Miracle-dz/UniFAR-FTUs huggingface dataset:
T-S Fuzzy H∞ Observer-Controller Design for 2-D Systems

The paper investigates the design problem of fuzzy observer-based $H_{\infty}$ control for two-dimensional 2-D discrete-time Takagi-Sugeno (T-S) fuzzy systems described by the second Fornasini-Marchesini F-M model. The use of fuzzy Lyapunov functions and free-weighting matrices allows to implement a new design process. Then, using certain particular derivations, Bi-linear Matrix Inequalities (BMIs) are successfully turned into a set of Linear Matrix Inequalities (LMIs) that can be solved in a single step. Then, the presented method allows simultaneously computing the fuzzy observer and controller gains, with a prescribed $H_{\infty}$ noise attenuation $\gamma$ for the whole system. Finally, a practical example is provided to verify the effectiveness and applicability of the proposed observer-based fuzzy control approach.
A Correlation for Heat Transfer and Wake Effect in the Entrance Region of an In-Line Array of Rectangular Blocks Simulating Electronic Components

An experimental investigation is carried out to study heat transfer in the entrance region of an array of rectangular heated blocks. The focus of the work is on the entrance heat transfer coefficients and the associated thermal wake effects. The experiments were performed with air as the working fluid. The geometric parameters of the array were varied in the range identified with B/L=0.5, S/L=0.128-0.33, and H/L=0.128-1. The Reynolds number, based on the height above the blocks and the fluid mean velocity in the bypass channel, ranged from 3000 to 15,000. The abiatic heat transfer coefficients and thermal wake effects are correlated for the entrance region. These correlations are incorporated into a user-friendly FORTRAN program, which can be used by the engineers to prodict the working temperatures of the components of circuit boards with similar layout. A typical computer output indicated that the mean deviation between the measured and predicted temperatures is 11.0 percent
Optimal Network Location in Infinite Horizon LQG Graphon Mean Field Games

We propose to study a class of infinite horizon linear quadratic Gaussian Graphon Mean Field Games (GM-FGs) inspired by the infinite horizon Mean Field Games in [1]. Graphon Mean Field Games (GMFGs) are non-uniform generalizations of Mean Field Games where the non-uniformity of agents is characterized by the nodes on which they are located in a network. Under mild conditions, we obtain for almost every node, an analytical expression for the cost at GMFG equilibrium, and propose a necessary and sufficient condition under which a particular node in the network is associated with the minimal cost at GMFG equilibrium.
Cross‐scale coupling‐induced intermittency near interplanetary shocks

[1] Several approaches exist to address the problem of intermittency in solar wind turbulence. The traditional explanation based on spectral transfer of energy between separated scales through sequential steps within a cascade can elucidate scalings and intermittent emergence of small-scale dissipative structures. A recent point of view connects intermittency mainly with the occurrence of discontinuities and interaction of flux tube-like coherent structures. In this paper we analyze the interrelationship between skewness and kurtosis near interplanetary shocks. On the basis of a comparison of our results with the behavior of skewness and kurtosis in laboratory experiments and numerical simulations we show that large-scale boundaries and/or scalar gradient fields imposed on solar wind turbulence can lead to a local increase of small-scale intermittency via direct coupling between separated scales. As each scenario is supported by experiments, they represent a complementary way of looking at intermittency.
Asymptotic spreading of competition diffusion systems: The role of interspecific competitions

This paper is concerned with the asymptotic spreading of competition diffusion systems, with the purpose of formulating the propagation modes of a co-invasion–coexistence process of two competitors. Using the comparison principle for competitive systems, some results on asymptotic spreading are obtained. Our conclusions imply that the interspecific competitions slow the invasion of one species and decrease the population densities in the coexistence domain. Therefore, the interspecific competitions play a negative role in the evolution of competitive communities.
A transformer-based model for effective and exportable IoMT-based stress detection

Pervasive e-healthcare services have evolved rapidly in the recent years with the surge of the Internet of Medical Things (IoMT). Intelligent stress monitoring assistant is such an example that uses affective computing to detect stress levels based on biological signals. Existing works use different forms of machine learning and deep learning methods to successfully detect stress in pre-defined individuals. However, such models fail to recognise stress in unseen people, a problem that is harder to solve but of critical importance when three quarters of people in our society feel unable to cope due to stress. In this work, we propose a deep learning method that leverages self-attention to compute representations of the network's input and output layers and yields a generic stress detection model. We present a comparative analysis in which we examine the efficacy of Random Forest with handcrafted features, CNN-based deep learning, and transformer network with multi-modal data. Our results show that the proposed transformer solution outperforms the state of the art in all scenarios with accuracy of 96% and F1 score of 97%. More importantly, the model is validated using a leave-one-subject-out (LOSO) approach, hence is exportable and can successfully detect the stress condition of any unseen person.
Power Level Design-aware Throughput Analysis of Grant-Free Power-Domain NOMA in mMTC

