Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deadbeat PI Controller With Modified Feedforward for PMSM Under Low Carrier Ratio

When the permanent magnet synchronous motor (PMSM) operates at low carrier ratio, the decoupling capability and dynamic performance of the proportional-integral (PI) controller are limited by the digitization delay. In this paper, a deadbeat PI controller is proposed by modified the feedforward. With the modified feedforward, the open-loop transfer function can eliminate imaginary components. Thereby the full decoupling of <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> is realized. At the same time, the controller also has full control over the location of the closed-loop poles, indicating that it can have a deadbeat response for the command tracking. For the disturbance rejection, the controller allows us to select the speed with which the controller can cancel the effect of a disturbance. Further considering that the motor parameters are inaccurate, by designing the controller coefficients with the maximum value of the closed-loop poles, the current-loop can ensure the dynamic performance and harmonic suppression ability. Finally, the effectiveness of the proposed controller is verified by the simulations and experiments.
Investigation of Bilateral Similarity in Central Retinal Blood Vessels

While our left and right eyes clearly have a high degree of external bilateral similarity, it is less obvious to what degree they have internal bilateral similarity. This is especially true for the central retinal blood vessels (CRBVs), which are responsible for supplying blood to retinas and also can be used as a strong biometric. In this paper, we investigate whether the CRBVs of the left and right retinas possess strong enough bilateral similarity so that we reliably tell whether a pair of the left and right retinas belong to a single subject. We evaluate and analyse the performance of both human- and deep neural network-based bilateral verification by experimenting on two publicly available data sets.
Machine Learning-Based Service Differentiation in the 5G Core Network

The proliferation of network virtualization, cloud computing, and software-defined networking have made a significant impact on how mobile networks are designed and operated. Much of this advancement can stand to gain from the incorporation of intelligent network management techniques such as those offered by Machine Learning. The increase in the amount of traffic with varying QoS requirements places an enormous challenge on end-to-end service provisioning and delivery. Network management required from providing support for service differentiation is one of the key pillars of 5G and beyond networks. In this paper, we present the design and implementation of a user traffic optimization framework that is based on the classification of network traffic of individual users. We also present the design and implementation of a network operations management framework, that is based on the usage of real mobile network usage data sets.
Coherent Ising Machine Based Optimal Channel Allocation and User Pairing in NOMA Networks

Non-orthogonal multiple access (NOMA) can multiplex different users on a single channel by applying superposition coding at the transmitter and Successive Interference Cancellation (SIC) at the receiver. It is considered to be an important technology for next generation wireless communications to achieve high throughput and large-scale connectivity. To take advantage of the effectiveness of NOMA, in addition to pairing users using criteria based on distance and power allocation, a key question is how to optimally allocate resources such as channels to users. Previous studies have discussed user pairing schemes and power allocation algorithms for NOMA systems, but the key issue is how to allocate the channels. Coherent Ising Machine (CIM) is an Ising computer that can solve NP-hard combinatorial optimization problems at high speed. CIM can run optimization algorithms based on mutually coupled neural networks and can be applied to a variety of optimization problems. In this paper, we proposed a CIM-based optimization method for user pairing and channel allocation in NOMA systems. Simulation results show that the CIM-based optimization can achieve superior performance of the NOMA system.
Human Sentiments and Associated Physical Actions Detection in Disasters with Deep Learning.

In the study of emotions and activities, the increasingly growing development of social networks and the tendency of users to share their physical activities, opinions, expressions and perspectives in text, visual and audio content have opened up new possibilities and challenges. While the literature has widely examined sentiment and action interpretation of text streams, it is comparatively recent but challenging to evaluate sentiment and physical actions from visuals such as photos and videos together. This paper focuses human emotion with connected physical activity analysis in a socially critical area, namely social media disaster/catastrophe analysis. For disaster-related videos and photos in occluded areas, we propose multi-tagging sentiment and associated action analysis. We believe that the proposed approach will lead to more viable communities by benefiting multiple stakeholders, such as news broadcasters, emergency relief services, and the public in general.
Digital Audio Forensics: Microphone and Environment Classification Using Deep Learning

The recording device along with the acoustic environment plays a major role in digital audio forensics. We propose an acoustic source identification system in this paper, which includes identifying both the recording device and the environment in which it was recorded. A hybrid Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) is used in this study to automatically extract environments and microphone features from the speech sound. In the experiments, we investigated the effect of using the voiced and unvoiced segments of speech on the accuracy of the environment and microphone classification. We also studied the effect of background noise on microphone classification in 3 different environments, i.e., very quiet, quiet, and noisy. The proposed system utilizes a subset of the KSU-DB corpus containing 3 environments, 4 classes of recording devices, 136 speakers (68 males and 68 females), and 3600 recordings of words, sentences, and continuous speech. This research combines the advantages of both CNN and RNN (in particular bidirectional LSTM) models, called CRNN. The speech signals were represented as a spectrogram and were fed to the CRNN model as 2D images. The proposed method achieved accuracies of 98% and 98.57% for environment and microphone classification, respectively, using unvoiced speech segments.
Can Deep Learning Break a True Random Number Generator?

