Miracle-dz/UniFAR-FTUs huggingface dataset:
A 220 GHz Antenna Array Based on the Half-Mode Corrugated Substrate Integrated Waveguide

A 220 GHz antenna array based on the half-mode corrugated substrate integrated waveguide (HMCSIW) is designed and simulated in this work. In CSIW transmission line, two rows of open-ended microstrip stubs spaced on the both sides evenly. And TE10 mode can be also transmitted. The magnetic-wall of the TE10 is on longitudinal axis, thus the half-mode (TE0.5,0) can be obtained by cutting the CSIW structure in half. For the TE0.5,0 mode, the maximum electric field strength is uniformly distributed on open side. A series of half-wavelength microstrip antennas are integrated at these locations to form an antenna array. This antenna array operates at 220 GHz, which is in THz band. Through simulation, the maximum gain and S11 of this antenna is 10.88 dBi and -16.7 dB at 220 GHz, respectively. Due to the good performance and the simple structure of HMCSIW, this antenna is suitable for communication in the THz band and can also greatly reduce the processing complexity.
Attention-Based Bidirectional LSTM With Differential Features For Disk RUL Prediction

With the rapid growth in the number of disks, disk failures are increasingly becoming a problem for data centers. To improve the reliability and security of the data center, deep learning methods have been widely used by performing the remaining useful life (RUL) prediction of hard disk drives (HDD). However, deep learning methods fail to deal with the long sequence data and extract the crucial degradation information. In this paper, an attention-based bidirectional long short-term memory (LSTM) with differential features method is proposed, in which the differential features are extracted by manual feature engineering, and then apply the attention-based bidirectional LSTM network to assign higher weights to crucial features that contain useful degradation information for RUL prediction. Experiments results on the Backblaze dataset show that the proposed approach outperforms the traditional LSTM methods, and achieves a 97.83% failure detection rate (FDR) to predict RUL of HDDs up to 60 days before failure.
Human-like Walking of Biped Robot with Foot Rotation Using Passive Metatarsophalangeal Joint*

In the process of walking, humans are accompanied by the important action of foot rotation. The foot rotates taking the heel and toe as the axis of rotation when landing on and leaving the ground, respectively. Although having high similarity to humans, many biped robots do not reproduce this important feature. This paper describes a method for adding foot rotation into biped robots using the passive metatarsophalangeal joint. Firstly, the biomechanics of human metatarsophalangeal joint during walking is analyzed. Then, the control strategy based on finite state machine for human-like biped walking with foot rotation is proposed for a simplified biped robot model that is composed of eight joints. Finally, the simulation results of the walking gait of the biped robot in MATLAB demonstrate the effectiveness of the control strategy.
Acoustic Model Adaptation In Reverberant Conditions Using Multi-task Learned Embeddings

Acoustic environment plays a major role in the performance of a large-scale Automatic Speech Recognition (ASR) system. It becomes a lot more challenging when substantial amount of distortions, such as background noise and reverberations are present. Of late, it has been standard to use i-vectors for Acoustic Model (AM) adaptation. Embeddings from Single Task Learned (STL) neural network systems, such as x-vectors and r-vectors, have also been used to a varying degree of success. This paper proposes the use of Multi Task Learned (MTL) embeddings for large vocabulary hybrid acoustic model adaptation in reverberant environments. MTL embeddings are extracted from an affine layer of the deep neural network trained on multiple tasks such as speaker information and room information. Our experiments show that the proposed MTL embeddings outperform i-vectors, x-vectors and r-vectors for AM adaptation in reverberant conditions. Besides, it has been demonstrated that the proposed MTL-embeddings can be fused with i-vectors to provide further improvement. We provide results on artificially reverberated Librispeech data as well as real world reverberated HRRE data. On Librispeech database, the proposed method provides an improvement of 10.9% and 8.7% relative to i-vector in reverberated test-clean and test-other data respectively, while an improvement of 7% is observed relative to i-vector when the proposed system is tested on HRRE dataset.
Design and development of IoT based Gateway for Advanced Metering Infrastructure in an Educational Institution

The world currently focuses on research and development in various domains and exploring new technologies. An improvement is needed in any technology that is framed, to study or analyze the technology a basic pre-requisite is to study the basics of the topic. So, there is a need for the basic data related to the topic in order to enhance the work and make a proper development, by analyzing the data, various improvements can be done. For this enhancement or improvement of work, the data is necessary at various points where the data is to be sent and necessary changes are found and the changes are rectified and completed. The paper proposes a new way of data transfer, particularly focusing on smart meter data. Smart meter data is a large dataset as it records data for every minute of load consumption in a building. Usually, large data which is sent are disrupted and corrupted and loss of data occurs. This paper designs an Internet of Things (IoT) gateway to send data without any disruption of data. The data is sent as a large file and it is also designed to transfer as a low bandwidth, high latency data sending protocol and this large data is also sent with no disruptions. The paper transfers Thiagarajar college smart meter data from a local server to a designed IoT gateway and data is used for various purposes like analyzing energy consumption hourly, daily, and weekly load usage.
A Hybrid Approach to detect and characterize Alzheimer’s Disease using Robust PCA and Random Forest Algorithm

