Miracle-dz/UniFAR-FTUs huggingface dataset:
A Proposal to Detect Anomalies in Connections Installations using Mobile Edge Computing with Deep Learning Approaches

Deep Learning (DL) methods have been applied successfully in Smart Grid systems for the Industry 4.0 era (SGI 4.0). On the other hand, supervising the installation of connectors in the low voltage network distribution is challenging. This service requires hours of human supervision, and its maintenance generates unnecessary costs for the company. Also, there are direct impacts on the energy supplied to the consumer when a poor installation is performed. In this work, the concept of a mobile inspection system based on DL technology to detect anomalies in connector installations automatically is presented. Besides a brief literature review to support this initiative is also presented. This proposal is part of the Connection Quality initiative in the Urban Futurability project developed by ENEL Distribuição São Paulo, aiming to improve the reliability and quality of the SGI 4.0.
A Multimodal Emotion Perception Model based on Context-Aware Decision-Level Fusion

A Multimodal Emotion Perception model with Audio and Visual modalities (MEP-A V) is proposed to detect the individual emotions in public area. The framework of MEP-AV model consists of four parts, i.e., data collection module, audio expression analysis module, visual expression analysis module and multimodal fusion module. In order to ensure that the emotion perception results meet the requirement of short-term continuity, a Context-Aware Decision-Level Fusion (CADLF) model is proposed and applied in multimodal fusion module. The CADLF model estimates the affective status by using context information of multimodal emotion. The short-term continuity is considered to improve the accuracy of the emotion perception results. The experiment results evaluated by various metrics demonstrate that the performance of the multimodal structure is improved compared with that of unimodal emotion classifiers. The MEP-AV model using multimodal fusion algorithm provides the accuracies of 70.89% and 77.07% in valence and arousal respectively. The Fl-scores reaches 70.2% and 75.6% respectively, indicating the boost performance on emotion perception.
THE CHEMICAL COMPOSITIONS OF VARIABLE FIELD HORIZONTAL-BRANCH STARS: RR LYRAE STARS

We present a detailed abundance study of 11 RR Lyrae ab-type variables: AS Vir, BS Aps, CD Vel, DT Hya, RV Oct, TY Gru, UV Oct, V1645 Sgr, WY Ant, XZ Aps, and Z Mic. High-resolution and high signal-to-noise ratio echelle spectra of these variables were obtained with the 2.5 m du Pont telescope at the Las Campanas Observatory. We obtained more than 2300 spectra, roughly 200 spectra per star, distributed more or less uniformly throughout the pulsational cycles. A new method has been developed to obtain the initial effective temperatures of our sample stars at a specific pulsational phase. We find that the abundance ratios are generally consistent with those of similar metallicity field stars in different evolutionary states and throughout the pulsational cycles for RR Lyrae stars. TY Gru remains the only n-capture enriched star among the RRab in our sample. A new relation is found between microturbulence and effective temperature among stars of the horizontal-branch population. In addition, the variation of microturbulence as a function of phase is empirically shown to be similar to the theoretical variation. Finally, we conclude that the derived Teff and log g values of our sample stars follow the general trend of a single mass evolutionary track.
Dual-Hop Mixed FSO-VLC Underwater Wireless Communication Link