True Random Number Generators (TRNGs) create a hardware-based, non-deterministic noise that is used for generating keys, initialization vectors, and nonces in a variety of applications requiring cryptographic protection. A compromised TRNG may lead to a system-wide loss of security. In this brief, we show that an attack combining power analysis with bitstream modification is capable of classifying the output bits of a TRNG implemented in FPGAs from a single power measurement. We demonstrate the attack on the example of an open source AIS-20/31 compliant ring oscillator-based TRNG implemented in Xilinx Artix-7 28nm FPGAs. The combined attack opens a new attack vector which makes possible what is not achievable with pure bitstream modification or side-channel analysis.
Improving the Performance of SDM-EON Through Demand Prioritization: A Comprehensive Analysis

This paper studies the impact of demand-prioritization on Space-Division Multiplexing Elastic Optical Networks (SDM-EON). For this purpose, we solve the static Routing, Modulation Level, Spatial Mode, and Spectrum Assignment (RMLSSA) problem using 34 different explainable demand-prioritization strategies. Although previous works have applied heuristics or meta-heuristics to perform demand-prioritization, they have not focused on identifying the best prioritization strategies, their inner operation, and the implications behind their good performance by thorough profiling and impact analysis. We focus on a comprehensive analysis identifying the best explainable strategies to sort network demands in SDM-EON, considering the physical-layer impairments found in optical communications. Also, we show that simply using the common shortest path routing might lead to higher resource requirements. Extensive simulation results show that up to 8.33% capacity savings can be achieved on average by balanced routing, up to a 16.69% capacity savings can be achieved using the best performing demand-prioritization strategy compared to the worst-performing ones, the most used demand-prioritization strategy in the literature (serving demands with higher bandwidth requirements first) is not the best-performing one but the one sorting based on the path lengths, and using double-criteria strategies to break ties is key for a good performance. These results are relevant showing that a good combination of routing and demand-prioritization heuristics impact significantly on network performance. Additionally, they increase the understanding about the inner workings of good heuristics, a valuable knowledge when network settings forbid using more computationally complex approaches.
The Flexible Smart Traction Power Supply System and Its Hierarchical Energy Management Strategy

With the increasing requirements of the railway sector for electrified railways and the development of society, the traction power supply system needs to become more flexible, economic and reliable. The traditional traction power supply system has many problems due to its own topology, such as negative sequence current, load fluctuation, low utilization rate of regenerative braking energy, and inability to use the distributed generation along the railway. In order to solve these problems and make the traction power supply system (TPSS) more flexible, efficient and reliable, a novel TPSS called flexible smart traction power supply system (FSTPSS) is proposed. This traction power supply system consists of AC-DC-AC traction substation and the microgrid along the railway. It can effectively mitigate the problems of negative sequence current, improve regenerative braking energy utilization and distributed generation utilization. A hierarchical energy management strategy is also proposed here, which can effectively improve the operating economy of FSTPSS and reduce the load fluctuation of FSTPSS. And based on the actual load measurement power of a traction substation of China’s Beijing-Shanghai high-speed railway, the proposed energy management strategy has been verified for the economic improvement and load fluctuation reduction of TPSS.
A Model for Inpatient Ward Simulation and Forecasting

Making decisions on capacity requirements in hospital environments has long-term consequences. Sound estimates of expected mid and long-term demand for hospital beds are required to avoid oversupply or deficits. Though in many cases, forecasts of admissions and Length of Stay evolutions for specific pathologies are available, current models rarely model hospital environments on the pathology level, rendering them useless. Additionally, existing models are most often unit type or facility-dependent. This work describes a model to estimate care demand and capacity needs per ward of a generic hospital, in which pathology-specific parameters can be used, and that supports patient stays structurally segmented across multiple wards. The methodology enables an increase in model complexity while explicitly guarding the accuracy of parameter estimates.
Accelerated Analysis of Simulation Dumps through Parallelization on Multicore Architectures