This paper proposes an analytical model for the throughput of the grant-free power-domain non-orthogonal multiple access (called GF-NOMA) in the massive machine-type communications, reflecting the characteristics of packet errors. GF-NOMA designs power resources (called power levels) for each terminal to autonomously satisfy the decoding condition in NOMA. This design causes packet errors at higher-power levels than the level used by multiple terminals due to the non-orthogonality. Our proposed analytical model reflects the packet errors due to this power level design to analyze the throughput more accurately than related works. Our analytical results highlighted that our proposed model underestimated the throughput by only 0.3% over the Monte Carlo simulation at fifteen power levels. In contrast, an existing model overestimated the throughput by 68% over the simulation. Our analysis also showed the throughput characteristics and revealed that using the lowest-power level provided 89% lower throughput than using the highest-power level at ten power levels.
Stabilization of Nonholonomic Pendulum Skate by Controlled Lagrangians

We consider the problem of stabilizing what we call a pendulum skate, a simple model of a figure skater developed by Gzenda and Putkaradze. By exploiting the symmetry of the system as well as taking care of the part of the symmetry broken by the gravity, the equations of motion are given as nonholonomic Euler–Poincaré equation with advected parameters. Our main interest is the stability of the sliding and spinning equilibria of the system. We show that the former is unstable and the latter is stable only under certain conditions. We use the method of Controlled Lagrangians to find a control to stabilize the sliding equilibrium.
EXOSAT observations of the blazar PKS 1510-089

A detailed study of X-ray emission from the high-polarization quasar PKS 1510 - 089, also known as a blazar, is presented, based on the Exosat observations in 1984 and 1985. The data were obtained from the EXOSAT archives. There is no evidence for any variations in the X-ray intensity on hourly time scales during either of the two observations. The 2-10 keV X-ray flux shows no significant change over a year. The observed X-ray spectral data is best fitted by a power law with a photon index, Γ of 1.40 +0.35 -0.30 (error bars are with 90% confidence). The slope of the power law is somewhat flatter for PKS 1510-089 as compared with the canonical spectra of the active galactic nuclei; however, the present observations do not rule out a normal Seyfert-type spectrum. There is no detection of any significant low-energy absorption within the blazar, nor is there any evidence for a soft X-ray excess. A line feature modeled as a Gaussian is detected with a high significance near 5 keV. The emission line could be the redshifted 6.4 keV line due to the fluorescence of cold iron present around the nucleus of PKS 1510 - 089 or the redshifted 6.7 keV line due to ionized iron in a hot plasma. The observed intensity of the line, if produced by fluorescence, would require the cold matter to subtend a very large solid angle to the X-ray continum source.
Disentangled Representation for Cross-Domain Medical Image Segmentation

Image segmentation is a long-standing problem in medical image analysis to facilitate the clinical diagnosis and intervention. Progress has been made due to deep learning via supervised training with elaborate human labeling, and however, the segmentation models trained by the labeled source domain cannot perform well in the target domain, making existing approaches lack robustness and generalization ability. Considering that the acquisition of medical image labels is quite expensive and time-consuming, we propose a novel feature disentanglement (FD)-based unsupervised domain adaptation (UDA) method to improve the robustness of the trained model in the target domain. A segmentation network is designed to learn disentangled features with two parts: 1) content-related features, which are responsible for the segmentation task and invariant across domains, and 2) style-related features, which elucidate the discrepancy between different domains. FD is achieved by multitask learning and image translation. Meanwhile, knowledge distillation is introduced to improve the performance on fine-grained segmentations. Also, for objects with regular shape, we incorporate the adversarial training to predict shape-invariant segmentation masks across domains. Comprehensive experiments are conducted on retina vessel segmentation and sinus surgical instrument segmentation to validate the effectiveness of the proposed method. The average Dice of twenty regular transfer directions achieves 79.26% on five public benchmarks of retina vessel segmentation, the average Dice of two transfer directions from regular to ultrawide field (UWF) attains 72.63%, and the Dice from cadaveric images to live images reaches 68.1% on sinus surgical instrument segmentation. The results demonstrate that the proposed method achieves the state-of-the-art segmentation performance in the UDA setting.
Trajectory-based Fall Detection and Recognition Using Ultra-Wideband Signals