Alzheimer's disease is a neurological degenerative disorder that impacts the memory potency and entire function of the human brain that leads to Dementia. It is commonly regarded as the seventh leading cause of mortality in the globe. Despite this, there is still no solid test to diagnose this condition. The focus of this thesis is to imply a new hybrid model for better detection and categorization Alzheimer's disease. The integration of brain MRI (Magnetic Resonance Imaging) data in Computerized Algorithms helps with the rapid recognition and segmentation of Alzheimer's disease. Data preparation, Data Augmentation, Feature extraction, and Classification are the four different phases outlined in the approach. We applied PCA(Principal Component Analysis) method for feature extraction and Random Forest Algorithm as a classification technique to cluster MRI images to achieve flawless results. Those image pixels are clustered into four leverages using the proposed approach. MildDemented, VeryMildDemented, NonDemented and ModerateDemeneted are among the four Alzheimer categories for which the algorithm has been implemented. The proposed system's efficiency is determined using the Kaggle Alzheimer's dataset. The results suggest that this approach has a high sensitivity for detecting lesion location in the brain with an overall accuracy level of 99.95%.
False Discovery Rate Control for Grouped Variable Selection in High-Dimensional Linear Models Using the T-Knock Filter

High-dimensional variable selection is a challenging task, especially when groups of highly correlated variables are present in the data, such as in genomics research, direction-of-arrival estimation, and financial engineering. Recently, the T-Knock filter, a new framework for fast variable selection in high-dimensional settings has been developed. It provably controls the false discovery rate (FDR) at a given target level. However, its current version does not consider groups of highly correlated variables, which can lead to a loss in the true positive rate (TPR), i.e., the power. Hence, we propose the T-Knock+GVS filter that allows for grouped variable selection with FDR control in such settings. This is achieved by modifying the forward variable selection algorithm within the T- Knock filter and by adjusting the knockoff generation process such that the generated sets of knockoffs mimic the group correlation structure within the original set of variables. For a special case, we prove that the proposed $\mathrm{T}-\text{Knock}+\text{GVS}$ filter possesses the grouped variable selection property. Through a simulated high-dimensional genome-wide association study (GWAS), we show that the proposed method significantly increases the TPR, while controlling the FDR at the target level.
Microsecond Pulse Generation in an Actively Q-Switched All-PM Fiber Laser Based on an Electro-Optic Modulator

Active Q-switching of an all polarizationmaintaining (All-PM) erbium fiber laser by an electro-optic modulator (EOM) is reported. Cavity loss modulation is achieved via an external modulation signal generated from an electrical signal source. The Q-switched pulsed laser operates in the $\displaystyle \mathbf{1.5\ \mu m}$ region with 0.83 nm linewidth. The shortest pulse width obtained is $\displaystyle \mathbf{1.06\ \mu s}$. We believe that this combination between all-PM configuration and EOM offers an ideal method to design single-polarization pulsed laser sources with high environmental stability towards temperature variations and mechanical perturbations.
Physiological Parameters Extraction by Accelerometric Signal Analysis During Sleep

Sleep quality is an index of well-being, since sleep disorders, such as sleep apnea, may constitute a health risk. A constant monitoring of subjects, especially when there are heart or respiratory diseases, is essential. The present paper aims to offer a non-invasive and comfortable sleep monitoring, by employing a BallistoCardioGraphic (BCG) signal processing. In particular, with a BCG device located below the mattress, we are able to extract the heart rate, respiratory rate and, therefore, to exploit this information to develop an automatic sleep apnea recognition algorithm. The automatic approach presented has proven to achieve accuracy and reliability and could represent a valid resource to prevent serious damages during sleep.
Fractals and Wavelets Based Energy Analysis of Cost-Balanced LEACH Sensor Network

Wireless Sensor Networks(WSNs) have made significant strides in recent years owing to their growth in terms of equipment and cost reduction. Several protocols have been designed, based on the application and network architecture. The LEACH (Low Energy Adaptive Clustering Hierarchy) mechanism is one of the most energy-efficient solutions in WSN environments. This hierarchical protocol aggregates and forwards data from cluster members to a fixed sink node using the cluster head feature of the nodes. We propose a new family of routing mechanisms called CB-LEACH by introducing movement possibility to the sink node and balancing the cluster head election decision based on the distances between the nodes and on the remaining energy of the potential cluster head candidates. We introduced the Hausdorff dimension, memory exponent, and common metric of fractality metrics to characterize the new routing mechanism It is proven that these metrics can highlight the most important features of the newly proposed CB-LEACH system
Algorithms to search for point-like neutrinos sources with the ANTARES telescope

