Miracle-dz/UniFAR-FTUs huggingface dataset:
2-D DOA Estimation for Incoherently Distributed Sources Considering Mixed Circular and Noncircular Signals in Massive MIMO System

In practical environment, the received signals cannot be regarded as point sources due to the existence of signal scattering phenomena. Incoherently distributed source model is suitable for this realistic scenarios. The incoherently distributed DOA estimators based on noncircular property can provide a better performance. However, these estimators are not fit for the mixed circular and noncircular signals. In this paper, a conjugate generalized shift invariance algorithm is proposed for incoherently distributed sources comprising of circular and noncircular signals. The general array manifold (GAM) model of incoherently distributed source is established by one-order Taylor series approximation. To utilize the noncircular property, each incoherently distributed circular source is separated as two strictly incoherently distributed noncircular sources. A generalized shift invariance property is derived by dividing the whole uniform rectangular array (URA) into four overlapping subarrays. Then, a modified conjugate generalized least square (CGLS) ESPRIT algorithm is proposed to obtain the central DOAs, which can improve the estimation accuracy without any spectrum searching. In addition, a pair matching method and an angular spreads estimation algorithm are also designed based on the GAM model for this mixed sources scenario. Furthermore, the Cramer–Rao bound (CRB) of proposed algorithm is given and analyzed under the mixed incoherently distributed sources scenario. Numerical results illustrate the validity of the proposed algorithm in this realistic scenario.
3-D Closely Spaced Dual-Band Frequency Selective Surface Based on Square Waveguide

In this paper, a three-dimensional (3-D) frequency-selective surface (FSS) is proposed to realize two close passbands with high selectivity. In each unit cell, one modified parallel-plate waveguide (MPPW) propagation path and one I-shaped slots (ISS) propagation path are constructed to generate three transmission poles and two transmission zeros. The E-field analysis of transmission poles and zeros is provided to understand the operating principle of the proposed FSS. According to the simulation results, the proposed 3-D FSS can achieve two closely spaced passbands with the center frequency ratio of passbands 1.1. Furthermore, the proposed FSS exhibits stable frequency responses under a large variation of incident angles for dual polarizations.
Identification of Coherent Generators by Support Vector Clustering With an Embedding Strategy

Identification of coherent generators (CGs) is necessary for the area-based monitoring and protection system of a wide area power system. Synchrophasor has enabled smarter monitoring and control measures to be devised; hence, measurement-based methodologies can be implemented in online applications to identify the CGs. This paper presents a new framework for coherency identification that is based on the dynamic coupling of generators. A distance matrix that contains the dissimilarity indices between any pair of generators is constructed from the pairwise dynamic coupling of generators after the post-disturbance data are obtained by phasor measurement units (PMUs). The dataset is embedded in Euclidean space to produce a new dataset with a metric distance between the points, and then the support vector clustering (SVC) technique is applied to the embedded dataset to identify the final clusters of generators. Unlike other clustering methods that need a priori knowledge about the number of clusters or the parameters of clustering, this information is set in an automatic search procedure that results in the optimal number of clusters. The algorithm is verified by time-domain simulations of defined scenarios in 39 bus and 118 bus test systems. Finally, the clustering result of 39 bus systems is validated by cluster validity measures, and a comparative study investigates the efficacy of the proposed algorithm to cluster the generators with an optimal number of clusters and also its computational efficiency compared with other clustering methods.
Analysis and Verification of the Stability for PV Series-Connected Power Optimizer System

Photovoltaic (PV) energy generation gets more and more attention in the last decades. Among many structures of PV system, series-connected power optimizer (SCPO) system is regarded as a promising one. However, the cross-coupling effect introduced by the series-connected structure cannot be avoided, which would weaken the stability of the system, resulting in failure of operation of the system. Therefore, it is significant to analysis the stability of the series-connected system and suppress the effect by improving the closed-loop control strategy and compensation design. In this paper, the small-signal ac model consisting of three optimizers is derived and the criterions of stability and compensation design are proposed. Computer simulation and hardware experiment are used to verify the validity of the proposed theories.
Classification of Patients with Bipolar Disorder and Their Healthy Siblings from Healthy Controls Using MRI