Automatic fall detection and recognition are challenging problems. In this paper, a novel solution is proposed based on the trajectories of human falls by using the ultra-wideband (UWB) communication system and machine learning methods for fall detection and recognition. Most previous studies of fall detection based on active UWB sensing used electromagnetic signals directly, which may bring problems like radar clutter, signal coupling, multi-path, fading, and interference. Our proposed method only uses human falls trajectories by passive UWB sensing, which achieved fall recognition performance of 93.26% by using the support vector machine with RBF kernel function (SVM-RBF). Compared with previous research, the superiority of this study is that our solution is robust against interference and environmental changes, which means it is reliable for real-world applications. The archived UWB datasets and code have been already published, which may provide the basis for the comparison of techniques and improvements.
Mutual Information-Maximizing Quantized Layered Min-Sum Decoding of QC-LDPC Codes

In this paper, we propose a mutual information-maximizing quantized layered min-sum (MIM-QLMS) decoder for quasi-cyclic low-density parity-check (QC-LDPC) codes. Our proposed decoder operates similarly to a layered min-sum decoder with additional reconstruction and quantization operations by using single-input lookup tables (LUTs). In particular, we first develop the protograph-based MIM density evolution to design the LUTs, which may differ for each iteration and each edge in the protograph of the QC-LDPC codes. Furthermore, to minimize the memory requirement for storing the LUTs, we propose an optimization method to unify all LUTs into only four distinct LUTs, which can be used for all decoding iterations. To the best of our knowledge, the proposed MIM-QLMS decoders are the first class of layered finite alphabet iterative decoders (FAIDs) that are designed based on accurately tracking the probability distributions of the exchanged messages. Simulation results show that for 3-bit (resp. 4-bit) exchanged message precision, the proposed MIM-QLMS decoders can reasonably (resp. generally) outperform the state-of-the-art layered FAIDs and the layered normalized min-sum decoder, in terms of both the error rate performance and the average number of iterations.
The Correlation Length for the High-Density Phase of Bernoulli Percolation

L'auteur examine 2 types standards de fonctions de connectivite dans la phase de densite elevee de filtration de Bernoulli du plus proche voisin. Il demontre que les 2 quantites decroissent exponentiellement avec le meme taux constant. La reciproque de cette constante definit donc une longueur de correlation, pour laquelle on n'arrive pas a montrer qu'elle est finie au-dessus d'une densite critique associee a la transition de phases. Des proprietes de semicontinuite et de convexite sont montrees pour cette longueur de correlation, et on obtient des resultats analogues a ceux concernant le modele d'Ising en deux dimensions
Delay-Doppler Domain Tomlinson-Harashima Precoding for Downlink MU-MIMO OTFS Transmissions

In this paper, we investigate the delay-Doppler (D-D) domain Tomlinson-Harashima precoding (THP) for downlink multiuser multiple-input and multiple-output orthogonal time frequency space (MU-MIMO-OTFS) transmissions. Instead of directly applying THP based on the huge equivalent channel matrix, we propose a simple implementation of THP that does not require any matrix decomposition or inversion. Such a simple implementation is enabled by the DD domain channel property, i.e., different resolvable paths do not share the same delay and Doppler shifts, which makes it possible to pre-cancel all the DD domain inter-ference in a symbol-by-symbol manner. We also demonstrate the theoretical results on the sum-rate performance of the proposed scheme. In particular, we show that the sum-rate of the proposed scheme increases logarithmically with the number of antennas, while increases linearly with the number of users. Our numerical results align well with our findings and also verify the effectiveness of the proposed scheme.
Connect Your UAV to the Cloud Using Urban 4G and 5G Cellular Networks: Performance Evaluation and Comparison

Use of unmanned aerial vehicles (UAVs) for urban services has been an exciting topic in today's urban planning and management research. Most UAVs use unlicensed spectrum for remote control and communications. Their communication distance is limited within a few hundred meters; and the control channel is prone to interference. In comparison, urban cellular networks can cover a wide urban area with a low chance of interference, making them a viable alternative for UAVs on services that require real-time and reliable connections. In this article we provide a performance evaluation of this concept. We first address some key challenges in designing and implementing a UAV platform connected to cellular networks. The cellular networks include 4G and 5G. Then we use the platform to test the actual performance of 4G and 5G in urban areas with a focus on their signal strength and packet delay. A comparative analysis reveals the benefits 4G and 5G can bring to UAVs in urban flights.
A Multi-objective Model for Misbehavior Detection in IoV

As the key element of the Intelligent Transportation System, Internet of Vehicles (IoV) is expected to reduce the traffic congestion and improve the road safety. For road safety, connected vehicles need to broadcast basic safety messages frequently, which contain location, speed, acceleration, steering information, etc. Due to the wireless environment and ad-hoc nature, the vehicular communications are vulnerable to various attacks. It is vital to protect the correctness of the exchanged messages, in which the misbehavior detection mechanisms play an important role. In this paper, we propose a data-centric misbehavior detection method based on a multi-objective learning model. Different from current machine learning based misbehavior detection system, our work exploits a new direction to improve the detection performance by maximizing the recall and specificity. To show the effectiveness of the proposed model, we conduct experiments on the VeReMi Dataset and compare the detection results with existing approaches.
Deep Reinforcement Learning for Dependency-aware Microservice Deployment in Edge Computing

