Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Source Separation with Differential Microphone Arrays and Independent Low-Rank Matrix Analysis

Acoustic source separation has been an active and important area of research in the field of acoustic signal processing. This paper deals with this problem using small and compact differential microphone arrays (DMAs) so that the resulting technology can be used in a broad range of small devices in voice communication and human-machine interfaces. A straightforward way to achieve source separation with DMAs is through differential beamforming. Although it has frequency-invariant beampatterns and high directivity in comparison with other existing beamforming methods with the same number of sensors, differential beamforming with small DMAs has limited spatial gain, generally leading to insufficient separation performance. To circumvent this limitation, we propose in this work a method to combine differential beamforming with an independent vector analysis (IVA) based algorithm. Specifically, differential beamformers are designed and applied to separate sound sources from different directions. Then, differential beamformers’ outputs are used as inputs for the independent low-rank matrix analysis (ILRMA) algorithm, a widely used IVA method for blind source separation. The advantage of this proposed method consists of at least three aspects: 1) improving the source separation performance, 2) helping deal with the permutation problem, and 3) helping improve the convergence of ILRMA.
Adaptation of cluster analysis methods to optimize a biomechanical motion model of humans in a nursing bed

The paper considers the optimization of a Hidden-Markov Model (HMM) based method for the generation of averaged motion sequences. To create averaged motion sequences, motion sequences of different test persons were originally recorded with a motion capture system (MoCap system) and then averaged using an HMM approach. The resulting averaged data sets, however, partly showed serious motion artifacts and uncoordinated intermediate movements, especially in the extremities. The aim of this work was to combine only movements with similar courses in the extremities by a suitable cluster analysis. For each test person, model body descriptions of 21 body elements are available, each of which is represented in three-dimensional time series. For optimization, the MoCap data are first compared using time warp edit distance (TWED) and clustered using an agglomerative hierarchical procedure. Finally, the data of the resulting clusters are used to generate new averaged motion sequences using the HMM approach. The resulting averaged data can be used, for example, in a simulation in a multilevel biomechanical model.
Recent Developments in High Power EM (HPEM) Standards With Emphasis on High Altitude Electromagnetic Pulse (HEMP) and Intentional Electromagnetic Interference (IEMI)

Standardization of High Power Electromagnetic (HPEM), environments, protection design and test methods is becoming increasingly important particularly as the perceived risk to society from HPEM is growing. Many generic standards and some application or product specific standards are now available. This letter will provide an overview of some recent developments in HPEM standards, in particular High Altitude Electromagnetic Pulse (HEMP) and Intentional Electromagnetic Interference (IEMI) phenomena.
Detecting Pork Adulteration in Beef for Halal Authentication Using an Optimized Electronic Nose System

Recently, the issue of food authentication has gained attention, especially halal authentication, because of cases of pork adulteration in beef. Many studies have developed rapid detection for adulterated meat. However, these studies are not yet practical and economical methods and instruments and a faster analysis process. In this context, this paper proposes the Optimized Electronic Nose System (OENS) for more accurately detecting pork adulteration in beef. OENS has advantages such as proper noise filtering, an optimized sensor array, and optimized support vector machine (SVM) parameters. Noise filtering is carried out by cross-validation with different mother wavelets, i.e., Haar, dmey, coiflet, symlet, and Daubechies. The sensor array was optimized by dimension reduction using principal component analysis (PCA). An algorithm is proposed for the optimization of the SVM parameters. An experiment was conducted by analyzing seven classes of meat, comprising seven different mixtures of beef and pork. The first and seventh classes were 100% beef and 100% pork, respectively, while the second, third, fourth, fifth, and sixth classes contained 10%, 25%, 50%, 75%, and 90% of beef in a sample of 100 grams, respectively. Sample testing was carried out for 15 minutes for each sample. The classification test results to detect beef and pork had an accuracy of 98.10% using the optimized support vector machine. Thus, OENS has a favorable performance to detect pork adulteration in beef for halal authentication.
Experimental Analysis of EM and MU Algorithms for Optimizing Full-rank Spatial Covariance Model