Detection of Bipolar Disease (BD), one of the most common neuroanatomical abnormalities, using machine learning algorithms together with Magnetic Resonance Imaging (MRI) data has been widely studied. BD is a highly heritable disease, yet not all siblings tend to have it despite they might have similar genetic and environmental risk factors. In this paper, the classifications of two self-acquired data groups, namely 26BD patients and 38 unrelated Healthy Controls (HCs) as well as 27 Healthy Siblings of BD (BDHSs) and 38HCs are examined. Voxel-Based Morphometry (VBM) is utilized to segment and pre-process the MRI data. In order to obtain the morphological alterations in the Gray Matter (GM) and White Matter (WM) of data groups separately, a general linear model is configured and a two sample t-test based statistical method is used. The obtained differentiated voxels are considered as Voxel of Interests (VOIs) and using VOIs reduces the dimension of the original data into the number of VOIs. The effects of using different covariates (i.e. total intracranial volume (TIV), age, and sex) on classification of the two data groups have been studied for GM-only, WM-only, and their combination. Principle Component Analysis (PCA) is used to reduce the dimension of the extracted VOIs data and Support Vector Machine (SVM) with Gaussian kernel is taken into account as a classifier. The experimental results indicate that among three covariates, TIV provides better results for both data groups and the classification accuracies of the combination of GM and WM maps is higher than that of GM-only and WM- only for both groups. In BD and HC comparison, the highest classification accuracies of 70.3% for GM, 79.7% for WM, and 82.8% for fusion of extracted GM as well as WM are obtained. In BDHS and HC comparison, the highest classification accuracies of 72.3% for GM, 76.9% for WM, and 78.5% for fusion of extracted GM as well as WM are obtained.
Toward Adaptive and Scalable OpenFlow-SDN Flow Control: A Survey

Software-defined networking (SDN) is an emerging network architecture that promises to simplify network management, improve network resource utilization, and boost evolution and innovation in traditional networks. The SDN allows the abstraction and centralized management of the lower-level network functionalities by decoupling the network logic from the data forwarding devices into the logically centralized distributed controllers. However, this separation introduces new scalability and performance challenges in large-scale networks of dynamic traffic and topology conditions. Many research studies have represented that centralization and maintaining the global network visibility over the distributed SDN controller introduce scalability concern. This paper surveys the state-of-the-art proposed techniques toward minimizing the control to data planes communication overhead and controllers’ consistency traffic to enhance the OpenFlow-SDN scalability in the context of logically centralized distributed SDN control plane architecture. The survey mainly focuses on four issues, including logically centralized visibility, link-state discovery, flow rules placement, and controllers’ load balancing. In addition, this paper discusses each issue and presents an updated and detailed study of existing solutions and limitations in enhancing the OpenFlow-SDN scalability and performance. Moreover, it outlines the potential challenges that need to be addressed further in obtaining adaptive and scalable OpenFlow-SDN flow control.
Co-analysis of 2-Coupled Superconducting Transmission Lines and Josephson Junctions Based on FDTD

As the scale of RSFQ circuits increasing, signal integrity issues of interconnects become critical. It is necessary to analyze time-domain response of coupled passive transmission lines in RSFQ circuits. In this paper, a method based on finite-difference time-domain (FDTD) method and the Newton-Raphson iterative algorithm is proposed to co-analyze 2-coupled passive transmission lines and Josephson junctions. The FDTD method is used to solve the telegraph equations of coupled passive transmission lines. And the Newton-Raphson iterative algorithm is adopted to solve the nonlinear equations of the boundary conditions. The simulation and numerical results show that the proposed co-analysis method has the same accuracy as WRspice and higher efficiency than WRspice.
Compact Multi-Band Differential Bandpass Filters Using Microstrip Multi-mode Resonators

