Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Inter-Constellation Rotation Based on Minimum Distance Criterion for Uplink NOMA

Non-orthogonal multiple access (NOMA) has recently emerged as a promising multiple access technique for LTE enhancements and 5G due to its better cell coverage and potentially higher throughput than the traditional orthogonal multiple access. In its simplest form that superimposes only two constellations in the power domain, we investigate the optimal inter-constellation rotation based on the minimum distance (MD) criterion over an uplink NOMA scenario in this paper. In comparison with existing work that adopts constellation-constrained mutual information (MI) as the optimization criterion and hence can only determine the optimal inter-constellation rotation angle numerically, closed-form expressions for the MD-maximizing inter-constellation rotation angle as well as its achievable largest minimum distance among joint constellation points can be obtained for usual combinations of modulation schemes. Since the MD-maximizing inter-constellation rotation has nothing to do with the signal-to-noise ratio (SNR), as contrary to the MI-maximizing inter-constellation rotation, a more feasible SNR-independent system formulation can be established. Simulation results show that the MD-maximizing design not only can maintain a much more robust error performance than the SNR-dependent MI-maximizing design but achieves a better fairness among users.
Experimental Evaluation of LoRaWAN in NS-3

Long Range Wide Area Networks (LoRaWAN) is a proprietary network technology devised for the long range connectivity of massive number of low power network devices. This work gives an overview of the key aspects of LoRaWAN technology and presents results that we achieved via extensive evaluation of Class A LoRaWAN devices in different network settings using the state-of-the-art network simulator (NS-3). At first, we focus on a single device and its mobility. We further undertook evaluations in an extended network scenario with a changing number of devices and traffic intensity. In particular, we evaluate the packet delivery ratio (PDR), uplink (UL) throughput, and sub-band utilization for the confirmed and unconfirmed UL transmissions in different environments. Our results give new insights for future efforts to optimize the LoRaWAN performance for different large scale Internet of Things $(\mathrm {I}\mathrm {o}\mathrm {T})$ applications with low power end devices.
Distributed Formation with Diffusive Obstacle Avoidance Control in Coordinated Mobile Robots

We study a multitasking control problem in a group of networked mobile robots where they simultaneously have to reach a prescribed shape/formation and to move as a whole towards a desired position while avoiding any obstacles encountered during transition. We propose a modular control framework that comprises of a distributed controller for achieving the formation task and obstacle avoidance task and a centralized controller for solving the group motion task. In particular, we can independently design a controller for each individual task and obtain the unified distributed controller by a linear combination of these controllers. We prove that the unified controller solves the aforementioned multitasking control problem. An illustrative example is given to show the efficacy of our proposed approach.
Synchrophsor Estimation Based on The Combination of The Modulated Sliding DFT and Taylor’s Series Expansion

In this paper, a new synchrophasor estimation algorithm is designed to meet the accuracy requirements of the IEEE Std C37.118. 1TM -2011 and its amendment IEEE Std C37.118. 1aTM -2014. This estimator is constructed by incorporating the Modulated Sliding Discrete Fourier Transform MSDFT in the four and six-parameter models of the complex Taylor expansion. The proposed algorithm is found to be accurate with less computational workload than the traditional one (based on Discrete Fourier Transform, DFT) under steady state and dynamic conditions.
Deep Transfer Learning for Segmentation of Anatomical Structures in Chest Radiographs

Segmentation of anatomical structures in Chest Posterior-Anterior Radiographs is a classical task on biomedical image analysis. Deep Learning has been widely used for detection and diagnosis of illnesses in several medical image modalities over the last years, but the portability of deep methods is still limited, hampering the reusability of pre-trained models in new data. We address this problem by proposing a novel method for Cross-Dataset Transfer Learning in Chest X-Ray images based on Unsupervised Image Translation architectures. Our Transfer Learning approach achieved Jaccard values of 88.20% on lung field segmentation in the Montgomery Set by using a pre-trained model on the JSRT dataset and no labeled data from the target dataset. Several experiments in unsupervised and semi-supervised transfer were performed and our method consistently outperformed simple fine-tuning when a limited amount of labels is used. Qualitative analysis on the tasks of clavicle and heart segmentation are also performed on Montgomery samples and pre-trained models from JSRT dataset. Our secondary contributions encompass several experiments in anatomical structure segmentation on JSRT, achieving state-of-the-art results in lung field (96.02%), heart (89.64%) and clavicle segmentation (87.30%).
Adaptive Scheme for Pathological Oscillations Disruption in a Delayed Neuronal Population Model

