Miracle-dz/UniFAR-FTUs huggingface dataset:
A Solid-State Current limiting Switch for the Application of DC Power Network

when designing a large distributed dc power network, special attention must be placed on the electrical stability, control of the systems, and individual load on the systems. In order to prevent the fault propagation, provide protection against overload and fault, and isolate the faulty load and equipment in a dc power network, an overcurrent protection device must be applied to the switch gear for the electric power management and control of the systems [1]. Overcurrent protection devices such as circuit breakers in the primary and secondary distribution systems protect all branch circuits on the load side of the discharge bus. A circuit breaker is electromechanical devices that may be reset and reused after an overload or low-value short circuit, but most manufactures recommend replacement if the device ever interrupts a short circuit with a fault current close to its interrupt rating. Furthermore, circuit breaker has the conflicting of time delay characteristics that trip delays must be long enough to open the circuit when a real fault exists [4]. A solid state type of power switching circuit has been developed as a new power protection technique for dc power network instead of electromechanical circuit breaker. The candidate for these switching devices should be Si Power MOSFET, IGBT, and SiC MOSFET. As an example of an electromechanical circuit breaker delay curve application, consider a 50 A circuit breaker and fault that draws 200 A (400% of circuit breaker rating). The opening mechanism in circuit breaker will not completely open until as much as 0.2 s has elapsed [1]. While on the other hand, solid-state current limiting switch will open within 20 μs and this elapsed time and current rating can be adjusted freely [2]. From the stand point of the dc network stability, when the transient over current caused by fault and or short circuit will be observed, a solid-state current limiting switch will act as over current suppression as a secondary role in the dc power network. In this paper, we describe the performance of these solid state type of power switching circuit in DC power network and result of stability analysis using Power Electronics Circuit Simulator, PSIM.
Cognitive Task Classification Using Fuzzy Based Empirical Wavelet Transform

Brain-Computer Interfaces (BCIs) systems convert brain signals into outputs commands those allow to user to communicate even absence of other body nerves and muscles activities. Response to cognitive activity (mental task) grounded BCI system is one of the dominate areas of research interest. Electroencephalography (EEG) signals are utilized to characterize the brain activities in the BCI domain. Efficient feature extraction from EEG signal is the most important aspect of good per-formance of classification model. Two known feature extraction methods for non-linear and non-stationary signals are Wavelet Transform and Empirical Mode Decomposition. By exploiting both techniques, an adaptive-filter based approach was proposed earlier famous as Empirical Wavelet Transform (EWT) to de-compose such dynamic signals. But EWT failed to provide useful features for dynamic signals which has overlapping in frequency domain and time domain. To overcome this problem, we utilized fuzzy c-means algorithm along with EWT in our experiment. A well-known multivariate feature selection technique named Linear Regression is used to avoid the problem of the small ratio of samples to features. Further, the Quadratic discriminant classifier (QDC) has been utilized to develop the classification model. The experiments have been done on a publicly available task-based EEG data for comparing the proposed approach with EWT based cognitive activity (mental task) classification. The experimental results show that the proposed fuzzy-based EWT approach for EEG classification gives superior performance over the original EWT.
Video Denoising Quality Assessment for Different Noise Distributions

Denoising algorithms often presume a single noise model, for instance, Gaussian noise, but it has been observed that during acquisition, image and video sequences can be corrupted by different types of noise, which follow a distinct probability distribution model depending on the application. This paper aims to compare the performance of several denoising algorithms, among them Non-Local Means and Block-Matching 3D, and other classical techniques such as median, Gaussian, bilateral and anisotropic diffusion, by simulating different noise distributions in videos and comparing the methods efficiency in multiple scenarios. Objective evaluation uses structural similarity (SSIM) and provides video specific assessment scores with NTIA Video Quality Metric (VQM). Results show considerable differences between intraframe and interframe filtering quality, while variations in filtering responses to each type of noise contribute to more appropriate selection of techniques to noise reduction and provide insight to noise difficulty levels.
Transfer Learning for a Multimodal Hybrid EEG-fTCD Brain–Computer Interface