Underwater optical wireless communications (UOWCs) are promising and potential wireless carriers to envisage underwater phenomenal activities for various applications towards the futuristic 5G and beyond (5GB) wireless systems. The main challenges to deploy underwater applications are the physicochemical properties and strong turbulence channel conditions. In this regard, the end-to-end (E2E) performance analysis of a dual-hop mixed FSO/UVLC system under the intensity modulation/direct detection (IM/DD) technique in consideration of pulse amplitude modulation (PAM) scheme is investigated. Throughout this study, to tackle the issues of moderate-to-strong turbulence channel conditions, this work deploys the Gamma-Gamma (GG) distribution fading model and the links are designed by unifying plane wave models in the corresponding links, respectively. This investigation outperforms higher achievable data rate with minimal delay response and enhance network connectivity in real-time monitoring scenarios as compared with the traditional underwater wireless communication technologies. In more contrast, the probability distribution function (PDF), cumulative distribution function (CDF), and closed-form expression of the system are derived and presented in terms of Meijer-G function as well as Extended Generalized Bivariate Meijer-G Function (EGBMGF). The significant E2E performance metrics are obtained by employing the decode-and-forward (DF) relay protocol in hostile channel conditions. In aggregating this work, we combine the analytical expressions that present an efficient tool to depict the impact of channel parameters on the system. The simulation results are plausible of the system performance metrics as average BER (ABER) and outage probability $(P_{out})$ in the presence of pointing and without pointing error events. Finally, in this work, we use the Monte-Carlo approach for the best fitting curves and validate the numerical expression yields simulation results.
Recognition for AER Objects Based on Hierarchical Feature Construction

Address event representation (AER) sensors are used to capture dynamic objects ignoring the static redundant information. But how to extract features efficiently from the asynchronous sparse event-driven data with high temporal resolution becomes a problem. In this study, a hierarchical recognition system is proposed to classify AER objects from the event flow. Motion symbol detection (MSD) is applied to adaptively separate the dense event flow into multiple segments with sufficient spatio-temporal information. A hierarchical model composed of two layers is used to construct feature maps based on specified orientations. A convolutional neural network is coupled with the model to further extract feature and classify objects. In the experiment based on MNIST-DVS dataset, it reaches a high accuracy of 99.85% in training and 92.5% in testing.
Evaluation and Analysis of Circuit Model for Lithium Batteries

Lithium battery is commonly used in energy storage system because of its long service life and high energy density. In this paper, five common equivalent circuit models of lithium batteries are evaluated and analyzed. By utilizing the hybrid pulse power characteristic (HPPC) method, the parameters identification of each circuit model is achieved through curve fitting and then their simulation models are established in Matlab/Simulink. By comparing the simulation results with testing data, the performance of each circuit model is discussed. The results show that the second-order RC model matches well with test data, while the rest four models have a relatively larger error. It should be noted that the main contribution of this paper is to analyze the performance of different circuit models for lithium battery, the accuracy of each circuit model may vary with each specific lithium battery. The optimum type of lithium battery circuit model shall be selected accordingly. Key Words: Lithium Battery, Equivalent Circuit Model, Parameter Identification
A Robust Edge Detection Algorithm for Finger Vein Recognition

By extracting vein patterns under the skin of fingers for identifying the authentication, finger vein recognition is a promising biometric recognition technology. A particularly critical step in locating the region of interest of the finger vein image is the edge detection of the finger. Aiming at the problem that the existing algorithm has low detection accuracy on low-quality finger vein images, this paper proposes a robust edge detection algorithm. Firstly, the upper and lower edges of the horizontally placed fingers are detected by using the upper and lower convolution kernels, and then the obtained edges are subjected to erroneous edge judgment and filtering denoising. Finally, edge fitting is performed by BP neural network to repair the wrong finger boundary. The experimental results show that the proposed algorithm can robustly detect the finger edge of low-quality finger vein images.
TWO EFFICIENT, NEW TECHNIQUES FOR DETECTING DISPERSED RADIO PULSES WITH INTERFEROMETERS: THE CHIRPOLATOR AND THE CHIMAGEATOR