Motivated by improved ways to disrupt brain oscillations linked to Parkinson's disease, we propose an adaptive output feedback strategy for the stabilization of nonlinear time-delay systems evolving on a bounded set. To that aim, using the formalism of input-to-output stability (IOS), we first show that, for such systems, internal stability guarantees robustness to exogenous disturbances. We then use this feature to establish a general result on scalar adaptive output feedback of time-delay systems inspired by the “($\sigma$-modification” strategy. We finally apply this result to a delayed neuronal population model and assess numerically the performance of the adaptive stimulation.
Using Machine Learning to Predict Path-Based Slack from Graph-Based Timing Analysis

With diminishing margins in advanced technology nodes, accuracy of timing analysis is a serious concern. Improved accuracy helps to reduce overdesign, particularly in P&R-based optimization and timing closure steps, but comes at the cost of runtime. A major factor in accurate estimation of timing slack, especially for low-voltage corners, is the propagation of transition time. In graph-based analysis (GBA), worst-case transition time is propagated through a given gate, independent of the path under analysis, and is hence pessimistic. The timing pessimism results in overdesign and/or inability to fully recover power and area during optimization. In path-based analysis (PBA), pathspeci? c transition times are propagated, reducing pessimism. However, PBA incurs severe (4X or more) runtime overheads relative to GBA, and is often avoided in the early stages of physical implementation. With numerous operating corners, use of PBA is even more burdensome. In this paper, we propose a machine learning model, based on bigrams of path stages, to predict expensive PBA results from relatively inexpensive GBA results. We identify electrical and structural features of the circuit that affect PBA-GBA divergence with respect to endpoint arrival times. We use GBA and PBA analysis of a given testcase design along with arti? cially generated timing paths, in combination with a classi? cation and regression tree (CART) approach, to develop a predictive model for PBA-GBA divergence. Empirical studies demonstrate that our model has the potential to substantially reduce pessimism while retaining the lower turnaround time of GBA analysis. For example, a model trained on a post-CTS and tested on a post-route database for the leon3mp design in 28nm FDSOI foundry enablement reduces divergence from true PBA slack (i.e., model prediction divergence, versus GBA divergence) from 9.43ps to 6.06ps (mean absolute error), 26.79ps to 19.72ps (99th percentile error), and 50.78ps to 39.46ps (maximum error).
Development of SIW LWA from Non-Uniform CRLH Unit Cells with SLL Reduction

In this communication, we propose an improved SIW LWA based on non-uniform CRLH unit cells. The antenna consists of 5 sections with changing the number of interdigital fingers of the unit cells. This configuration allows the reduction of the side lobe level. In addition, a high gain value around 15 dB is obtained over the frequency bandwidth from 9 to 13.4 GHz. The main beam of the proposed design scans from −52 to 64° and a high radiation efficiency with an average of 95% is found on the frequency band.
High Performance InGaAs Gate-All-Around Nanosheet FET on Si Using Template Assisted Selective Epitaxy

We report InGaAs gate-all-around nanosheet NFETs on Si substrate using template-assisted-selective-epitaxy (TASE) and a gate-last process with thermal budget advantages. Compared to our early report of the TASE process, in this paper we demonstrate that TASE can be scaled to a channel thickness of ∼10 nm, which enables short gate devices without significant leakage. The defects and composition of the fabricated nanosheet FETs are also investigated. Enabled by this VLSI compatible process and a novel high-pressure deuterium annealing process, our 39 nm-$L_{g}$ device shows a peak $g_{m}$ of $1.37 \ \text{mS}/\mu \mathrm{m}$, a subthreshold slope in saturation of 72 mV/decade, and an $I_{on}$ of $355 \ \mu \mathrm{A}/\mu \mathrm{m}$ at 0.5 V $V_{gs}$, the highest among reported sub-50 nm-$L_{g}$ III-V FETs on Si.
Research of Harmonic Effects on Core Loss in Soft Magnetic Composite Materials