Humans can transfer knowledge previously acquired from a specific task to new and unknown ones. Recently, transfer learning (TL) has been extensively used in brain–computer interface (BCI) research to reduce the training/calibration requirements. BCI systems have been designed to provide alternative communication or control access through computers to individuals with limited speech and physical abilities (LSPA). These systems generally require a calibration session in order to train the BCI before each usage. Such a calibration session may be burdensome for the individuals with LSPA. In this article, we introduce a multimodal hybrid BCI based on electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) and present a TL approach to reduce the calibration requirements. In the hybrid BCI, EEG, and fTCD are used simultaneously to measure the electrical brain activity and cerebral blood velocity, respectively, in response to motor imagery (MI) tasks. Using the data we collected from ten healthy individuals, we perform dimensionality reduction utilizing regularized discriminant analysis (RDA). Using the scores from RDA, we learn class conditional probabilistic distributions for each individual. We use these class conditional distributions to perform TL across different participants. More specifically, in order to reduce the calibration requirements for each individual, we choose the recorded data from other individuals to augment the training data for that specific individual. We choose the data for augmentation based on the probabilistic similarities between the class conditional distributions. For the final classification, we use the RDA scores after TL as features input to three different classifiers: quadratic discriminant analysis (QDA), linear discriminant analysis (LDA), and support vector machines (SVMs). Using our experimental data, we show that TL decreases the calibration requirements up to $87.5\%$. Also by comparing SVM, LDA, and QDA, we observe that the SVM provides the best classification performance.
Reconfigurable Metasurface as Microwave Reflectors and Polarization Converters

An ultrathin metasurface is made reconfigurable through the use of unit cells incorporating voltage-controlled varactor diodes. The phase characteristics of each unit cell are individually controlled for reconfigurability mechanisms. Two main applications are proposed in the domain of microwave devices and antennas. The planar metasurface is illuminated by a primary source and is firstly used as reconfigurable reflector allowing beam steering performances and frequency agility. A second functionality is also demonstrated, where the metasurface is used as a reconfigurable polarizer. Measurements are performed on a fabricated prototype to validate the concept.
Speech Recognition for People with Dysphasia Using Convolutional Neural Network

As the advance of technology, it is increasingly speech recognition tools on mobile devices, such as Google Voice and Apple Siri, those have been widely used and have high recognition rate of people's speech. However, these speech recognition tools cannot work well for people with the disease of "Dysphasia" and has very low recognition rate. It is important issue to develop a speech recognition toot for dysphasia, such as Cerebral Palsy(CP) and Amyotrophic Lateral Sclerosis(ALS), to assist those people communicating with others well. Recently, there are various open source programs has been announced, such as Google's Tensorflow, which is used to develop speech recognition based on Deep Neural Networks (DNNs). In this paper, we propose a Convolutional Neural Networks (CNNs) model to perform speech recognition for ALS. The model consists of two hidden CNN layers, each includes one CNN, Rectified Linear Unit (ReLU), Dropout Unit, and MaxPooling Layer. We implement the CNN for dysphasia speech recognition using Google's Tensorflow and collect 33 pronunciations from a dysphasia, each pronunciation at least has 350 training voice files. The highest accuracy is about 63% for one word. And it will be up to totally 94% for top five words. The result shows that it can effectively recognize speech for dysphasia. It can be expected to construct a speech recognition system for assisting dysphasia communicating with others.
The Sphere Packing Bound via Augustin’s Method

A sphere packing bound (SPB) with a prefactor that is polynomial in the block length <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is established for codes on a length <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> product channel <inline-formula> <tex-math notation="LaTeX">$W_{[1], [n]}$ </tex-math></inline-formula>, assuming that the maximum order 1/2 Rényi capacity among the component channels, i.e. <inline-formula> <tex-math notation="LaTeX">$\max _{t\in [1], [n]} C_{ {\scriptstyle {}^{\scriptstyle 1}}\hspace {-0.224em}/\hspace {-0.112em}{\scriptstyle 2},W_{t}}$ </tex-math></inline-formula>, is <inline-formula> <tex-math notation="LaTeX">$\mathit {O}(\ln n)$ </tex-math></inline-formula>. The reliability function of the discrete stationary product channels with feedback is bounded from above by the sphere packing exponent. Both results are proved by first establishing a non-asymptotic SPB. The latter result continues to hold under a milder stationarity hypothesis.
Perceptual Video Content Analysis and Application to HEVC Quantization Refinement

In this paper, we propose a set of perceptual features aiming to consistently describe the visual information. The measurement is performed in the complex frequency domain according to human visual system (HVS) mechanisms. The aim is to explore the performance of these features in a video coding scheme. Particularly, we consider the High Efficiency Video Coding (HEV C) standard as it introduces several efficient tools along with new coding structures. The quantization parameter (QP) is an essential factor that affects the coding performance and has a relationship the Lagrangian multiplier. Based on extracted measures, a perceptual factor is proposed to adjust the Lagrangian multiplier and subsequently, the QP is refined over the adjusted value. The achieved BD-rate savings over several resolutions of video sequences, using the Bjontegaard metric, show the promising coding efficiency of the proposed method with regard to an adequate rate-distortion (R-D) compromise. We opted for the Structural SIMilarity (SSIM) metric to carry out a perceptual R-D comparison. The R-D curves demonstrate that the obtained bitrate savings are associated to convenient quality measures, compared to HEVC anchor and a state-of-the-art QP refinement model.
Real-Time Energy Management of Battery/Supercapacitor Electric Vehicles Based on an Adaptation of Pontryagin's Minimum Principle