Searching for dispersed radio pulses in interferometric data is of great scientific interest, but poses a formidable computational burden. Here, we present two efficient, new antenna-coherent solutions: The Chirpolator and The Chimageator. We describe the equations governing both techniques and propose a number of novel optimizations. We compare the implementation costs of our techniques with classical methods using three criteria: the operation rates (1) before and (2) after the integrate-and-dump stage, and (3) the data rate directly after the integrate-and-dump stage. When compared with classical methods, our techniques excel in the regime of sparse arrays, where they both require substantially lower data rates, and The Chirpolator requires a much lower post-integrator operation rate. In general, our techniques require more pre-integrator operations than the classical ones. We argue that the data and operation rates required by our techniques are better matched to future supercomputer architectures, where the arithmetic capability is outstripping the bandwidth capability. Our techniques are, therefore, viable candidates for deploying on future interferometers such as the Square Kilometer Array.
Semantic Characteristics of Schizophrenic Speech

Natural language processing tools are used to automatically detect disturbances in transcribed speech of schizophrenia inpatients who speak Hebrew. We measure topic mutation over time and show that controls maintain more cohesive speech than inpatients. We also examine differences in how inpatients and controls use adjectives and adverbs to describe content words and show that the ones used by controls are more common than the those of inpatients. We provide experimental results and show their potential for automatically detecting schizophrenia in patients by means only of their speech patterns.
Observer-based Event-triggered Control for a Class of Networked Control Systems under Denial of Service Attacks

This paper is concerned with the observer-based control design for a continuous linear networked control systems under denial of service attacks. In order to save network communication resources, a new flexible event-triggered control strategy is designed on the premise that denial of service attacks are power-limited pulse width modulation interference. Considering this influence of denial of service attacks on event-triggered state, the maximum system performance lost is calculated. The sufficient conditions of system stability are derived by using the Lyapunov functional method. The constructive design of the controller is expressed in terms of linear matrix inequalities. Finally, the theoretical results are verified by a simulation example.
The Finite-Time Stabilization of Nonlinear Systems with Output Constraint

This article considers the problem of finite-time stabilization for a nonlinear system with an output constraint. Without considering external disturbances, a finite-time controller is proposed to control the uncertain nonlinear dynamics of second-order systems. The innovation of this paper is to use tan-type barrier Lyapunov function (BLF) to handling the output constraint and establish the new relational expression of nonlinear function. Besides, adding a power integrator is also applied to build controller. Theoretically, Lyapunov stability theory is used to show that the system state is finite time stable under the proposed control scheme. To verify the derived theoretical results, we conduct a simulation case study.
A Reconfigurable Deep Neural Network on Chip Design with Flexible Convolutional Operations

The designs of deep neural network (DNN) accelerators have gradually gained attention due to the increased demand for real-time AI applications. On the other hand, due to the diverse applications, kernel sizes and shapes for the involved convolutional operation in the target DNN model are not fixed. Therefore, it is necessary to design a reconfigurable DNN accelerator to cover different kernel sizes for convolutional operation in DNNs. However, due to the worst-case design policy, the designers usually select the largest kernel size as the design parameter to implement the DNN accelerator, which leads to lower hardware utilization. The reason is that the conventional array-based DNN design method restricts the efficiency of data delivery. Besides, the complicated data flow between neuron layers of DNN models counteracts the benefit of the involved data reuse method. To mitigate the design problems of complicated data flow on DNN accelerators, Network-on-Chip (NoC) interconnection has become an emerging technology to realize the Deep Neural Network on Chip (DNNoC). Compared with the conventional array-based DNN acceleration design, the DNNoC design supports flexible data flow, which leverages reconfigurable DNN accelerator implementations. In this work, we leverage the flexible NoC interconnection and propose a hybrid input/weight reuse method to reduce memory access. In addition, our proposed hybrid input/weight reuse method supports arbitrary kernel sizes for flexible convolutional operations. Compared with the related works, the proposed reconfigurable DNNoC with flexible convolutional operations helps to improve the utilization of computational capability in a PE by 1 % to 34 %, reduce memory access by 66% to 85%, which helps to improve 40% to 117% throughput.
Micronutrient-Fortified Drinks: Commercial Products and Compelling opportunities