A magnetic properties measurement system with the frequency-domain feedback control method is developed in this paper. The system can generate non-sinusoidal waveforms consisting of harmonics with variable orders, contents, and phase angles. Core loss measurement of soft magnetic composite materials under both the sinusoidal and non-sinusoidal excitations is performed. In terms of the harmonic order, content, and phase angle, the effects of harmonic for core loss are analyzed. The frequency-domain Bertotti equations are used to predict the core loss and then compared with the experimental results. The applicability of this method for non-sinusoidal waveforms is also discussed in detail.
Hybrid Fuzzy-Statistical System for Learning Analytics

Learning analytics, being a very active field of research, recently has a number of significant contributions where data related to students' demographics, learning and behaviour is being used to predict student performance. In higher education institutes most of these models are based on statistical and machine learning techniques. The biggest challenge that can be seen in almost all these research efforts is the ineffective portability of prediction model across multiple cohorts even in the same institute. We propose to use hybrid fuzzy-statistical model for predicting student performance based on behaviour related predictors in a particular cohort. We have recently started this project so preliminary details of our model based on Fuzzy Expert System are provided. The model is proposed for enhancing traditional statistical models with Fuzzy SetslFuzzy Logic to overcome the limitations of commonly used statistical techniques and deliver a stronger system with combined advantages of both domains.
Continuous-Time Gain-Scheduled $H_{\infty}$ Controllers with Causality for Scheduling Parameters via Parameter-Dependent Lyapunov Functions

When continuous-time Gain-Scheduled Output Feedback (GSOF) controllers are designed via Parameter-Dependent Lyapunov Functions (PDLFs), the GSOF controllers, in general, depend on not only the current scheduling parameters but also the derivatives of scheduling parameters. However, it is obvious that the derivatives of scheduling parameters are not available in real world; that is, the designed GSOF controllers cannot be implemented to practical systems. We therefore address the design problem of GSOF controllers via PDLFs while causality of scheduling parameters in GSOF controllers is kept. For this problem, we propose a new formulation in which the causality of scheduling parameters is kept by over-bounding the term causing the causality problem. It is also shown that our method is no more conservative than an existing method in the literature. A numerical example is included to demonstrate the usefulness and effectiveness of our method.
Distributed Control for Waterfilling of Networked Control Systems with Coupling Constraints

In this paper we present a distributed control approach for the multi-user multi-constrained waterfilling. This a specific category of distributed optimization for Networked Control Systems (NCSs), where agents aim at optimizing a non-separable global objective function while satisfying both local constraints and coupling constraints. Differently from the existing literature, in the considered setting we adopt a fully distributed mechanism where communication is allowed between neighbors only. First, we formulate a general multi-user waterfilling-structured optimization problem including coupling constraints, which may represent many engineering distributed control problems. Successively, we define a low-complexity iterative distributed algorithm based on duality, consensus and fixed point mapping theory. Finally, applying the technique to a simulated case referring to the electric vehicles optimal charging problem, we show its effectiveness.
Dynamically Controllable Terahertz Graphene Y-Circulator

