Miracle-dz/UniFAR-FTUs huggingface dataset:
A Real-Time Hybrid Modular Multilevel Converters Emulator for Hardware-in-Loop-Tests

Real-time simulation of modular multilevel converter (MMC) is essential for delivering practical projects. However, existing real-time MMC simulators have functional limitations in some aspects. To solve this problem, this paper presents a general-purpose MMC emulator with a hybrid submodule topology. The MMC emulator is implemented on the field-programmable gate array (FPGA), supporting all circuit parameters flexible configuration and internal fault real-time simulation with a time-step of 2$\mu$s. Each submodule has its own independent storage space for configurability. To improve the accuracy of the simulation, all equations are calculated in the single floating-point precision. This paper adopts a novel method based on partial results caching and merging unit, which can reduce the latency of the accumulation. Moreover, a fault parameter addressing module is presented to simulate internal MMC valve faults. The real-time simulation results, which are obtained by simulating YUE Back-to-Back Project, are provided to verify the correctness of the designed emulator.
1Gbit High Density Embedded STT-MRAM in 28nm FDSOI Technology

High density 1Gb embedded STT-MRAM in 28nm FDSOI technology was successfully demonstrated. Based on the highly reliable and manufacturable eMRAM technology, high yield over 90% was achieved at the operating temperature from −40°c to 105°c with satisfying read, write function and 10 years retention at 105°c. These results are mainly attributed to the advanced process for better control of MTJ CD, highly manufacturable process window and robust circuit design for high density chip. MTJ properties can be systematically adjusted by tailoring the MTJ stack and MTJ module process. Improved endurance up to 1E10 cycles was achieved to broaden eMRAM applications to eDRAM replacement.
Rectangular Patch Antenna Bandwidth Enhancement Applying New Sleeve-Arm Feeding

Expanding the bandwidth of a rectangular patch antenna (RPA) has been a research focus for decades. This paper proposes a sleeve-arm shape structure to expand an RPA's fractional bandwidth from 9.3% to 50%. The sleeve-arm configuration transforms the RPA's impedance values to be closer to 50 Ohm, allowing easy matching in a wider bandwidth, comparing with the conventional RPA. Simulated results show the impedance transformation and the corresponding broadband matching from 3 GHz to 5 GHz. This architecture is simple and occupies little space. It can be used in broadband applications to enhance patch antenna's impedance bandwidth.
An Empirical Analysis of Stock Market Price Prediction using ARIMA and SVM

Autoregressive Integrated Moving Average (ARIMA) model is the most acceptable and applied model in the terms of time series forecasting mechanism. Although, this model has its own kind of parametric limitations to capture the nonlinear patterns in the case of stock market prediction. Support vector machines (SVM) which is a novel neural network technique, can easily solve these problems which is available in the ARIMA model. Maximum number of the available reviewed paper and chapter has its own kind of limits as they concentrate on the particular application of financial market or explores machine learning tools and techniques that was applied on the particular dataset. This study will provide a comparative study of some relevant existing tools and the techniques applied in the area of the financial market analysis. The main aim of this study is: (i) a comparative study of the recent and relevant available model of the area, (ii) comparative study of ARIMA and SVM models in R-language (iii) review of the fundamental challenges and futuristic challenges of the field.
Blind Synchronization of Channel Impulse Responses for Channel Reciprocity-based Key Generation

Local, blind synchronization of Channel Impulse Responses for key generation algorithms without leaking details to adversaries is challenging, due to noise from transmission and measurements. Extracting the channel characteristics as a common source of randomness using CIR promises notable improvements in key bit rates for Physical Layer Security, however, reciprocity requirements necessitate successful synchronization at both ends.We propose a one-dimensional Gaussian filter towards this end, which is simple, more robust, and outperforms competing approaches in almost all settings. To evaluate the quality of our approach, we assess the synchronization accuracy on extensive datasets. Experiments using data generated synthetically with the standardized IEEE 802.15 UWB channel model indicate a performance improvement of up to 31%. Performance on par or better than all alternatives in real measurements underlines its superior robustness against synchronization errors.
Fashion Recommendation with Social Intelligence on Personality and Trends

Today's online shopping is very prosperous, and fashion shopping online has also expanded rapidly. Effective fashion recommendation has become an increasingly important issue. In the past, most of the research focused on personal recommendation, however personality change with the environment in addition to time and space such that this trait never stagnate. Fashion reflects not only a person's character, but also his/her person's current personality influenced by the larger environment. The purpose of this study is to design a recommendation mechanism, which could infer fashion trends derived from social commerce platform and accurately recommend appropriate clothing for the users.
Derivation and Analysis of Probability Distribution of Visible-Light Channel Gain Difference Based on Lambertian Radiation