The detection of astrophysical sources of high energy neutrinos is one of the main motivations for building a neutrino telescope. The ANTARES telescope is being deployed in the Mediterranean Sea. The detector will consist in a tridimensional array of photomultipliers (PMTs) that will detect the Cherenkov light induced by the muons produced in neutrino interactions. Since the neutrino fluxes from point-like sources are expected to be small, it is of the utmost importance to take advantage of the ANTARES accurate pointing power (angular resolution less than 0.3 degrees for greater than 10 TeV) to disentangle a possible signal from the unavoidable atmospheric neutrino background. In order to distinguish an excess of neutrino events from the background, several search algorithms have been developed within the ANTARES collaboration. Some of them perform binned searches of clusters, some others use likelihood maximization techniques and recently a method based on the well known Expectation-Maximization clustering algorithm has been devised. In this contribution, these different methods are reviewed and their discovery potential using the ANTARES neutrino telescope are presented.
Ambiguous tDCS: variability of the brain response to transcranial direct current stimulation in reinforcement learning task

Transcranial electrical stimulation (TES) is a popular approach for studying and modulating cortical function. Currently, numerous studies use TES in behavioral experiments with no physiological control. The authors of such studies rely on the assumption of fairness and complete predictability of their stimulation models. However, control reveals the actual effect induced by the stimulation rarely matches the one previously modeled. Our study illuminates diverse effects caused by tDCS and brings up the importance of physiological control when including TES in the research design.
Fast Sampling for Large-scale Linear Inverse Problems via Enlarging Principle Submatrix

Sampling for linear inverse problems is the active selection of partial observations so that the entire physical signal with linear model can be well recovered. In this paper, we will propose a fast sampling algorithm for large-scale linear inverse problems. Specifically, assuming that the field signal f is represented by a linear model $\mathrm{f}=\Phi \mathrm{g}$, it can be estimated from partial noisy samples via an unbiased least-squares (LS) method, whose expected mean square error (MSE) depends on chosen samples. First, we formulate an approximate MSE problem, and then prove it is equivalent to an optimization of a principle submatrix of $\Phi\Phi^{\mathrm{T}}$ indexed by sample set. By enlarging the submatrix greedily, we solve the proposed combinatorial problem with simple matrix-vector multiplications, leveraging a matrix inverse lemma. To further reduce complexity, we reuse results in the previous greedy step for warm start, so that candidates can be evaluated via lightweight vector-vector multiplications. Extensive experiments show that our proposed sampling method achieved the least execution time and the best performance compared to state-of-the-art schemes, especially for large-size scenarios.
Load Day-ahead Prediction of Integrated Energy System Based on VMD-MLR-TCN

Aiming at the randomness and uncertainty of load variation of large-scale user-level integrated energy system, a prediction model based on VMD-MLR-TCN is proposed in this paper. First, the original load was reconstructed into 24 sub-sequences according to the different data at the same time, and then decomposed into multiple modal components by Variational Mode Decomposition (VMD). Each modal component was analyzed by sample entropy (SE), the high frequency component was predicted by time convolution network (TCN), and the low frequency component was predicted by multiple linear regression (MLR). Finally, compared with VMD-TCN, VMD-GRU and CEEMDAN-TCN, the model proposed in this paper has higher advantages in electricity, heat and cooling load prediction.
An Unmanned Vehicle Inspection System for Airport Runways

Foreign objects on the airport runway can affect the takeoff of the plane and seriously endanger the safety of people's lives and property. The existing solutions for these foreign bodies on the runway of the airport is manual cleaning during the take-off interval of the aircraft, but this method has high labor cost, low efficiency and poor reliability. In this paper, an unmanned vehicle inspection system for airport runways is introduced to address the above problems, which is used to detect runway foreign objects at airport runways. This paper introduces the hardware configuration of the unmanned vehicle and its software functions such as automatic cruise and path planning in detail, and briefly introduces the foreign object detection function. Among them, a new aggregated Euclidean clustering method suitable for sub-regional adaptive thresholds of fused point clouds is proposed when performing lidar point cloud clustering. Experiments show that this method can greatly improve the effect of point cloud clustering for obstacles detection.
Digital Payment Technology for SMEs: Can it be Adopted and Use Properly?