Three types of miniaturized multi-band differential bandpass filters (BPFs) using microstrip multi-mode resonators are presented in this paper. The first one is a dual-band differential BPF using four-mode stepped-impedance square ring loaded resonators (SI-SRLRs) with a controllable frequency separation between its common-mode (CM) and differential-mode (DM). Next, by adding two additional open-circuited stubs to the square ring of the SI-SRLR, a new six-mode SI-SRLR is then obtained. By using two coupled six-mode resonators, a tri-band differential BPF is designed. Finally, to reduce the insertion loss due to the metal loss, high-temperature superconducting (HTS) technology is applied to develop a fourth-order HTS dual-band differential BPF using modified SRLRs. All these BPFs shown compact size and favorable performance.
Off-the-person ECG Biometrics Using Convolutional Neural Networks

The paper introduces a two-stage approach for ECG based biometric identification, using signals acquired on fingers. A 2D/3D phase-space representation of the original raw data is first computed, followed by an image oriented classification step using convolutional neural networks. Experimental results reveal that the corresponding identification performances are in line with existing results, while the off-person acquisition setup makes the proposed approach suitable for wearable/mobile applications.
Pattern Reconfigurable Metasurface Antenna with Five States

This paper introduces a low profile radiation pattern reconfigurable antenna with five states using metasurface superstrate. The proposed antenna consists of a planar slot coupling antenna with an array of metallic rectangular patches with three sets of switches which can be view as a reconfigurable metasurface superstrate. There are five operating states of radiation patterns generated from the proposed antenna by controlling the working states of the switches. The radiation pattern would steer to 0°, ±30° and ±90° with respect to the normal direction in H plane. Furthermore, the overlap simulated -10 dB impedance bandwidth associated with all pattern modalities is 9.8% from 5.8 to 6.4 GHz. This reconfigurable antenna can form a C-band beam steering array with a total profile of 0. 08λ0 (4mm) at its centre operating frequency.
Mutual Coupling Reduction for Linearly Arranged MIMO Antenna

In the present paper, a new technique to reduce mutual coupling between linearly arranged patch antenna array is presented. Antennas are arrange in close space with edge-to-edge separation of 0. 096λ and center-to-center spacing of 0. 29λ, where λ is the free space wavelength. Two separate rectangular shape and T-shape parasitic elements are used to cancel the surface current (SC) in order to reduce mutual coupling. The parasitic elements are placed in between the antennas with an extension of the T-shape towards the boundary of patches. The obtained results indicate that mutual coupling for two consecutive antennas are less than -15 dB over the entire bandwidth.
Compact Tri-Band Bandpass Filter Based on Ring Multi-Mode Resonator

A novel tri-band bandpass filter (BPF) based on ring multi-mode resonator (R-MMR) is proposed in this paper. The filter consists of an R-MMR and a pair of parallel coupled lines. Because the R-MMR is symmetrical, its resonant characteristics can be analyzed by using odd- and even-mode analysis method. When a couple of stubs are loaded on the ring resonator, six transmission poles and four transmission zeros are created, and a tri-band BPF is realized by employing a couple of parallel coupled lines. To miniaturize the size, the R-MMR is folded. A prototype is designed, fabricated and measured. Good agreement is obtained between the simulated and measured results.
Malware Detection Using 1-Dimensional Convolutional Neural Networks