The distribution of channel gain difference (CGD) is important for maximizing system capacity and implementing user cooperation. However, its analysis in visible light communication has not been extensively studied thus far. In this study, we investigate the probability distribution of visible-light CGD based on Lambertian radiation. First, owing to the difficulties in direct analysis, we derive the approximate expression for CGD of visible light based on the Lambertian model. Second, when user distribution is uniform, we derive the probability density function and cumulative distribution of the CGD based on visible-light channel. Third, we provide the closed form of optimal lighting angle for the visible-light cell, leading to the high probability of CGD. Furthermore, we analyze the relationship between the CGD and other parameters in lighting deployment. Finally, the simulation results demonstrate the approximation between theoretical and actual distributions of CGD, and the numerical results offer the insight between CGD and lighting deployment based on visible-light channels.
Time Series Modeling of Storm Outages with Weather Mesonet Data for Emergency Preparedness and Response

Emergency managers at electric distribution utilities benefit from tools that help estimate the impacts of weather on their infrastructure networks when communicating with customers and regulators. In this paper, we adopted deep learning methods - Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) networks - to model a time series of county-level customer outages for electric distribution utilities across ten counties in New York State. We utilized hourly weather data (i.e. wind, temperature, precipitation, gust, and wind direction) from the New York State mesonet stations, and customer outages data reported by utility companies as inputs into the neural network models, and used the maximum customers without power per hour as the response variable. A robust estimate of each model error is calculated and compared through a nested cross validation method. We demonstrated that models developed with the neural network methods are capable of forecasting the multivariate, multi-step, and multi-site maximum customers without power response variable. We found that both the LSTM and MLP sliding-windowed models outperformed baseline models with an average RMSE improvement of 31.77% - 34.12% over the baselines for lead times of 1hr, 6hrs, 12hrs, 18hrs, and 24hrs respectively, with 72hrs lookback periods. These neural network frameworks and datasets can be configured to create accurate and scalable predictive models suitable for any county's implicit uniqueness (i.e. infrastructure maintenance.), and as input to a larger weather forecasting model.
Extending Kubernetes Networking to make use of Segment Routing over IPv6 (SRv6)

—Kubernetes is the leading platform for orchestrating containerized applications. In this paper, we extend Kubernetes networking to make use of SRv6, a feature-rich overlay networking mechanism. Integration with SRv6 can be very beneﬁcial when Kubernetes is used in large-scale and distributed multi-datacenter scenarios. We have focused on the Calico CNI plugin, one of the most used Kubernetes networking plugins. In particular, we consider Calico-VPP, a version of the Calico plugin based on the VPP (Vector Packet Processing) data plane, which provides support for SRv6 operations with very high performance. The proposed SRv6 overlay networking solution for Kubernetes offers several advantages compared to a traditional overlay (e.g. IP in IP), in particular the possibility to use Trafﬁc Engineering for the overlay tunnels. In the paper, we provide the architecture and the detailed design of the SRv6 based overlay and describe our open source implementation. We consider the research and technological question on how to extend Kubernetes networking to support large-scale and distributed multi-datacenter scenarios, which is an important goal for Cloud and Network providers. In this respect, we compare two different solutions for the control plane architecture of the SRv6 capable Kubernetes networking plugin, one based on the BGP routing protocol and another one based on extending the Kubernetes control plane. Finally, we report a performance evaluation of the data plane of the proposed SRv6 overlay networking, showing that it has comparable performance to existing overlay solutions (e.g. IP in IP), while offering a richer set of features.
Packet Caching Mechanism for Mobile Node in IPv6 Wireless Sensor Network

Mobile nodes in IPv6 wireless sensor network (WSN) will constantly switch links and select new parent nodes when they are moving. Packets sent to mobile nodes may be lost during the link handover process, leading to increasing the network packet loss rate and affecting the stability of the network. To handle the packet loss problems caused by node mobility, this paper realizes link switching and node location information propagation when nodes move, and designs a packet caching mechanism for mobile nodes based on RPL routing protocol. Furthermore, we simulate the packet caching mechanism in Contiki3.0 Cooja platform, and the results indicate that the proposed mechanism outperforms RPL, mRPL, and EC-MRPL in the performance of both the upward and downward packet arrival rate.
Privacy Preserving of Shared Data in Deep Learning

In the age of Big Data the need of developing machine learning algorithms has increased. Such algorithms are used to extract valuable information needed for different types of sectors; health, education, financial …etc. In many cases data has to be shared among several parties to guarantee better accuracy of the results of such algorithms. In these cases privacy of data would be questionable. In this paper a survey has been conducted on research that focused on Privacy Preserving techniques when applying deep learning algorithms on distributed or shared data.
On the Kaniadakis Distributions Applied in Statistical Physics and Natural Sciences

