Miracle-dz/UniFAR-FTUs huggingface dataset:
ML Algorithm Performance to Classify MCS Schemes During UACN Link Adaptation

This research classifies the modulation and coding rate for link adaptation in Underwater Acoustic Communications Networks (UACNs). Recently, the UACN has become a promising technology for military, commercial, and civilian applications, as well as scientific research. However, we should minimize the dataset dimension for real-time implementation due to the sensor nodes’ energy limitations in the underwater environment. We used an Incheon sea trial’s measured dataset of 18 features, applying Principal Component Analysis (PCA) to select the dominant eigenvalue components in order to reduce the curse of dimensionality, and then selected 11 parameters. After that, we applied Machine Learning (ML) algorithms with different combinations of the parameters to separately classify the modulation and the coding rate and measured both individual and overall classification accuracy. The findings are compared with two Taean sea trial datasets with 11 features to finalize the selected parameters for link adaptation. For modulation classification, we observed 96.83% accuracy with the K-nearest Neighbors (KNN) algorithm in three-parameter and two-parameter cases. In coding rate classification, we found 100% accuracy with the KNN algorithm using the same three-parameter case. However, for the best fit among the three datasets, we finalized another three parameters at the expense of accuracy. To find the optimum threshold values for all modulation and coding rate labels, we used Rule-based (RB) 2D and 3D analysis. However, with a hard limit on non-overlapping data, at best, 35.51% classification accuracy was found for a 1/3 coding rate (Turbo code) with QPSK modulation, which showed much less reliability for RB analysis in a UACN, so it is not useful in this regard. Besides, our analysis shows data independence in the Doppler Spread (DS) and the Frequency Shift (FS), mitigating the time-variability channel’s challenge. We use the Gaussian distribution plot, a confusion matrix, multi-dimensional scatter plots, interpolated plots to analyze the data.
An Approach Adopting Ethereum as a Wallet Domain Name System within the Economy of Things Context

The interest in blockchain technologies has been growing in the last years. Decentralization, scalability, and data integrity are characteristics that can solve problems in different application fields. However, some issues need to be addressed, aiming to facilitate using these systems. Every day the number of users increases, and the possibility of using multiple cryptocurrencies, with several wallets in each one, also brought an issue: How to manage all these addresses? This work proposes a novel architecture, named Wallet Domain Name System (WDNS), to handle several wallets and contracts in different blockchains. Using the Ethereum network to develop a DNS approach, the WDNS uses smart contracts to store and resolve domains and enable multiple subdomains that are managed by the users. Our work provides an open and free data architecture where any person and system can connect and consume. The initial tests showed an average transaction time of almost 15 seconds and a price of 3.30 USD plus 0.0012 USD per character for the domain requests. Also, the tests showed a fixed cost of 0.71 USD plus 1 USD per each synchronized instruction. Comparing the proposed domain price and the average renewal prices for internet domains makes it possible to ensure our proposal’s feasibility.
LncRNAs and Protein-coding Genes Expression Analysis for Myelodysplastic Syndromes Diagnoses

Myelodysplastic syndromes(MDS) is a genetic disease that affects stem cells in the bone marrow. It infects around 10,000 people yearly in the united states alone [1]. Studying the genes of patients carrying MDS in contrast to healthy genes would provide insights on how to diagnose and treat it. Biologists are recently studying both protein-coding and non-coding genes roles in MDS through gene expression data. In this paper, two gene expression datasets are obtained and processed to identify the most statistically significant genes that contribute to the best clustering results. Those genes are further analysed to study their deferential expression levels. The paper concludes that the majority of the selected features are deferentially expressed and provide a high Adjusted Rand Index (ARI) score(i.e. exceeding 0.8 for both datasets) when used for clustering MDS and control groups. We propose these genes to be further studied by the biologists for identifying their potential relation to MDS disease and possibly assist in MDS diagnosis.
A Regularized RLS Algorithm for the Identification of Third-Order Tensors