Micronutrients are vitamins and minerals needed by the body in small amounts to produce enzymes and hormones necessary for normal development. Children and pregnant women in developing countries bear a disproportionate burden of micronutrient deficiencies that lead to severe and life-threatening conditions. Besides educating families about nutrition and consuming balanced diets from locally-available foods, food fortification and supplementation are necessary. The majority of fortified food products are solid foods and the literature on fortifying liquids is sparse. The growing popularity of energy drinks, carbonated beverages, and colorful flavored drinks presents new opportunities for fortified drink products. This article provides a narrative review of micronutrient-fortified drink products that are 1) commercially-available, and 2) under research and development (R&D). A total of three commercial products, all of which are sold in powdered form for the customer to dissolve in water, were studied. The commonly targeted micronutrients were vitamins A, B, C, iron, zinc, and calcium. The four R&D products identified through an exhausting literature search were all liquids, with a particular emphasis on delivering Vitamin A to children. The paper concludes with practical recommendations on socio-cultural, economic, and health considerations for social entrepreneurs seeking to introduce fortified drink products in emerging markets.
Gait Generation for the Multi-legged Robot with Injured Legs

When multi-legged robots work in dangerous and complex environments, their legs are easily damaged, resulting in a sharp decline in their movement ability, or even completely out of control. In this paper, the gait generation method combined deep reinforcement learning with bionic gait restrictions is presented for multi-legged robots with injured legs. In this study, we first built an ant gait video acquisition platform using the Camponotus Japonicus as the research object, and on this basis established a gait dataset of injured ants. Then, the gait characteristics of ants in the injured state were investigated. Finally, our proposed gait generation method combining bionic gait constraints with deep reinforcement learning was compared with the reinforcement-learning-based method in the MATLAB simulation environment. The test results show that the training reward values of our proposed algorithm are 1–2 times higher than those of the reinforcement learning method alone, and at the same time, the moving speed of the injured robot is also nearly twice as fast.
Research on the Face Silent Liveness Algorithm Based on Auxiliary Information

With the rapid development of artificial intelligence and computer vision, face recognition system has been widely used in the field of internet finance to achieve digital authentication and the security of systems has received attention increasingly. As an important part of the face recognition system which is used to resist the attack, the liveness detection will directly affect the security level of the whole system. In order to improve the existing silent liveness algorithms with shortcomings, such as insufficient features, over-fitting easily and lacking of interpretability, this paper proposes a face silent liveness detection algorithm based on multi -dimension auxiliary information and the multi -dimension information, such as depth map, reflection map and spectral map, are introduced as the auxiliary supervision in the liveness algorithm. In order to verify the validity of the proposed liveness detection algorithm, multiple comparative experiments are set up and different kinds of auxiliary information are introduced for comparisons. All experiments are conducted on the CelebA-Spoof data set. The experimental results are highly consistent with the theoretical expectations of the algorithm design, which provides more interpretability for the liveness detection.
Reliable observer-based control against sensor failures for fractional order systems

In the paper, the state and sensor failure observer for fractional order systems with order 0 < a < 1 and 1 < a < 2 is proposed to estimate the system state and sensor failures at the same time. In general, unbounded sensor faults will inevitably lead to system failures. Therefore, it is very necessary to study the reliable controller for fractional order systems against sensor failures. Applying the linear matrix inequality concept and the augmented system method, the sensor faults can be in any form, even unbounded. A reliable observer-based controller based on the state and sensor failures estimation is proposed, which can make the system work well regardless of whether the sensor failures. When a = 1, methods proposed in the paper are equivalent to these of the integer order systems, scilicet, our methods can be extended to integer order systems. Finally, a simulation example is used to demonstrate the efficiency of the present methods.
First Detection of 12CO (1→ 0) Emission from Two Narrow-Line Seyfert 1 Galaxies