This work introduces a highly accurate and efficient malware detection system based on 1-dimensional convolutional neural networks. The system takes as input a binary file and classifies it as malicious or benign. There is minimal pre-processing of the binaries, with features discovery left to the network during training. A crucial difference with other convolutional neural networks (CNN) based approaches is the use of 1-dimensional convolutions; this methodological choice is shown to have significant positive consequences for the detector. In order to compare the detector with state-of-the-art techniques a TF-IDF based benchmark malware detector is also implemented: experiments show an improved accuracy of the proposed CNN detector while maintaining similar training times. The system is also compared, on a publicly available dataset of 11130 binaries, with an existing embedding based CNN detector. The proposed system outperforms, both in accuracy and training time the embedding based CNN.
Multistage Model Predictive Control with Online Scenario Tree Update using Recursive Bayesian Weighting

This work deals with a nonlinear multistage model predictive control (MPC) formulation, where the future propagation of the uncertainty in the prediction horizon is represented via a discrete scenario tree. The scenario tree is often generated using finite realizations of the uncertainty sampled from an uncertainty set or a probability distribution function. Once the scenarios are chosen, the scenario tree is often kept fixed for all the iterations. In this paper, we propose to update the different discrete realizations of the uncertainty in the scenario tree using a recursive Bayesian weighting approach. We show that by gradually shrinking the uncertainty set, we can further reduce the conservativeness of the closed-loop solution. The effectiveness of the proposed method is demonstrated using an oil and gas production optimization case study.
Risk-Coherent $\mathcal{H}_{\infty}$ -optimal Filter Design Under Model Uncertainty with Applications to MISO Control

This work presents a framework to address the problem of designing discrete-time LTI (linear and time-invariant) multiple-input and multiple-output (MIMO) filters, aiming to optimize the performance of a system when model uncertainty is considered. Additionally, we present an interesting application to control design for disturbance rejection under model uncertainty. To account for this uncertainty we employ coherent measures of risk, which are a family of measures in theory of risk. We particularly discuss which measures are suitable by comparing the conditional value-at-risk (CVaR) to other three common designs. Using a scenario approach, we derive a convex optimization problem based on linear matrix inequalities (LMIs), whose solution minimizes the risk of falling into poor $\mathcal{H}_{\infty}$ performance. Finally, we present an application to multiple-input and single-output (MISO) control design under model uncertainty in the auto-covariance function of the output noise, comparing approaches minimizing different notions of risk.
Testbed to experiment with LTE WiFi Aggregation

Due to the exponential growth in data usage that mobile networks have been experiencing in the past years, improvements in the performance and capacity of these networks have become key for the next mobile generation. Traffic aggregation at different layers in the radio interface increases the bandwidth and reliability for specific users. A promising aggregation approach for future networks is LWIP, which consists of LTE-WLAN aggregation at the IP level, just above the PDCP layer. It allows integrating WiFi with the LTE access network for the user plane data, making use of the legacy WLAN infrastructure. This paper presents a testbed where researchers can test different aggregation policies with real LTE and WiFi traffic making use of our own LWIP implementation.
Compositional Synthesis of not Necessarily Stabilizable Stochastic Systems via Finite Abstractions

In this paper, we propose a compositional framework for the construction of finite abstractions (a.k.a. finite Markov decision processes (MDPs)) for networks of not necessarily stabilizable discrete-time stochastic control systems. The proposed scheme is based on a notion of finite-step stochastic simulation function, using which one can employ an abstract system as a substitution of the original one in the controller design process with guaranteed error bounds. In comparison with the existing notions of simulation functions, a finite-step stochastic simulation function needs to decay only after some finite numbers of steps instead of at each time step. In the first part of the paper, we develop a new type of small-gain conditions which are less conservative than the existing ones. The proposed condition compositionally quantifies the distance in probability between the interconnection of stochastic control subsystems and that of their (finite or infinite) abstractions. In particular, using this relaxation via finite-step stochastic simulation functions, it is possible to construct finite abstractions such that stabilizability of each subsystem is not necessarily required. In the second part of the paper, for the class of linear stochastic control systems, we construct finite MDPs together with their corresponding finite-step stochastic simulation functions. Finally, we demonstrate the effectiveness of the proposed results by compositionally constructing finite MDP of a network of four subsystems such that one of them is not stabilizable.
Physical Layer Security in Cognitive Millimeter Wave Networks