The recently proposed recursive least-squares (RLS) algorithm for trilinear forms, namely RLS-TF, was designed for the identification of third-order tensors of rank one. In this context, a high-dimension system identification problem can be efficiently addressed (gaining in terms of both performance and complexity) based on tensor decompositions and modelling. In this paper, following the framework of the RLS-TF, we propose a regularized version of this algorithm, where the regularization terms are incorporated within the cost functions. Furthermore, the optimal regularization parameters are derived, aiming at attenuating the effects of the system noise. Simulation results support the performance features of the proposed algorithm, especially in terms of its robustness in noisy environments.
Content-aware Facial Image Compression with Deep Learning Method

Recently, online office work and education through video conferences have become a typical part of people’s daily life, increasing the need for bandwidth and storage of wireless communication networks. To avoid video stalling and blurring, this paper proposes a content-aware facial image compression method based on deep learning networks at low bit rates for higher quality of experience (QoE). In this paper, the average face calculated from the database collected by the server is incorporated into the face representation model, which is used as prior knowledge to improve the performance of the video communication system. With the face representation model, facial images are decomposed into shape components and texture components, and different compression methods are applied to these components according to different redundancy levels. Specifically, a convolutional neural network (CNN) method is proposed to process texture components. The texture components are optimized by end-to-end rate-distortion learning with mean squared error (MSE) and structural similarity (SSIM) as optimization objectives for high fidelity reconstruction. The shape components use quantizer and entropy coding to preserve the details. Compared with traditional compression methods JPEG and JPEG2000, our proposed method could achieve a better performance in terms of PSNR and SSIM values at low bit rates.
Sensor Minimization for Energy Management in Smart Buildings

Energy management is one of the critical challenges of smart buildings. This is achieved by sensing the different factors of building. The existing approaches overlook the problems associated with the deployment of a large number of sensors. However, this paper presents a novel and holistic minimum sensor deployment method - an optimization framework to sense different factors for better energy management in buildings. It describes how intelligently using the existing information can lead to a reduction in sensors. The optimization framework is compared with the baseline approach, which deploys sensors at all the locations where they should be sensed, for an existing building. The results indicate that the newly developed optimization framework requires 72.92% less sensors as compared to the baseline approach. This feature makes it an impressive proposition and results in lesser e-waste generation.
Stochastic Geometry Analysis of Three-Dimensional Aerial Ad hoc Network with Directional Antennas

Aerial Ad hoc Network (AANET) is a particular type of three dimensional (3D) wireless ad hoc network. With the help of directional antenna, AANET can not only get long transmission distance, but also strong concealment and anti-interference performance. Although many works have analyzed the capacity of 3D AANET, self-interference caused by concurrent communication links using directional transmission is still a problem cannot be ignored. In this paper, performance of 3D directional AANET is studied using stochastic geometry. At first, the characteristic function of self-interference is derived based on physical transmission model and interference model. On this basis, the closed expressions of connectivity probability and average throughput per node are derived according to cumulative distribution of self-interference. Besides, protection zone is proposed to established to reduce self-interference by forbidding interference transmitters around target receiver to transmit packets. By simulations, the connectivity probability and average throughput of AANET can be improved by establishing protection zone. And adjusting the beamwidth (or the gain) of directional antenna properly with the different distribution and traffic load of nodes in network can greatly improve the performance of AANET.
Deep-learning-based Bouncing-order Prediction for Propagation Channel Characterization Using Graph Modeling