With the explosion of off-the-shelf SoCs in terms of size and the advent of novel techniques related to failure modes, commercial ATPG and fault simulation engines can often be insufficient to measure the coverage of very specific metrics. In these cases, many researchers firstly store the simulation trace during the analysis phase. Then, they collect the desired statistics during a post-processing step. In this framework, the so-called Value Change Dump (VCD) is a very commonly used file format to record simulation traces. The target of this paper is twofold. From the one hand, we illustrate some Burn-In (BI) related metrics which cannot be evaluated by current commercial fault simulators and ATPG engines. These metrics are indeed based on a post-processing analysis of memory dumps in VCD format. From the other hand, we mitigate the evaluation time and the memory required to analyze huge VCD files by exploiting optimization techniques coming from modern programming features and smart parallelization. Adopting this strategy, we can analyze simulation dumps of more than 250 GBytes in less than one hour, showing improvements of two orders of magnitude over previous tools, with a consequent higher scalability and testability power.
Study and Active Enhancement by Converter Reconfiguration of the Performance in Terms of Stator Copper Loss, Derating Factor and Converter Rating of Multiphase Drives Under Two Open Legs With Different Stator Winding Connections

Multiphase machines offer inherent tolerance to faults such as open converter legs (OCLs), which are especially frequent. Because of this reason, they are particularly attractive for applications where fault tolerance is important, such as offshore wind energy or aerospace, naval and military vehicles. It has been previously shown that, under an OCL, certain stator winding configurations (SWCs) different from star yield smaller stator copper loss (SCL) and larger maximum achievable torque (MAT) than star SWC for the same torque command and machine. This advantage comes just at the expense of a moderate increase in converter rating. However, only the case of single OCL was studied in general. The SCL, MAT and required converter rating for two OCLs are currently unknown for different combinations of phase number, faulted legs and SWCs. Actually, under two OCLs (unlike for one OCL) it may be possible to actively modify the order of the faulted/healthy legs to enhance the performance in these terms, but this possibility has not been studied so far in spite of its potential. This paper addresses the postfault performance of multiphase drives with two OCLs, for various SWCs. The MAT (derating factor), SCL and necessary converter rating are assessed in numerous possible scenarios. The most convenient alternatives are established. Most importantly, in view of the conclusions of this analysis, a novel method is proposed to improve substantially the MAT and SCL by actively altering the connections between the converter and machine terminals. Experimental results with six-phase and twelve-phase setups are provided.
Delay-Aware Energy-Saving Strategies for BBU Pool in C-RAN: Modeling and Optimization

The surging mobile traffic poses serious challenges for mobile operators and the advancement of information and communication technologies (ICT). The cost and energy consumption cannot support affordable mobile services and sustainable growth. As a new architecture, cloud-based radio access network (C-RAN) is proposed to confront these challenges. C-RAN is a deployment paradigm that seeks to isolate baseband unit (BBU) from its remote radio unit (RRU) in base station (BS), consolidating the BBUs into a common place (i.e. BBU pool). In the BBU pool, the computing resources provided by the BBUs can be dynamically assigned to RRUs on demand by the BBU controller. Thus, with the fluctuation of data traffic from RRUs, a part of BBUs can be dynamically turned on or off. As a result, the energy consumption of the BBU pool can be reduced correspondingly. In this paper, we design a Markov chain-based calculation model to formulate the energy-saving operation of the BBU pool with an active/sleep mode under dynamic RRU traffic load. On this basis, we propose two different BBU state transition strategies for the BBU pool, i.e., “Cautious ON, Bold OFF (CO-BF)” strategy and “Bold ON, Cautious OFF (BO-CF)” strategy. We formulate the key performance indicators in terms of the energy saving efficiency (ESE), the consequent packet queuing delay (PQD) and the normalized tradeoff cost as well. In extensive simulations, we investigate the effect of these key parameter indicators on the system performances of the BBU pool. Achieving the minimum normalized cost, an optimal operating strategy for the BBU pool can be determined by adjusting the parameter values of the (<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>). The simulation results show when RRU traffic load is light (e.g., <inline-formula> <tex-math notation="LaTeX">$\lambda =0.1$ </tex-math></inline-formula>), the proposed BO-CF strategy can lead to the minimum normalized cost, which corresponds to ESE of up to 75.4% and PQD of 0.0097ms. For heavier load (e.g., <inline-formula> <tex-math notation="LaTeX">$\lambda =0.4$ </tex-math></inline-formula>), the minimum normalized cost can be obtained along with ESE of up to 74.1% and PQD of 0.1241ms, when the CO-BF strategy is adopted. Hence, we observe that the BO-CF strategy is slightly better than the CO-BF for the light traffic load of the BBU pool, while the CO-BF strategy would be more suitable for the BBU pool with the heavier traffic load. It proves the efficacy of our proposed Markov chain-based calculation model to pursuit an optimal operating strategy for the delay-aware and energy-efficient BBU pool.
A Design of Learning based Human Positioning Algorithm using Images