Constitutive relations are fundamental and essential to characterize physical systems. By utilizing the κ-deformed functions, some constitutive relations are generalized. We here show some applications of the Kaniadakis distributions, based on the inverse hyperbolic sine function, to some topics belonging to the realm of statistical physics and natural science.
A Comparative Study of Body Hydration Measurement Techniques for Health Monitoring

In this paper, we have discussed various methods available for detection of body dehydration and compared their pros and cons. The main purpose of this study is to find scope of research for detection of water dehydration in wireless body area sensor network on the basis of blood concentration and heartbeat. We find that most of works done in this area are based on conventional laboratory diagnosis methods which are uncomfortable and time consuming. Least work is done in wireless body area sensor network for detection of dehydration. We compared the techniques based upon time and cost metrics, convenience, accuracy of results and reliability.
Spin norm and Lambda norm

Given a $K$-type $\pi$, it is known that its spin norm (due to first-named author) is lower bounded by its lambda norm (due to Vogan). That is, $\|\pi\|_{\rm spin}\geq \|\pi\|_{\rm lambda}$. This note aims to describe for which $\pi$ one can actually have equality. We apply the result to tempered Dirac series. In the case of real groups, we obtain that the tempered Dirac series are divided into $\#W^1$ parts among all tempered modules with real infinitesimal characters.
UWB Radar for Multiple Human Detection Through the Wall Based on Doppler Frequency and Variance Statistic

Multiple human detection through the wall has become an interesting topic for security, rescue, life detecting under earthquake rubble, etc. This paper presents a UWB radar at 3 GHz for detecting multiple humans through the wall based on Doppler frequency and variance statistic of the respiratory signal. Technically, we have referred to efficiently simple methods are FFT and variance statistic for identifying the respiratory frequency of multiple persons quickly. Experimental results show the methods can identify and evaluate quickly respiratory frequencies of two persons through the wood wall.
Airport Delay Prediction Based on Spatiotemporal Analysis and Bi-LSTM Sequence Learning

The air traffic network is the small-world system whose delay propagates among the entire network quickly. These frequent delays bring huge economic and resources loss. Therefore, accurately predicting the airport delay is essential for the airlines or air traffic controllers to adjust flight schedules. This paper proposes a multi-step deep sequence learning model (Bi-LSTM+Seq2Seq) to predict airport delay which considers the spatial-temporal correlation of other airports in the network. Firstly, the dataset is processed to analyze the temporal delay correlations of airports based on the complex network theory. The PageRank and K-means algorithm are used to cluster the behavior of the networks and to know the state of the entire network. Secondly, based on time series data about the current state of the network and delay relationship between airports, the Bi-LSTM+Seq2Seq model has been proposed and trained. Through the experiments, the proposed model has better accuracy and stability compared with other prediction algorithms.
Automatic Identification of Epileptic Focus on High-Frequency Components in Interictal iEEG

The localization of the seizure focus affected by epilepsy is crucial for epilepsy treatment due to observing long-term interictal intracranial electroencephalogram (iEEG) for categorizing the patterns of seizures by the neurological experts. Therefore, a computer-aided system based on machine learning method for automatic localization of focal patterns is promising future. In this study, we presents a filter-bank entropy-based feature-extraction approach in high-frequency components to detect epileptic focus, which consider a valid biomarkers to guide epilepsy surgery. The experimental results on real-world interictal iEEG recorded from eight patients demonstrate that our proposed method can achieve average AUC 0.79, which can reduce the workload of clinical experts for detection of epileptic focal.
Design of an Ultra-Wideband Tightly Coupled Dipole Array

A low profile ultra-wideband tightly coupled dipole array with a bandwidth of 14.5:1 is proposed in this paper. A simple unbalanced feed structure that consists of a stripline and a parallel transmission line is employed to excite the tightly coupled dipoles and a resistive sheet is used to broaden the bandwidth. Simulated results have shown that the array can operate from 0.2 to 2.9 GHz with $\text{VSWR} < 2.5$ at broadside, which means an ultra-wideband bandwidth is obtained. It is also worthwhile to mention that the total height of the antenna above the ground plane is $0.083\ \lambda_{\mathrm{L}}$ (the wavelength of the lowest frequency), indicating that the antenna achieves a low profile performance.
A Cell Voltage Classification Method Based on Wavelet Packet Analysis and Residual Network