The combination of batteries and supercapacitors is promising in electric vehicles context to minimize battery aging. Such a system needs an energy management strategy (EMS) that distributes energy in real-time for real driving cycles. Pontryagin's minimum principle (PMP) is widely used in adaptive forms to develop real-time optimization-based EMSs, thanks to its analytical approach. This methodology leads to an off-line optimal solution, which requires an extra adaptive mechanism for real-time applications. In this paper, a simplification of the PMP method is proposed to avoid the adaptation mechanism in real-time. This new EMS is compared to well-known conventional strategies by simulation. Furthermore, experimental results are provided to assess the real-time operation of the proposed EMS. Simulation and experimental results prove the advantages of the proposed approach by a reduction up to 50% of the batteries rms current on a real-world driving cycle compared to a battery-only EV.
A New Multi-Static System for Microwave Breast Cancer Imaging: Preliminary Design

Experimental microwave imaging systems for breast cancer diagnosis typically employ multi-static antenna configurations. In such systems, large number of antennas, which are positioned around breast at fixed locations, are driven by N-port microwave switching matrix or a N-port vector network analyzer. In either case, multi-static scattering parameters measurements required for breast imaging can be completed very rapidly due to electronic switching between ports. This rapidness is in fact very advantageous from patient perspective. In this work, we investigate design consideration for a cylindrical microwave breast scanner and present a preliminary design that uses low-cost and low-loss dielectric materials for impedance matching between the breast and the antennas.
A 20mV Rectifier for Boosting Internet of Natural Things (IoNT)

Internet of Natural Things (loNT) is a new paradigm coming from IoT that enable Natural Things, as trees, to be connected to the Internet. However, to embed electronic in natural trees is a challenge since the use of chemical batteries is not a reasonable solution. For this, Energy Harvesting, that is, the conversion of ambient energy present in the environment into usable electrical energy, is a very interesting solution, but the low level of generated energy from the ambient energy causes the necessity to develop low-power and low-voltage circuits. A very frequently circuit need in this context is the rectifier since a lot of energy sources generate alternate voltage. In this paper, a 20mV MOSFET-based rectifier based on Latour detector and on Zero-Threshold transistor is introduced. A discrete prototype of the rectifier has been made and experimental results confirm its low-voltage operation when the input voltage supply is about 20 mV or even lower.
0.5V 1OMS/S 9-Bits Asynchronous SAR ADC for BLE Receivers in L80NM CMOS Technology

This work presents the design and measured results of a 9-bit 10MS/s asynchronous SAR ADC at O.5V of supply voltage for Bluetooth Low Energy RF receivers. The ADC was designed in 180nm CMOS technology using low-Vt transistors in order to operate in ultra-low supply voltage. A dynamic comparator and a bootstrapped circuit topologies, adequate for low voltage SAR ADCs with low power operation and moderate speed are proposed in this work. The experimental power consumption of the fabricated circuit is $48\mu W$ at 10MS/s, with 8.55-bit of ENOB and FoM of 13fJ/step.
Cancelling Noise in Multi-Band Digital-Intensive Transmitters

Digital-intensive RF transmitters using delta-sigma modulation have a compact physical footprint, enabling a low-cost mobile MIMO transmitter for 5G systems, but suffer from a high noise floor due to quantization errors. In order to eliminate the need of high-Q multi-band RF filters for attenuating such high noise floor, this paper for the first time presents a multi-band noise cancellation technique for digital-intensive transmitters based on concurrent multi-band delta-sigma modulation. Using an asymmetric RF power combiner, an experimental implementation realizes the bandpass noise cancellation for non-contiguous interband carrier aggregation of 835 MHz and 1450 MHz bands. For 40-MHz aggregated bandwidth, the proposed noise cancellation technique achieves ACPR better than −47 dB and −42 dB for the low-band and the high-band, respectively. With the dual-band prototype with a 6-GHz sampling rate, the proposed technique needs three times less bandwidth for noise cancellation signal compared to conventional feed-forward techniques, and thus the sampling rate requirement on noise cancellation path can be significantly relaxed.
Unsupervised Dialogue Act Classification with Optimum-Path Forest

Dialogue Act classification is a relevant problem for the Natural Language Processing field either as a standalone task or when used as input for downstream applications. Despite its importance, most of the existing approaches rely on supervised techniques, which depend on annotated samples, making it difficult to take advantage of the increasing amount of data available in different domains. In this paper, we briefly review the most commonly used datasets to evaluate Dialogue Act classification approaches and introduce the Optimum-Path Forest (OPF) classifier to this task. Instead of using its original strategy to determine the corresponding class for each cluster, we use a modified version based on majority voting, named M-OPF, which yields good results when compared to k-means and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), according to accuracy and V-measure. We also show that M-OPF, and consequently OPF, are less sensitive to hyper-parameter tuning when compared to HDBSCAN.
Distributed Continuous Control with Meta Learning on Robotic Arms