In this paper, a learning based human positioning algorithm using image was proposed to overcome the disadvantages of fingerprint map-based localization on hardware devices, and the human positioning accuracy was also verified through an experiment. The core parts of proposed algorithm are the learning phase of human positioning map (HPM) and human positioning phase. As a result of the experiment, the human recognition accuracy of proposed algorithm showed a high recognition accuracy of over 99.74% when a person moves between Om and 6m.
Performance-Fairness Trade-off for Wi-Fi and LTE-LAA Coexistence

Long Term Evolution (LTE)-License Assisted Access (LAA), which leverages unlicensed resource sharing with the Wi-Fi network, is a promising technique to address the spectrum scarcity issue in present and future wireless networks. However, unlicensed spectrum sharing between Wi-Fi and LAA requires fair resource allocation with specific performance guarantees for both sets of Wi-Fi and LAA stations. In this paper, an optimal communication policy is devised for LAA stations coexisting on a single unlicensed channel with Wi-Fi stations. The inter-network collisions are avoided through non-overlapping transmission phases for Wi-Fi and LAA networks. The throughput performance of LAA network is maximized while guaranteeing a proportionally fair performance among LAA stations and a fair share for Wi-Fi stations. The proposed scheme, unlike the state-of-the-art coexisting mechanism, jointly optimizes the transmission probability and the transmission rate for each LAA station. The formulated optimization problem to maximize network throughput is solved analytically. The numerical results demonstrate a significant improvement in the LAA throughput, more than 75%, as compared to the case when transmission probabilities are not optimized. Moreover, a notable gain of 8-9% in the fairness index reflects the intra-network fairness of the proposed LAA network over the conventional LAA network.
Understanding the Effect of Generation through the Sustainability of OMNI-channel Commerce

Popularity from each generation effects the economic movement, especially the retail business, which expected to have a slight decrease at 5-8% due to the customer’s behavior, ideas, and lifestyle that change with the era. This research aims to build a conceptual framework based on IS success model and loyalty model for initially studying the strategy model of OMNI business sustainability with intensely focusing on generation factors and understanding the attitude of each generation. This research would a better understand potential factors affecting the model of OMNI business sustainability on generations. Then, for further research after data collection, the AMOS program will be used to analyze the relationship of variables to develop sustainable practices in OMNI businesses from the perspective of consumers of each generation such as Baby Boom, X, Y, and Z.
A Miniaturized MIMO Antenna With Triple Band-Notched Characteristics for UWB Applications

In this paper, a novel compact four-element ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna with triple band-notched characteristics is proposed. The proposed antenna is composed of four slot antenna elements with a common rhombic slot, each feeding by a microstrip-fed line to greatly reduce the overall size of the antenna. It has a compact size of 34 mm $\times34$ mm $\times1.6$ mm. The high isolation and polarization diversity are achieved by placing the four microstrip-fed lines perpendiculars to each other, while a parasitic strip is employed as a decoupling structure between adjacent microstrip-fed lines to further improve isolation. Moreover, the proposed antenna can achieve triple band-notched characteristics by embedding L-shaped and C-shaped slots on each radiator and loading electromagnetic band gap (EBG) structures next to micro-strip feeders respectively. As a result, the proposed antenna obtains three notched bands of 3.3-3.9 GHz, 5-6 GHz, and 7.4-8.5 GHz, which are in good agreement with the interference bands of WiMAX (3.3-3.7 GHz), WLAN (5.15-5.875 GHz) and X-band (7.3-8.5 GHz), respectively. The antenna prototype has been fabricated and measured. The results show that the antenna has an impedance bandwidth of 2.5-12 GHz (except for the three notched bands). Besides, the isolation among the elements, envelope correlation coefficient, radiation characteristics, efficiency, realized gain, and total active reflection coefficient are also investigated. The experimental results indicate that the proposed antenna can be a good candidate for UWB-MIMO wireless communication applications.
Low-Cost Real-time Driver Drowsiness Detection based on Convergence of IR Images and EEG Signals

This paper focused on low-cost real-time driver’s drowsiness detection by fusing facial image information obtained through the IR camera (Infrared Camera) and EEG (Electroencephalogram) signal acquired through the EEG sensor. The proposed method was tested on the target board (i.MX6Quad). The i.MX6Quad is the SoCs (System-on-Chip) that integrate many processing units into one die, like the main CPU, a video processing unit and a graphics processing unit for instance. Instead of the RGB camera, the IR camera is applied to driver condition monitoring and drowsiness detection technology by extracting the driver’s facial feature information robustly against daytime, night-time, and frequent change of brightness around the face. The headphone type EEG sensor is also used to minimize the user’s discomfort. On the target board, the processing time per image frame is about 60ms, so that it can process about 17 frames per second. This processing time can be suitable for the driver’s drowsiness detection systems.