The propagation of electromagnetic waves can be traced along multiple paths where interactions between waves and objects occur resulting in "bouncing" effect. The number of bounces, called as "bouncing-times", can significantly influence the fading characteristics, the delay and angular spreads of receiver signals. Therefore, it is meaningful and valuable to research on bouncing-times. Graph modeling is an efficient technique that can be applied to simulation of the channel characteristics. Bouncing-times is an important parameter for graph modeling of propagation characteristics with fidelity and accuracy. A proper setting of the bouncing-times can largely reduce the computational cost of graph modeling with no tradeoff of the performance. In this work, a novel method based on deep learning (DL) applied to prediction of bouncing-times accurately is proposed. We choose foliage scenario as sample environment and train a neural network that can predict bouncing-times directly from the power delay profile (PDP) of a propagation channel. Both experimental PDPs and measurement PDPs are used to validate the neural network being trained. The result shows that the neural network being trained can accurately predict the bouncing-times for foliage scenario. With appropriate modifications, the proposed DL method can be generalized to more scenarios of parametric channel characterization.
A Ramp-Up Technique for Chopper-Stabilized DC-Coupled Fully Differential PGAs suitable for Current Measurements with High Dynamic Range

This paper presents a fully differential chopper-stabilized programmable gain amplifier (PGA) for shunt-based measurement of DC and AC currents in high-power applications. A consistently high gain over a comparatively wide bandwidth (DC to 50 kHz) is achieved by using a staggered series connection of amplifier stages, each with fixed gain settings at a comparatively low level. For offsets and 1/f-noise cancellation, the chopper technique is used. Due to the optionally adjustable very high overall gain of the amplifier chain and a certain start-up time until the local chopper stabilization takes effect, a novel rampup technique is presented here. The circuit is fabricated in a 0.18 µm standard CMOS technology with a chip size of 0.16 mm2. Measurements show that the PGA has a variable gain range of 0 to 69.56 dB from DC to 50 kHz. The root-mean-square input noise at maximum gain does not exceed 810 nV at a total bandwidth of 10 kHz. The DC offset is less than 12µV for 12 Die-to-Die samples. The PGA dissipates 20mW from a 5-V supply.
Using Dual-use Electronic Lectures in E-learning: An Empirical Study of Teaching and Learning Mathematics at Vietnamese High Schools

E-learning is a form of teaching with several advantages, especially in the context of disease outbreaks such as COVID-19. In terms of teaching at High Schools, E-learning lectures are utilized both for direct classroom teaching and for online teaching via the Internet. This article presents a dual-use electronic lecture design model (DUEL) with iSpring Suite, which can be flexibly used for both classroom and online learning goals. An empirical research on DUEL is conducted in Mathematics at high school. After the experiment, a survey that evaluating the effectiveness of using DUEL has sent to learners. The feedback results showed that the first step of using DUEL was received quite positively and had developed to their full potential in fluctuations of today’s teaching context.
Trajectory of Prediction of Immediate Surroundings for Autonomous Vehicles Using Hierarchical Deep Learning Model

A predicting model based on long-short-term-memory (LSTM) and gated recurrent unit (GRU) is proposed to assist autonomous vehicles (AVs) to drive safely. To understand the behaviors of surroundings under a mixed scene of vehicles, bicycles, and pedestrians, the proposed model can predict the future trajectory of each object with models constructed by GRU. Since different objects have diverse behaviors, this paper applies different models to different categories for vehicles, pedestrians, and cyclists. For each object, the proposed model considers three observed trajectories with different time steps as the input data to predict a more accurate future trajectory. The proposed model is verified and compared with LSTM and GRU on KITTI dataset in the conducted experiments.
Distributed Community Detection on Overlapping Stochastic Block Model

Community detection, referred as the clustering procedure for similar nodes in the network, is an important research topic in the area of big data network. Distributed community detection algorithm, with its simplicity and high-efficiency, has drawn extensive attention recently. The existing distributed algorithm can only be applied on networks with non-overlapping communities, however, overlapping communities widely exist in real networks, which cannot be detected by the distributed algorithm. We carefully study the overlapping community structure and introduce the overlapping stochastic block model, in which each node may belong to multiple communities. Based on the existing distributed algorithm, we further propose an improved algorithm with an extra stage to discover nodes belonging to the overlapping community, which can be applied on the overlapping stochastic block model. Through theoretical analysis, we show that the proposed algorithm achieves effective detection results on both overlapping and non-overlapping communities. We also conduct various simulations on the overlapping stochastic block model, showing that the proposed algorithm outperforms the existing distributed algorithm and maintains effectiveness in a wide range of system parameters.
Design of a Simple Circularly Polarized Antenna for Millimeter-Wave Applications