In the aluminum reduction production process, the cell voltage signal is an influential index of the cell condition, reflecting the stability and controllability of the aluminum reduction cell. According to the spectral characteristics of the voltage signal of aluminum reduction cell, in this paper, we propose an integrated method for classifying and evaluating the cell voltage signal. Firstly, we decompose the original signal into low frequency components and high frequency components using wavelet packet analysis and we construct a energy model of the cell voltage signal for the calculation of the proportion of high frequency fault energy consumption of the original voltage signal. Then we build a dataset of low frequency component samples of cell voltage signal and we train a residual network model on this dataset for the identification of the fluctuations of low-frequency component of cell voltage. Finally, some expert rules are developed to determine the state of cell voltage. And the experimental results show that the effectiveness of this classification method for the analysis of the cell state.
Avoiding Faults due to Dangling Dependencies by Synchronization in Microservices Applications

Microservices architecture and containerization are promising software development approaches. In spite of their claimed autonomy, microservices often depend on other microservices to complete their tasks. Since each microservice of an application has its own life-cycle, dependencies may cause faults if they are not satisfied due to the unreadiness of target microservices. To address this problem, we argue the necessity of a mechanism to synchronize microservices according to their running states: dependent microservices should wait for unready ones, so avoiding useless and faulty interactions. In this paper, we propose a new framework, implemented as a service at platform level, able to support synchronization among microservices by exploiting distributed registries for collecting health/state information about deployed containers and related microservices. The service has been implemented for the OpenShift/Kubernetes platform and experimented with different use cases. We mainly focus on avoiding faults due to dangling dependencies during the bootstrap of microservices applications. The valuable effects of the proposed service provided us useful feedback to extend its adoption to other use cases even through its integration in existing microservices frameworks.
Neural Network Approximation for Nonlinear Partial Differential Equations with Quasi-Newton Optimization and Piecewise Strategy

In this paper, we propose a neural network based approach to approximate numerical solutions of partial differential equations (PDEs). Concretely, we understand the solution of PDEs as finding a reasonable mapping from the PDEs with initial and boundary conditions to the space of solutions. Then, we approximate this mapping by a neural network with best fitting the data from the PDEs, where the initial and boundary conditions are cooperated to the loss function. The main contributions of proposed method are twofold. First, we adopt the quasi-Newton algorithm to minimize the loss instead of the stochastic gradient method, which is benefit from the relatively low sampling rate. It avoids the instability arising from the stochastic method. On the other hand, we adopt the piecewise strategy to improve the computational efficiency. To further understand the approximate process, we also investigate how the network architecture affect the performance. Finally, we compared the proposed method with some state-of-the-art numerical methods for PDEs. The experimental results validate that the proposed method is more flexible and can avoid the difficulties caused by the complexity of target PDEs, and hence obtains the best performance, even for nonlinear equation with shock.
SMT Solvers in Application to Static and Dynamic Symbolic Execution: A Case Study

This paper studies the performance and working aspects of SMT solvers on processing formulas acquired during path-sensitive static analysis and dynamic symbolic execution. We review some general patterns of building SMT formulas in the QF_BV logic during analysis and related technical specifics. We also provide the results of comparing different solvers on two sets of requests obtained by Svace static analyzer and Anxiety dynamic symbolic execution tool. It turns out that Yices2 solver performs the best, although, for Svace, notable part of requests can be done better by other solvers. In return, Yices2 misses some features crucial to top-tier analyzers such as deterministic time limit. A brief attempt at making machine learning based solver portfolio shows that solving time can be enhanced, but requires some serious work on feature selection, while technical difficulties may render it unpractical. For Anxiety we found out that with Yices2 incremental solving is almost always faster (sometimes dozens of times faster) than non-incremental. Moreover, the more queries we solve incrementally, the higher acceleration we get.
Blind Signal Detection in Cellular Bands

In this article, we propose a blind wideband signal detection algorithm to detect and locate occupied channels in cellular bands without a priori knowledge of the band channelization. This is formulated as a change-point detection problem. The proposed algorithm is validated using over-the-air measurements taken at different cellular bands and shown to work in a global system for mobile communication, long-term evolution (LTE), and universal mobile telecommunications system bands.
Adaptive back-stepping tracking control of the hypersonic vehicle with input saturation

This paper studies the tracking problem of hypersonic vehicles subjected to external disturbances, modeling uncertainty, and input saturation. The adaptive anti-saturation control scheme is designed based on the first-order command filter, dynamic surface control and auxiliary system. The proposed control strategy not only uses adaptive algorithms to estimate the external disturbance, but also introduces an auxiliary system to solve the problem of input saturation. The stability of the closed-loop system with respect to the proposed control scheme is proved by using Lyapunov theory, and the results of numerical simulations are presented to demonstrate the effectiveness of the proposed control strategy.