A new type of the graphene-based three-port circulator for the terahertz (THz) region is suggested and analyzed theoretically. The cross section of the component presents a three-layer structure consisting of the layers of graphene, silica, and silicon. The in-plane figure of the circulator consists of a circular graphene resonator and three waveguides symmetrically connected to it. The resonator is magnetized normally to its plane by a dc magnetic field. The working principle of the device is based on the dipole resonance of the magnetized graphene resonator. The numerical simulations are fulfilled by a full-wave computational program. For the analysis of the circulator, the analytical temporal coupled mode theory and circuit theory are also used. Numerical calculations demonstrate the isolation greater than −15 dB and the insertion loss better then −3 dB within the bandwidth of about 7.4% and the central frequency of 5.38 THz. At this frequency, the minimum of the insertion losses is −2.65 dB, and the maximum of isolation is −40 dB. The biasing dc magnetic field is 0.45 T, and the Fermi energy is <inline-formula> <tex-math notation="LaTeX">${ \epsilon _{\mathrm F} = 0.15}$ </tex-math></inline-formula> eV. In our example, the diameter of the graphene resonator is <inline-formula> <tex-math notation="LaTeX">$1.2~{{\mu } }\text{m}$ </tex-math></inline-formula> which corresponds to <inline-formula> <tex-math notation="LaTeX">$0.0215 { \lambda _{0}}$ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">${ \lambda _{0}}$ </tex-math></inline-formula> is the operating wavelength in the free space. The influence of geometrical and physical parameters of the circulator on its characteristics is discussed. The frequency band can be enlarged at the expense of higher dc magnetic field. The central frequency of the circulator can be adjusted by the Fermi energy of graphene through the electrostatic gating, and it allows one to control dynamically the circulator responses.
Regression Modelling for Stress Detection in Humans by assessing most prominent Thermal Signature

Many previous studies have demonstrated non invasive and non contact methods for stress detection. However, very few studies are reported to identify stress states at individual level or regression modelling for identifying prominent features for stress detection. We, here, report a multinomial logistic regression model for stress detection based on Heart Rate Variability (HRV), for detecting stress in students, by assessing most prominent thermal signature. It models various thermal features and predicts the stress level. Thermal signatures are used to establish stress markers such as Heart Rate, HRV, so as to detect the most prominent signature for stress detection in students during competitive examination. These parameters are extracted from 10 participants and further used to classify psychophysiological state i.e. stress state, of students. Age, gender, CGP A of the students are other factors considered in this work. Likelihood ratio test is performed to find the significance of each predictor variable. Odds ratio test is made to find most significant thermal signature. Prediction classification rate is also made to evaluate the regression model. Our work shows high confidence w.r. t ground truth values.
A Multi-channel Episodic Memory Model for Human Action Learning and Recognition

Human actions can be realized by observing the trajectories of skeleton joints. In this paper, we propose an unsupervised episodic memory learning model for skeleton based action learning and recognition. The proposed model, Multi-channel Episodic Memory Adaptive Resonance Theory (McEM-ART), consists of three layers: short term memory, working memory and Episodic memory. The short term memory layer is formed by multiple ART networks to obtain sensory data and cluster them into neurons in working memory layer. Instead of obtaining the whole skeleton as an input, we divide the human skeleton into three parts, upper part body, main body and lower part body. Each of them is then feed to McEM-ART short term memory layer for learning. Episodic memory layer extracts novel events and encodes spatio-temporal connection between them as episodes by generating cognitive neurons incrementally for action recognition. Comparing with previous works, McEM-ART further integrates a novel memory anticipation functions for encoding crucial events and episodes and recalling them using partial and inexact cues. Experimental results demonstrate that McEM-ART is capable of clustering human skeleton data into event neurons, encoding sequence of activation events as episode neurons for action recalling and recognition.
Analytical Modeling and Experimental Validation of Rotor Harmonic Eddy-Current Loss in High-Speed Surface-Mounted Permanent Magnet Motors

The temperature rise of the rotor caused by harmonic eddy-current loss constrains the performances of high-speed permanent magnet (PM) motors. Excessive temperature rise can even result in irreversible demagnetization in the PM. An analytical model for the rotor harmonic eddy-current loss of high-speed surface-mounted PM motors in consideration of eddy-current reaction fields, harmonic currents, stator slotting, and different rotation states of the rotor is proposed, which can be utilized to reliably calculate and evaluate the rotor harmonic eddy-current loss. The accuracy and effectiveness of this analytical model are validated by using the finite-element method and a proposed experimental method.
Load Balance-Aware Multi-Core Parallel Routing for Large-Scale FPGAs