A low-profile right-handed circularly polarized antenna for millimeter-wave applications has been proposed. The planar antenna consists of two substrates with a short-ended substrate integrated waveguide (SIW) obtained in the lower substrate. The electromagnetic wave is excited by SIW and coupled by a rectangular slot etched in the top ground. Furthermore, the circularly polarized wave is generated by printing two mirrored L-shaped dipoles which are connected to the ground plane. Compared with other circularly polarized antennas, the proposed antenna design has the merits of low-profile, broad bandwidth, good circularly polarized performance and easy fabrication. The obtained results can confirm that the proposed antenna has achieved an impedance bandwidth $(\vert \mathrm{S}_{11}\vert \leq- 10\ \mathrm{dB})$ of 23.6-26.2GHz, the 3-dB axial ratio bandwidth of 24-25.2GHz and a stable realized gain from 3.5-3.9dBi.
Point Cloud Geometry Prediction Across Spatial Scale using Deep Learning

A point cloud is a 3D data representation that is becoming increasingly popular. Due to the large size of a point cloud, the transmission of point cloud is not feasible without compression. However, the current point cloud lossy compression and processing techniques suffer from quantization loss which results in a coarser sub-sampled representation of point cloud. In this paper, we solve the problem of points lost during voxelization by performing geometry prediction across spatial scale using deep learning architecture. We perform an octree-type upsampling of point cloud geometry where each voxel point is divided into 8 sub-voxel points and their occupancy is predicted by our network. This way we obtain a denser representation of the point cloud while minimizing the losses with respect to the ground truth. We utilize sparse tensors with sparse convolutions by using Minkowski Engine with a UNet like network equipped with inception-residual network blocks. Our results show that our geometry prediction scheme can significantly improve the PSNR of a point cloud, therefore, making it an essential post-processing scheme for the compression-transmission pipeline. This solution can serve as a crucial prediction tool across scale for point cloud compression, as well as display adaptation.
A State Observer from Multilevel Quantized Outputs

In this paper, a Kalman Filter type algorithm is introduced for the recursive estimation of the states of linear systems whose outputs are measured through sensors with multilevel quantized outputs. It is also shown how the algorithm can be adapted for the parameter estimation of linear systems in linear regression form, from quantized outputs. The influence of the quantization step on the performance of the proposed algorithms is analyzed through simulation examples.
A Quantifiable Muscle Fatigue Method Based on sEMG during Dynamic Contractions for Lower Limb Exoskeleton

Advanced interactive methods between users and lower-limb exoskeletons are gradually drawing attention. Muscle fatigue is regarded as a significant contributory factor in muscle injuries and low work efficiency which is harmful to human-robot interactions. The detection and quantification of muscle fatigue during dynamic contractions remain as a challenging research. In this research, we introduce a novel method called Wavelet Packet Energy Entropy (WPEE) to quantify the muscle fatigue based on sEMG during dynamic contractions. Fatigue experiments and exoskeleton experiments are executed and three healthy subjects complete these experiments. sEMG signals from six muscles of arms are selected and analyzed. The results derived from fatigue experiments validate that the introduced method is better at quantifying the muscle fatigue compared with traditional method mean power frequency (MNF). WPEE is applied in exoskeleton experiments to investigate the arms’ muscle fatigue under three conditions. The quantitative results detailly express the user’s muscle fatigue condition and fatigue trends when the user is using an exoskeleton with crutches. Considering the muscle fatigue will contribute to ergonomic design and high-quality interactive control of exoskeleton.
Unequal Amplitudes of Harmonic Terms in Particle Dynamics inside Paul Trap