Full-rank spatial covariance analysis (FCA) is based on a flexible source model, and achieves high-quality results for blind source separation. An expectation-maximization (EM) algorithm as well as a multiplicative update (MU) algorithm are known to optimize the FCA model parameters. In this paper, we first investigate the behaviors of both algorithms. We observed that the MU algorithm minimizes the FCA objective function faster than the EM algorithm, but the separation performance at the converged point is better by the EM algorithm than the MU algorithm. We found that the MU algorithm tends to push the covariance matrices towards rank deficient. To mitigate this tendency, we propose a modified FCA model where the tempo-ral parameters are shared within a time block. Experimental results show that the modified model provides better separation performance not only by the MU algorithm but also by the EM algorithm.
A Graph Signal Processing Framework for the Classification of Temporal Brain Data

Graph Signal Processing (GSP) addresses the analysis of data living on an irregular domain which can be modeled with a graph. This capability is of great interest for the study of brain connectomes. In this case, data lying on the nodes of the graph are considered as signals (e.g., fMRI time-series) that have a strong dependency on the graph topology (e.g., brain structural connectivity). In this paper, we adopt GSP tools to build features related to the frequency content of the signals. To make these features highly discriminative, we apply an extension of the Fukunaga-Koontz transform. We then use these new features to train a decision tree for the prediction of autism spectrum disorder. Interestingly, our framework outperforms state-of-the-art methods on the publicly available ABIDE dataset.
Theoretical Tuning of the Autoencoder Bottleneck Layer Dimension: A Mutual Information-based Algorithm

Under the transportation field, the literature states that forecasting with excessive number of features can be computational inefficient and undertakes the risk of over-fitting. Because of that, several authors proposed the use of autoencoders (AE) as a way of learning fewer but useful features to enhance the road traffic forecast. Notably, the adequacy of the bottleneck layer dimension of the AE has not been addressed, thus there is no standard way for automatic selection of the dimensionality. We address the problem from an information theory perspective as the reconstruction error is not a reliable indicator of the performance of the subsequent supervised learning algorithm. Hence, we propose an algorithm based on how mutual information and entropy of data evolve during training of the AE. We validate it against two real-world traffic datasets and provide discussion why the entropy of codes is a reliable performance indicator. Compared to the tendency found in the literature, based on trial-and-error methods, the advantage of our proposal is that a practitioner can efficiently find said dimension guaranteeing maximal data compression and reliable traffic forecast.
Affine Intra-prediction for Versatile Video Coding

New algorithms are being investigated in the context of next generation video coding to achieve higher compression efficiency. In particular, intra block copy is well known to accurately predict screen content and artificially generated content where patterns and edges may repeat within the frame. On the other hand, such type of video content is often subject to geometrical transformations such as transitions, rotations, zooms etc, which may not be accurately captured by simply copying neighbouring pixels. A new intra-prediction scheme is presented in this paper whereby blocks of samples from already reconstructed areas are processed by means of an affine-type transformation. This allows more accurate prediction of blocks which improve compression efficiency in a variety of conditions. Experiments in the context of Versatile Video Coding standard show the proposed method can improve intra-coding compression efficiency by 2.01% BD-rates on average, and up to 4.81%, with negligible impact on the decoder complexity.
Online Switch-Based Hybrid Beamforming for Massive MIMO Systems

Switch-based hybrid beamforming is a low-cost solution for implementing the analog segment of a hybrid beam-forming network. Although an analog beamformer comprising a network of switches allows low hardware complexity, designing such a network is computationally expensive. In this paper, we consider a single user massive multiple-input multiple-output (MIMO) system and propose a low computational complexity method for designing a switch-based hybrid precoder that maximizes the mutual information. We propose a method wherein the analog beamformer is approximated after solving a convex (concave) problem and employing low-rank matrix decomposition. Then, considering a sequence of channel realizations we frame the intermediate convex problem as an online convex optimization (OCO) and give the conditions under which the online version approaches the solution of the primary convex problem after some iterations by learning from previous steps. We finally study the performance through numerical results and demonstrate that the proposed online method offers a low complexity solution that tracks the spectral efficiency delivered by fully digital beamformer, and converges to the solution provided by direct maximization of the intermediate problem.
PIV Invastigation of UAV Rotor in Hover

This paper presents the results of an experimental study of the aerodynamic performance of a helicopter rotor for UAV applications in steady hover at Reynolds numbers below 50 000 by means of particles image velocimetry (PIV). Results are shown for a two-bladed rotor configuration for various pitch angles and angular velocities. A brief description of the designed test stand and PIV system is provided. The experimental setup allows for the measurement of thrust, torque and velocity field around the helicopter rotor with diameters of up to 1 meter, for pitch angles varying from −12° to +24° and for angular velocities of up to 3 000 RPM. The internal friction losses of the test stand are estimated and taken into account in measurements. Conclusions are drawn about the aerodynamic performance of the studied rotor configuration.
Improving k Nearest Neighbors and Naïve Bayes Classifiers Through Space Transformations and Model Selection