Routing is probably the most time-consuming stage in the FPGA compilation flow. While parallelization has the potential to reduce the routing time, load imbalance still arises in parallel routing process, resulting in the degradations of speedup and quality of results. Furthermore, when scaling the number of processing cores, load imbalance will become more severe in parallel routing. In this paper, we explore load balance-aware parallel routing to obtain significant speedup. To study load balance problem, we implement a basic MPI-based parallel routing framework on multi-core distributed-memory platform. This framework provides two dynamic partitioning algorithms and both of them can automatically maintain the load balance of parallel routing at runtime. The first is the heuristic algorithm and it is able to assign more nets to fast MPI processes by removing an equal number of nets from slow MPI processes. The second algorithm selects the re-partitioning of the nets for all the MPI processes at each iteration, further leading to a good load balance during parallel routing. The key idea of these two algorithms is to analyze the behaviours of previous iteration to guide the parallelization of next iteration. Results show that the effectiveness and efficiency of the two algorithms in multi-core distributed-memory parallel routing framework. Notably, our parallel router can achieve about 6x speedup on average using 8 MPI processes, comparing to the serial router. This is a 1.2x improvement over a state-of-the-art multi-core parallel router.
Dynamical Model of Overconfidence Phenomena Due to ZE-Type Confirmation Bias

This paper presents a dynamical model of overconfidence phenomena due to a confirmation bias. The model assumes an informational environment where a person updates one's belief about a state of the world through signals which may be misread and ignored due to a confirmation bias. A general type of confirmation bias, called ZE-type, is introduced and formalized using a model of noisy channels between signals and perceptions. It contains two subtypes of confirmation biases, called Z-and E-types, as its special cases. Persons suffering from Z-type receive a signal and perceive it incorrectly as a different signal with a certain probability, while those suffering from E-type fail to receive a signal with a certain probability. This paper derives a key factor, called a filtering coefficient, which plays a central role in analyzing the overconfidence phenomena. The analytical results suggest that the substantial part of the overconfidence phenomena due to the confirmation bias is regarded as a switched system with multiple linear time-invariant subsystems.
Ridge Gap Waveguide Multilevel Sequential Feeding Network for High-Gain Circularly Polarized Array Antenna

This paper proposed a wideband, efficient, and high gain circularly polarized (CP) array antenna based on the gap waveguide technology for 30 GHz frequency band applications. The antenna subarray is a $2 \times 2$ slot groove gap waveguide-based cavity-backed antenna. The antenna is fed by a ridge gap waveguide (RGW). Two corners of the radiating slots are truncated to obtain CP radiation, and some corrugations are introduced to the radiating layer to suppress the surface waves between neighbor subarrays. The planar 16-way feeding network based on RGW is utilized to feed the subarrays. The feeding network is multilevel sequential phased with a quadrature phase shift. To validate the array antenna performance, a prototype of the antenna has been manufactured using standard milling technology. The measured reflection coefficient bandwidth is about 22% covering from 28.3 to 35.3 GHz, and a measured 3 dB axial ratio bandwidth of about 21.8% is achieved from 27.8 to 34.6 GHz. The maximum measured gain is 23.5 dBi which is occurred at 30.75 GHz.
A Generalized Digital Predistortion Model Based on Artificial Neural Networks

In this paper, the CPWL-based DPD model in is extended to a DPD model based on artificial neural networks, where the mapping from $\mathbb{R}^{n}$ to $\mathbb{C}^{m}$ in CPWL functions is implemented by ANNs. The linearization performance of ANNs model with different activation functions is also validated with platform tests. The test results show the ANNs-based DPD model with ABS activation function can achieve similar performance as the CPWL-based DPD model. The performance of ANNs model with other activation functions (PWL, ReLU and tanh functions), is $1\sim 2\text{dB}$ worse than that of the ABS activation function.