown filter weights (of a CNN) by using observations and ground-truth directional information. This trained CNN is then used to predict incident directions given test observations. Most existing methods train using spectrally-flat random signals and test using speech. In this paper, which focuses on single source DoA estimation, we find that training with speech or music signals produces a relative improvement in DoA accuracy for a variety of audio classes across 16 acoustic conditions and 9 DoAs, amounting to an average improvement of around 17% and 19% respectively when compared to training with spectrally flat random signals. This improvement is also observed in scenarios in which the speech and music signals are synthesised using, for example, a Generative Adversarial Network (GAN). When the acoustic environments during test and training are similar and reverberant, training a CNN with speech outperforms Generalized Cross Correlation (GCC) methods by about 125%. When the test conditions are different, a CNN performs comparably. This paper takes a step towards answering open questions in the literature regarding the nature of the signals used during training, as well as the amount of data required for estimating DoA using CNNs.
4G LTE Network Throughput Modelling and Prediction

The past decade has witnessed a staggering evolution in cellular networks. Mobile wireless technologies have undergone four distinct generations; from uncomplicated voice calls in the first generation to high-speed, low latency and video streaming in the fourth generation. The numerous services brought to the users by 4G network have caused an increasing load demand. This increasing demand in network usage has proven the necessity of further service enhancements, such as predictive resource allocation techniques and handover analysis. For these techniques to be deployed, network quality and performance analysis must be performed on real-world network data. Since throughput is a major indicator of the network’s performance, throughput modelling and prediction can be utilized for analyzing network quality. In this paper, two approaches for throughput analysis are examined: classical machine learning and time series forecasting. For the first approach, various machine learning models were deployed for throughput prediction and our analysis showed that the random forest model achieved the highest prediction performance. For time series forecasting, statistical methods as well as deep learning architectures were used. The evaluation shows that the machine learning models had a higher throughput prediction performance than the time series forecasting techniques.
RECON: Resource-Efficient CORDIC-Based Neuron Architecture

Contemporary hardware implementations of artificial neural networks face the burden of excess area requirement due to resource-intensive elements such as multiplier and non-linear activation functions. The present work addresses this challenge by proposing a resource-efficient Co-ordinate Rotation Digital Computer (CORDIC)-based neuron architecture (RECON) which can be configured to compute both multiply-accumulate (MAC) and non-linear activation function (AF) operations. The CORDIC-based architecture uses linear and trigonometric relationships to realize MAC and AF operations respectively. The proposed design is synthesized and verified at 45nm technology using Cadence Virtuoso for all physical parameters. Implementation of the signed fixed-point 8-bit MAC using our design, shows 60% less area, latency, and power product (ALP) and shows improvement by 38% in area, 27% in power dissipation, and 15% in latency with respect to the state-of-the-art MAC design. Further, Monte-Carlo simulations for process-variations and device-mismatch are performed for both the proposed model and the state-of-the-art to evaluate expectations of functions of randomness in dynamic power variation. The dynamic power variation for our design shows that worst-case mean is $189.73\mu W$ which is 63% of the state-of-the-art.
Optimal Control Strategy Parameters of Parallel Hybrid Electric Vehicles Based on Particle Swarm Optimization

Hybrid Electric Vehicles (HEVs) allow fuel economy and reduced emissions in comparison to conventional vehicles. To improve HEV performance in relation to reduce fuel utilization and emissions, and guarantee driving performance, the optimization of control strategy is indispensable. In this paper, the multiobjective optimization problem is converted to single-objective problem. Particle Swarm Optimization (PSO) algorithm is then used to conceive appropriate control parameters, for the purpose to reduce fuel consumption and emissions with conserved vehicle performance requirements. To simulate a parallel hybrid electric vehicle, ADvanced VehIcle SimulatOR (ADVISOR) is used with Federal Test Procedure (FTP) and Urban Dynamometer Driving Schedule (UDDS) to estimate Fuel Consumption (FC), emissions and vehicle dynamics.
Intelligent Reflecting Surface Assisted NOMA Over Fading Channels

This paper considers a two-user downlink intelligent reflecting surface (IRS) assisted network over fading channels. Particularly, non-orthogonal multiple access (NOMA) and two orthogonal multiple access (OMA) schemes, namely, time division multiple access (TDMA) and frequency division multiple access (FDMA), are studied. Our goal is maximizing the system average sum rate for delay-tolerant transmission. The power budget, minimum average user rate and discrete unit modulus reflection coefficient constraints are considered as constraints. To solve the problem, phase shifters adjustment algorithms with low complexity are proposed to obtain high quality solutions. With given phase shifters, the optimal power allocation is obtained using the Lagrangian dual decomposition. Numerical results demonstrate the performance gain by introducing IRS into the system as well as the utility of the proposed algorithms.
Trans-ethnic eQTL meta-analysis of human brain reveals regulatory architecture and candidate causal variants for brain-related traits

While large-scale genome-wide association studies (GWAS) have identified hundreds of loci associated with neuropsychiatric and neurodegenerative traits, identifying the variants, genes and molecular mechanisms underlying these traits remains challenging. Integrating GWAS results with expression quantitative trait loci (eQTLs) and identifying shared genetic architecture has been widely adopted to nominate genes and candidate causal variants. However, this integrative approach is often limited by the sample size, the statistical power of the eQTL dataset, and the strong linkage disequilibrium between variants. Here we developed the multivariate multiple QTL (mmQTL) approach and applied it to perform a large-scale trans-ethnic eQTL meta-analysis to increase power and fine-mapping resolution. Importantly, this method also increases power to identify conditional eQTL's that are enriched for cell type specific regulatory effects. Analysis of 3,188 RNA-seq samples from 2,029 donors, including 444 non-European individuals, yields an effective sample size of 2,974, which is substantially larger than previous brain eQTL efforts. Joint statistical fine-mapping of eQTL and GWAS identified 301 variant-trait pairs for 23 brain-related traits driven by 189 unique candidate causal variants for 179 unique genes. This integrative analysis identifies novel disease genes and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer's disease.