Improving classifiers’ performance is the goal of techniques like prototype selection, normalization, and feature mapping; these techniques aim to reduce the complexity and improve the accuracy of models. In this manuscript, we present a boosting artifact for the well-known single-label <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-Nearest Neighbors (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-NN) classifier and the Naïve Bayes (NB) classifier. The improvement comes as a pipeline that includes several data transformations orchestrated by a model selection scheme. The construction of these classifiers relies on the composition of simpler parts found in several open-source libraries and can be effortlessly put together to replicate our proposal. We also explore ensembling and the effect of preprocessing and normalizing the data. We compare our approach experimentally with 17 popular classifiers using raw and rank-based scores on 34 different benchmarks; statistical tests support our results. For instance, our results regarding average performance ranks under balanced error rate show that the models created with our proposal achieve first, third, and fourth-best ranks, compared with 10th position of raw <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-NN and 14th of raw NB.
Low-Complexity HEVC Transrating Based on Prediction Unit Mode Inheritance

Video transcoding for bit rate adaptation has become mandatory for over-the-top applications that deliver multimedia content in heterogeneous environments under different network conditions and user capabilities. As transcoding requires sequentially decoding and re-encoding the video bitstream, the computational cost involved in the process is too high, especially when considering current state-of-the-art codecs, such as the High Efficiency Video Coding (HEVC). This work presents a fast HEVC transcoder for bit rate adaptation based on Prediction Unit (PU) mode inheritance, which uses information gathered from the HEVC decoding process to accelerate PU mode decision in the re-encoding process. Experimental results show that the proposed method achieves an average transrating time reduction of 42% at the cost of a bitrate increase of 0.54%.
New Dictionary Learning Methods for Two-Dimensional Signals

By growing the size of signals in one-dimensional dictionary learning for sparse representation, memory consumption and complex computations restrict the learning procedure. In applications of sparse representation and dictionary learning in two-dimensional signals (e.g. in image processing), if one opts to convert two-dimensional signals to one-dimensional ones, and use the existing one-dimensional dictionary learning and sparse representation techniques, too huge signals and dictionaries will be encountered. Two-dimensional dictionary learning has been proposed to avoid this problem. In this paper, we propose two algorithms for two-dimensional dictionary learning. According to our simulations, the proposed algorithms have noticeable improvement in both convergence rate and computational load in comparison to one-dimensional methods.
Localized Interpolation for Graph Signals

Graph structure is a powerful framework for the characterization of interaction between entities. In the context of graph signal processing, interpolation methods are based on the smoothness assumption that assumes that neighboring nodes present similar values. However, in several contexts, the strong relationship between two connected nodes may express different behaviour. In this paper, we propose a graph signal interpolation algorithm that uses a graph localization penalization on the reconstruction weights. These weights are learned from the data, thus allowing the use of signal anti-correlation on connected nodes in order to perform a more robust interpolation. The results displayed in the paper show that our approach is relevant when dealing with real data, for both smooth and non-smooth signals.
Application of A Taylor Expansion Criterion-based Pruning Convolutional Network for Bearing Intelligent Diagnosis

Deep learning methods provide an effective tool for processing massive data obtained in data-driven machine health monitoring because of their powerful feature self-learning ability from raw data. As one of popular deep networks, the convolutional neural networks (CNNs) show successful applications in various fields. However, a full CNN with a large amount of parameters suffers from the problem of long latency and high memory cost and a much smaller network can achieve similar generalization ability. In this paper, the network pruning based on the Taylor expansion criterion is introduced to process bearing vibration data and then conduct intelligent fault diagnosis. Experimental data collected from different bearings are used to access the efficiency and accuracies of the original and pruned networks. The results demonstrate that the network pruning on large pretrained CNN can keep a balance between the classification accuracy and compact structure and can be further extended to applications of online condition monitoring and intelligent fault diagnosis of bearings.
Classification of IoT Malware based on Convolutional Neural Network