The dynamics of charged particles inside a Paul trap is a superposition of a slow secular motion and a fast micro-motion. The conventional methods, such as harmonic balance, approximate the particle trajectory with harmonic terms of the same order contributing equally to the motion. However, a careful analysis will show that in the analytically approximate solution, the harmonic terms have an unequal amplitude contribution to the particle dynamics given a set of operating conditions. In this paper we apply the modified Lindstedt Poincare method to highlight these unequal contributions. The harmonic terms surely become a significant factor in affecting the dynamics and hence in various applications inside Paul traps.
The Bases of Harmonization of Regulations on Food Production and Labeling in the Customs Union within the Eurasian Economic Community

Abstract The Eurasian Economic Community (EurAsEC) is an international economic organization created to effectively advance the process of forming a Customs Union and Common Economic Space by member states and also to implement other goals and objectives connected with the enhancement of integration in the economic and humanitarian spheres. One of the major challenges for the successful functioning of the common customs space of the members of the Customs Union, is the harmonization and unification of the legislation, including food legislation of the countries - members of the Customs Union. Unification of the legislation related to the labeling of food products will promote the trade and will ensure the awareness of the consumers.
Standard Cell based Memory Compiler for Near/Sub-threshold Operation

Voltage scaling to near/sub-threshold region is commonly used to achieve energy-efficient operation in digital circuits. However, the voltage-scaling potential of traditional 6T SRAM memories is limited by reliability problems. For small size memories, designers tend to a use latch/flip-flop based register file which brings relatively high area overhead, high delay penalty, and power overhead. In this work, a standard-cell based memory (SCM) compiler is presented to automatically generate a 12T OAI based SCM for multiple CMOS technologies operating in near/sub-threshold region. The SCM compiler utilizes Python-MyHDL for RTL and constraint generation. The timing and floor-plan constraints are generated based on user specified technology, memory size, and memory shape inputs. The power, area, energy per access, read delay, and write delays are evaluated for the generated memories of different sizes and shapes. The proposed 12T OAI based SCM achieves average speed-up of 28%/39% in write delay and average speed-up of 25%/19% in read delay when compared to D-latch/flip-flop based memories in a 28-nm FDSOI technology. Furthermore, 41% reduction in energy per access and 33% area saving are obtained, compared to a recently published SCM design in 40-nm CMOS technology [1].
Majorization-Minimization Algorithm for Discriminative Non-Negative Matrix Factorization

This paper proposes a basis training algorithm for discriminative non-negative matrix factorization (NMF) with applications to single-channel audio source separation. With an NMF-based approach to supervised audio source separation, NMF is first applied to train the basis spectra of each source using training examples and then applied to the spectrogram of a mixture signal using the pretrained basis spectra at test time. The source signals can then be separated out using a Wiener filter. Here, a typical way to train the basis spectra is to minimize the dissimilarity measure between the observed spectrogram and the NMF model. However, obtaining the basis spectra in this way does not ensure that the separated signal will be optimal at test time due to the inconsistency between the objective functions for training and separation (Wiener filtering). To address this mismatch, a framework called discriminative NMF (DNMF) has recently been proposed. While this framework is noteworthy in that it uses a common objective function for training and separation, the objective function becomes more analytically complex than that of regular NMF. In the original DNMF work, a multiplicative update algorithm was proposed for the basis training; however, the convergence of the algorithm is not guaranteed and can be very slow. To overcome this weakness, this paper proposes a convergence-guaranteed algorithm for DNMF based on a majorization-minimization principle. Experimental results show that the proposed algorithm outperform the conventional DNMF algorithm as well as the regular NMF algorithm in terms of both the signal-to-distortion and signal-to-interference ratios.