Deep reinforcement learning has been proposed to train the control agent for robotic arms, such as Deep Q-Learning (DQN) and Policy Gradient (PG). The approach of Deterministic Deep Policy Gradient (DDPG) takes the advantage of deterministic policy instead of stochastic policy to further simplify the training process and improve the performance. Reinforcement Learning takes the reward from the environment and trains the underlying control agent to achieve the task. An appropriate reward will get better performance and shorter training time, but it requires the domain knowledge and the method of trial and error to define the appropriate reward function. In this paper, we proposed a method that is based on DDPG and makes use of Prioritized Experience Replay (PER), Asynchronous Agent Learning and Meta Learning. The proposed Meta Learning approach uses multiple distributed learners, called workers, to learn from consecutive previous states and rewards. Simulations are done on 6-DOF (IRB140) and 7-DOF (LBR iiwa 14 R820) robotic arms to train the control agents to reach random targets in the three dimension space. The experiments show that the algorithm we proposed is better than the algorithm using DDPG with specialized reward function on the task success rate and the training speed.
The Rényi Capacity and Center

Rényi’s information measures—the Rényi information, mean, capacity, radius, and center—are analyzed relying on the elementary properties of the Rényi divergence and the power means. The van Erven– Harremoës conjecture is proved for any positive order and for any set of probability measures on a given measurable space and a generalization of it is established for the constrained variant of the problem. The finiteness of the order <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> Rényi capacity is shown to imply the continuity of the Rényi capacity on <inline-formula> <tex-math notation="LaTeX">$(0,\alpha]$ </tex-math></inline-formula> and the uniform equicontinuity of the Rényi information, both as a family of functions of the order indexed by the priors and as a family of functions of the prior indexed by the orders.
Accelerometer-Based Joint User-Device Clone-Resistant Identity

Unclonable identity, Security, and privacy issues are becoming fundamental requirements for most smart application environment. Recently, several state-of-the-art approaches have been proposed to deal with secured combined user-Smartphone identification. In this paper, we propose a new approach to automatically secure the joint-identity of a Smartphone and its user/owner. The joint identification is reached by linking the user's individual/unique hand movement with an embedded clone-resistant Smartphone identity. The smartphone identity is created as a Secret Unknown Cipher which is manufacturer and possibly operator independent. It is a permanent, unique and clone-resistant physical Smartphone-device identity. The proposed approach creates also a user-dependent biometric identity with relatively high accuracy based on machine learning algorithms. The security against several attacks is evaluated and showed that the proposed approach is robust and able to offer many attractive solutions in future real field applications.
A Cleaning Algorithm for Noiseless Opinion Mining Corpus Construction

This paper presents DyCorC, an extractor and cleaner of web forums contents. Its main points are that the process is entirely automatic, language-independent and adaptable to all kinds of forum architectures. The corpus is built accordingly to user queries using expressions or item keywords as in research engines, and then DyCorC minimizes the boilerplate for further feature-based opinion mining and sentiment analysis, gathering comments and scorings. Such noiseless corpora are usually hand made with the help of crawlers and scrapers, with specific containers devised for each type of forum, entailing lots of work and skills. Our aim is to cut down this preprocessing stage. Our algorithm is compared to state of the art models (Apache Nutch, BootCat, JusText), with a gold standard corpus we released. DyCorC offers a better quality of noiseless content extraction. Its algorithm is based on DOM trees with string distances, seven of which have been compared on the reference corpus, and feature-distance has been chosen as the best fit.
DeepNap: Data-Driven Base Station Sleeping Operations Through Deep Reinforcement Learning

Base station (BS) sleeping is an effective way to reduce the energy consumption of mobile networks. Previous efforts to design sleeping control algorithms mainly rely on stochastic traffic models and analytical derivation. However, the tractability of models often conflicts with the complexity of real-world traffic, making it difficult to apply in reality. In this paper, we propose a data-driven algorithm for dynamic sleeping control called DeepNap. This algorithm uses a deep Q-network (DQN) to learn effective sleeping policies from high-dimensional raw observations or un-quantized systems state vectors. We propose to enhance the original DQN algorithm with action-wise experience replay and adaptive reward scaling to deal with the challenges in nonstationary traffic. We also provide a model-assisted variant of DeepNap through the Dyna framework for inferring and simulating system dynamics. Periodical traffic modeling makes it possible to capture the nonstationarity in real-world traffic and the incorporation with DQN allows for feature learning and generalization from model outputs. Experiments show that both the end-to-end and the model-assisted version of DeepNap outperform table-based ${Q}$ -learning algorithm and the nonstationarity enhancements improve the stability of vanilla DQN.