Recently, we have observed an explosion in the intellectual capacity of user equipment, coupled by a meteoric rise in the need for very demanding services and applications. The majority of the work leverages edge computing technologies to accomplish the quick deployment of microservices, but disregards their inter-dependencies. In addition, while constructing the microservice deployment approach, several research disregard the significance of system context extraction. The microservice deployment issue (MSD) is stated as a max-min problem by concurrently evaluating the system cost and service quality. This research first analyzes an attention-based microservice representation approach for extracting system context. The attention-modified soft actor-critic method is proposed to the MSD issue. The simulation results reveal the ASAC algorithm's priorities in terms of average system cost and system reward.
Sufficient Conditions for Optimality in Finite-Horizon Two-Player Zero-Sum Hybrid Games

A finite-horizon two-player zero-sum game under dynamic constraints given in terms of hybrid dynamical systems is formulated in this paper. Sufficient conditions that consist of a hybrid version of the Hamilton-Jacobi-Isaacs equations are provided to guarantee that a pure strategy is a saddle-point equilibrium for the game. It is shown that when the players select the optimal strategy, the value function can be evaluated without needing computing solutions. Using this framework, a finite-horizon optimal control problem under an adversarial action with decision-making agents exhibiting hybrid dynamics is addressed.
A Distributed Algorithm for Controlling Continuous and Discrete Variables in a Radial Distribution Grid

The increased integration of distributed energy resources (DERs) in the distribution network with intermittent generation profiles will likely make voltage regulation a difficult task. However, DERs bring both challenges and opportunities, as they can provide renewable forms of local energy and act as voltage regulating components. The DERs are usually interfaced with power electronic devices, in which both their active and reactive power outputs can be regulated and treated as continuous control variables. In contrast, other voltage regulatory devices such as On-Load Tap-Changing (OLTC) transformers are often controlled by making discrete tap changes. Thus, appropriate control strategies are required to control and coordinate the DERs with other voltage regulatory devices. In this work, a distributed control strategy based on the Alternating Direction Method of Multipliers (ADMM) is developed, which controls both the continuous and the discrete variables in a distribution grid. The proposed control strategy is compared to a centralized and a local control architecture, where optimal parameters have been computed for the local controllers. Finally, a simulation study is made for the three different control architectures using a modified CIGRE medium voltage network. The results showed significant improvements in the daily voltage profiles while also reducing the power losses by over 30% when using an optimal control strategy.
A descriptive study of adult student preferences for technology-enabled learner support amid the COVID-19 Pandemic

Although online distance education provides adult learners with an opportunity for lifelong learning, technology-enabled learner support remains a challenge. The COVID-19 pandemic introduced additional complications. The purpose of the current study was to determine which form of technology-enabled academic learner support students would prefer, and to assess their readiness for information and communications technology (ICT), be that under normal conditions or amid the pandemic. The study collected data using an online survey among fourth-level students enrolled in a research proposal module. The respondents indicated that, under normal, healthy conditions, they would prefer webinars to face-to-face learner support. Under COVID-19 conditions, respondents still ranked webinars first, online question and answer (Q&A) sessions second, vodcasts third, and lecturer-recorded videos made available via YouTube, fourth. The remainder indicated a preference for other forms of learner support. The respondents indicated their timing preference as a month before each of the assignment due dates or alternatively a week prior to the due date, with the remainder preferring more frequent online learner support. The practical implications of the study are that webinars, online Q and A sessions, and vodcasts should be actively considered for use instead of face-to-face classes for students at the University of South Africa (UNISA).
Organization of rainfall by an unstable jet with an application to African waves

Abstract African waves are believed to originate as shear instabilities, although in certain cases rainfall is organized so that latent heating contributes to wave growth. What determines whether the shear instability can organize rainfall is considered here; in particular, why African waves organize rainfall mainly during the late summer, despite the regular occurrence of shear instability and rainfall throughout the season.During GATE, moisture convergence by the waves was also largest toward the late summer. It is assumed that an African wave will organize rainfall if it converges moisture—as measured by the ascent at the top of the moist layer—with sufficient amplitude. The wave amplitude is specified at some level beneath the 600-mb African jet, whose instability is a plausible source of the wave. The ascent is calculated using the quasigeostrophic potential vorticity and thermodynamic equations, and depends on the zonal wind separating the unstable jet from the top of the moist layer.Before turning .