In order to investigate how the growth of galactic bulges progresses with the growth of central black holes (BHs), we observed molecular gas (fuel for the coming star formation) in possibly young active galaxies: narrow-line Seyfert 1 galaxies (NLS1s). We present the results of radio observations of 12CO (1→ 0) using the Nobeyama Millimeter Array (with 2-4 kpc spatial resolution) for two FIR-bright NLS1s, yielding the first detection of their CO emission. Corresponding molecular gas masses M(H2) of (1–3) × 109 M☉ are the second and fourth largest among NLS1s. By estimating dynamical masses and bulge masses Mbulge for these two NLS1s using CO channel maps and CO line widths, we found that M(H2) amounted to 13%-35% of these masses. Taking into account the star formation efficiency (~0.1), the increase in Mbulge in those NLS1s in the near future (≲107.5 yr) is not expected to be a huge fraction (1%-5% of the preexisting stars). Bulge growth may have finished before BH growth, or bulge-BH coevolution may proceed with many occasionally discrete events, where one coevolution event produces only a small amount of mass growth of BHs and bulges. We also discuss the ratios of star formation rate to gas accretion rate onto BHs, finding that two NLS1s have very small ratios (≈1) compared with the Mbulge/MBH ratios found in active and inactive galaxies (≈700). This huge difference suggests either nonoverlapped coevolution, a long star formation duration, or a temporarily high accretion rate during the NLS1 phase.
Quark Mass Textures and sin 2 beta

Recent precise measurements of $\mathrm{sin}2\ensuremath{\beta}$ from the B factories (BABAR and BELLE) and a better known strange quark mass from lattice QCD make precision tests of predictive texture models possible. The models tested include those hierarchical N-zero textures classified by Ramond, Roberts, and Ross, as well as any other hierarchical matrix Ans\"atze with nonzero $12=21$ and vanishing 11 and 13 elements. We calculate the maximally allowed value for $\mathrm{sin}2\ensuremath{\beta}$ in these models and show that all the aforementioned models with vanishing 11 and 13 elements are ruled out at the $3\ensuremath{\sigma}$ level. While at present $\mathrm{sin}2\ensuremath{\beta}$ and $|{V}_{\mathrm{ub}}{/V}_{\mathrm{cb}}|$ are equally good for testing N-zero texture models, in the near future the former will surpass the latter in constraining power.
SCVO: Scale-Consistent Depth and Pose for Unsupervised Visual Odometry

The technology for Visual Odometry (VO) that estimates the position of moving objects through image sequences, has drawn significant attention in visual localization. The performance of current VO models is limited by the fragility of photometric consistency as the supervisory signal, which causes the accumulation of scale errors over time. Moreover, unidentified moving objects cause noisy signals during training. To tackle these challenges, we propose a novel monocular visual odometry model called SCVO, which requires only unlabeled video sequences and achieves scale-consistent pose estimation. Specifically, considering the relevant information of adjacent pixels, a improved depth consistency loss is introduced into our framework. Based on the original depth information constraints, we integrate SSIM to constrain the depth consistency of adjacent frames from both local and global perspectives. Based on source images and warped images, we further propose a depth-consistency mask to exclude mismatched pixels caused by dynamic objects and occlusions. Experiments on the KITTI dataset show that the SCVO has achieved superior performance than recently developed VO models.
Event-based tracking of nonholonomic systems in chained form

This paper investigates an event-triggered tracking control problem of nonholonomic systems in chained form. We first design a time-varying controller while ignoring the sampling/triggering. Then, the event-triggered control strategy is considered and a dynamic event trigger is proposed to reduce the information transmission. The solutions of nonholonomic systems in chained form are proved to be uniformly globally asymptotically tracking the desired reference trajectory under some standing assumptions. Our analysis is based on Lyapunov's direct method and an explicit global strict Lyapunov function is constructed. Furthermore, the Zeno behavior is avoided and a positive lower bound between any two triggering instants is ensured. Finally, a simulation example is provided to illustrate our results.