Millimeter wave (mmWave) communications and cognitive radio networks are two key technologies to improve the spectral efficiency. For a cognitive mmWave wiretap network in which randomly located eavesdroppers may reside in the signal beam of the secondary network, confidential messages could still be wiretapped. Moreover, the primary user may be covered by the main lobe generated by directional beamforming of the secondary network, the interference to the primary network is an inevitable problem. Motivated by these challenges, in this paper we investigate physical layer security of cognitive mmWave wiretap networks, where the secondary transmitter (SU-Tx) sends confidential messages to a secondary receiver (SU-Rx) under the interference temperature constraint of a primary receiver (PU-Rx). Thereby, two different secure transmission schemes, namely complete transmission (CT) scheme and threshold-based (TB) scheme are introduced to investigate the reliability and security performance of SU-Rx. Specifically, under stochastic geometry framework, we derive closed-form expressions for the connection outage probability (COP), secrecy outage probability (SOP) and secrecy throughput (ST) to characterize the performance for both schemes. The numerical and analysis results reveal that the reliability and security of SU-Rx in both schemes can be balanced by the interference temperature constraint of PU-Rx. Besides, when the SU-Rx requires both security and reliability, TB scheme shows its superiority, and CT scheme is more suitable for the scenario where SU-Rx only requires higher security.
Game-theoretic robust energy coordination for a neighbourhood of smart homes

Scalable coordination algorithms for neighbourhoods of households featuring electric vehicles (EVs) and embedded renewable generation are required in order to achieve safe and efficient operation of local distribution networks without excessive centralization of control decisions. This paper presents a non-cooperative algorithm to coordinate multi-period EV charging amidst uncertainty affecting both the charging dynamics and the intermittent renewable energy supply. The strategy space includes not only nominal EV charging plans, but also a causal system of linear responses to the forecast errors of renewable infeeds. In a setting where the price of electricity from the main grid has a fixed component and a component that depends on the aggregate purchase requirements of the neighbourhood, we prove a condition under which convergence to the unique Nash equilibrium of the associated game is guaranteed. The algorithm relies on an update rule known as the Krasnoselskij iteration, and the convergence proof is based on the non-expansivity property of an associated operator. We demonstrate the algorithm for a group of 100 households.
Duobinary Modulation for Visible Light Communications

The paper proposes and experimentally investigates the performance of the duobinary transmission technique for a highly bandlimited VLC system. By adding a controlled amount of inter-symbol interference (ISI) into the transmitted signals through the use of pulse shaping filters, data rate can be doubled within the same signal bandwidth. To gain full insight into duobinary signalling, the so-called modified binary scheme is also tested. Bit error rate (BER) performance of both systems is measured for a range of data rates and compared to BERs for ideal binary and traditional on-off keying non-return to zero (OOK-NRZ) formats, across the same physical link. We show the duobinary system can support higher bit rates and lower BER than OOK-NRZ while requiring half the bandwidth of the binary scheme.
Spectral Efficiency of Multicarrier Schemes for 5G

This paper compares the spectral efficiency of some of the most popular multicarrier schemes proposed for the fifth generation (5G) of cellular systems, such as orthogonal frequency-division multiplexing (OFDM), filterbank multicarrier (FBMC), generalized frequency-division multiplexing (GFDM), and universal filtered multicarrier (UFMC). First, we propose a unified signal model valid for many multicarrier schemes. Second, we employ the mismatched detection theory, together with the unified signal model, to obtain closed-form achievable lower bounds on the channel capacity, conditioned on the multipath channel realization. Third, we numerically compare the achievable spectral efficiency (ASE) of OFDM, FBMC, GFDM and UFMC, in different multipath channel scenarios. Our analysis proves that the best (ASE-optimal) multicarrier scheme depends on the channel model and on the signal-to-noise ratio (SNR).