The presence of financial technology provides many conveniences for payment transaction activities. SMEs have tried to adapt, but the number of SMEs using digital payment facilities is still very small, adaptation to financial technology carried out by SMEs is dominated by capital activities, not transactions with customers; This condition is very contradictory to the condition of consumer behavior which has entered the digital economy era. The data was taken through a questionnaire distributed to SMEs actors, a total of 364 data could be analyzed further. The results of the study found that SMEs had hopes and intentions to use digital payments, encouragement from people around them, and consumer demands made SMEs dare to adopt digital payments. Another finding is that SMEs need facilitating conditions, technology adoption is not easy for SMEs, they need a long time, and they need additional efforts to understand and experiment with this technology.
Auto-weighted Sequential Wasserstein Distance and Application to Sequence Matching

Sequence matching problems have been central to the field of data analysis for decades. Such problems arise in widely diverse areas including computer vision, speech processing, bioinformatics, and natural language processing. However, solving such problems efficiently is difficult because one must consider temporal consistency, neighborhood structure similarity, robustness to noise and outliers, and flexibility on start-end matching points. This paper presents a proposal of a shape-aware Wasserstein distance between sequences building upon optimal transport (OT) framework. The proposed distance considers similarity measures of the elements, their neighborhood structures, and temporal positions. We incorporate these similarity measures into three ground cost matrixes of the OT formulation. The noteworthy contribution is that we formulate these measures as independent OT distances with a single shared optimal transport matrix, and adjust those weights automatically according to their effects on the total OT distance. Numerical evaluations suggest that the sequence matching method using our proposed Wasserstein distance robustly outperforms state-of-the-art methods across different real-world datasets.
A Feature-based Approach on Contact-less Blood Pressure Estimation from Video Data

Conventional blood pressure monitors and sensors have several limitations in terms of accuracy, measurement time, comfort or safety. To address these limitations, we realized and tested a surrogate-based contact-less blood pressure estimation method which relies on a single remote photoplethysmogram (rPPG) captured by camera. From this rPPG signal, we compute 120 features, and perform a sequential forward feature selection to obtain the best subset of features. With a multilayer perceptron model, we obtain a mean absolute error ± standard deviation of MAE $5.50\pm 4.52$ mmHg for systolic pressure and $3.73\pm 2.86$ mmHg for diastolic pressure. In contrast to previous studies, our model is trained and tested on a data set including normotensive, pre-hypertensive and hypertensive values.
GPS Signal Acquisition Based on Multichannel Frequency Modulation and Compressive Sensing

A new Global Positioning System signal acquisition method based on multichannel frequency modulation and compressive sensing (CS) is proposed. Spread spectrum is a key component of GPS which is consistent with random sensing of CS. The Gold code has high auto correlation at zero lag, and nearly no correlation at nonzero lag or with another satellite Gold codes. The sparse correlation characteristic of Gold codes is acquisition principle, which can be used as sparse signal reconstruction of CS. Firstly, the GPS carrier can be stripped by multichannel frequency modulation. The compressed signal is obtained by low-pass filter and low rate sampling. Finally, sparse dictionary based on spread spectrum code are designed, and a new acquisition method is designed to capture the GPS signal from the compressed data. Simulation experiments have verified the practicability of the new proposed algorithm. The new method can acquire GPS signal faster than traditional parallel acquisition method.
A Neural Network Approach for Ultrasound Attenuation Coefficient Estimation

Quantitative ultrasound (QUS) imaging complements the standard B-mode images with a quantitative represen-tation of the target's acoustic properties. Attenuation coefficient is an important parameter characterizing these properties, with applications in medical diagnosis and tissue characterization. Traditional QUS methods use analytical models to estimate this coefficient from the acquired signal. Propagation effects, such as diffraction, which are difficult to model analytically are usually ignored, affecting their estimation accuracy. To tackle this issue, reference phantom measurements are commonly used. These are, however, time-consuming and may not always be feasible, limiting the existing approaches' practical applicability. To overcome these challenges, we leverage recent advances in the deep learning field and propose a neural network approach which takes the magnitude spectra of the backscattered ultrasound signal at different axial depths as the input and provides the target's attenuation coefficient as the output. For the presented proof-of-concept study, the network was trained on a simulated dataset, and learnt a proper model from the training data, thereby avoiding the need for an analytical model. The trained network was tested on both simulated and tissue-mimicking phantom datasets, demonstrating the capability of neural networks to provide accurate attenuation estimates from diffraction affected recordings without a reference phantom measurement.
A Dual-polarized Patch Antenna Array with The Decoupling Structure with Polarization Rotation

In this paper, a 1 × 2 dual-polarized patch antenna array with the decoupling structure with polarization rotation (DSPR) has been proposed. A dual-polarized patch antenna is realized by two double torsion coil feeding (DTC) structures. And the band of the antenna is from 3.24 GHz to 3.86 GHz with a high isolation better than 25 dB. The DSPRs are placed above the antenna array to reduce the mutual coupling. Finally, the coupling between co-polarization is decreased from −19 dB to −26 dB and the coupling between cross-polarization is reduced from −22 dB to −25 dB. As a consequence, the antenna array has a wide frequency band spanning from 3.44 GHz to 3.83 GHz with high isolation.