In this paper, we propose an algorithm for the malware classification problem in the IoT domain. Application executions are represented by sequences of consecutive API calls. The time series of data are analyzed and filtered based on information gains, which reduce sequence lengths while keeping important information. We use convolutional neural networks to classify various types of malwares. The experimental results on real world IoT malware samples show that this approach has a faster and more accurate classification rate than the recurrent neural networks and some other machine learning classification algorithms.
Exploiting Long-Term Dependency for Topic Sentiment Analysis

Most existing unsupervised approaches to detect topic sentiment in social texts consider only the text sequences in corpus and put aside social dynamics, as leads to algorithm’s disability to discover true sentiment of social users. To address the issue, a probabilistic graphical model LDTSM (Long-term Dependence Topic-Sentiment Mixture) is proposed, which introduces dependency distance and uses the dynamics of social media to achieve the perfect combination of inheriting historical topic sentiment and fitting topic sentiment distribution underlying in current social texts. Extensive experiments on real-world SinaWeibo datasets show that LDTSM significantly outperforms JST, TUS-LDA and dNJST in terms of sentiment classification accuracy, with better inference convergence, and topic and sentiment evolution analysis results demonstrate that our approach is promising.
Gaussian process with physical laws for 3D cardiac modeling

This paper introduces some physical laws into a Gaussian process for a statistical three-dimensional (3D) cardiac computational model. The 3D cardiac shape modeling is still challenging, since it involves personality and diversity. However, in spite of such variety, the heart shape must be ruled by some physical laws, which should be an important clue for the statistical shape estimation. Specifically, we introduce the Frank-Starling laws into the Gaussian process as a linear constraint, whose resulting process also follows a Gaussian process. For demonstration, we apply our model into the pipeline that estimates the heart shape from cardiovascular magnetic resonance (CMR) imaging, by combining it with the deep neural networks-based anatomical segmentation of CMR imaging.
Determination of Core Losses and Optimal Values of Rotation Rate of an Induction Motor Driving a Compact Electric Vehicle under Different Parameters and Conditions

Environment and transport are currently among the top priorities at local, national and global levels. Related to this is vehicle electrification as one of the major targets. In this regard, over the last few decades, the energy efficiency of electric motors has been important focus for both manufacturers and end users. A part of the efforts is to develop new ways for increasing the energy efficiency of three-phase induction motors. The useful power of the shaft characterizes the efficient operation of the electric drive and the electric power - the consumption of electricity per unit time. Efficiency is one of the main parameters of an induction motor, which determines its energy properties - economy in the process of operation. Efficiency can be implicitly obtained by the motor losses determining, Moreover, the efficiency of the motor, and in particular the magnitude of its losses, regulates the heating temperature of its main parts. For its part, as a specific toolkit, mathematical modeling of induction motors has a very long history and has so far been an exciting topic for research. Paper deals with determination of core losses when operating an induction type electric motor applicable to the driving of an electric vehicle. Optimal values of motor speed under different parameters and conditions are also determined. A mathematical model of an electromechanical system has been used. Considering the results obtained, some of which are presented in tables and graphs, relevant conclusions are drawn.
Revisiting SincNet: An Evaluation of Feature and Network Hyperparameters for Speaker Recognition

The SincNet architecture [1] was recently introduced as an approach to the speaker recognition task. Its main innovation was the sinc layer—an elegant and lightweight way of extracting features from speech. Despite good performance on multiple datasets, little information was provided on the architectural choices. In this work, we aim to shed some light on the importance of the network topology and various hyperparameters. We replace the original network trunk with a lightweight trunk inspired from residual networks (ResNets) and optimize its hyperparameters. Furthermore, we carry an extensive study on the sinc layer’s hyperparameters. Our main finding is that the stride and window size of the feature extractor plays a crucial role in obtaining good performance. Further experiments on conventional features, such as MFCCs and FBANKs, yield similar conclusions; in fact, by using optimal values for these two hyperparameters, traditional features are able to match the performance of sinc features. Surprisingly, the best results obtained go against conventional wisdom: an analysis window of only a couple of milliseconds and a stride of only a couple of samples are found to give the best results. Our code is available at https://bitbucket.org/doneata/sincnet.
Distributed Trace Ratio Optimization in Fully-Connected Sensor Networks

The trace ratio optimization problem consists of maximizing a ratio between two trace operators and often appears in dimensionality reduction problems for denoising or discriminant analysis. In this paper, we propose a distributed and adaptive algorithm to solve the trace ratio optimization problem over network-wide covariance matrices, which capture the spatial correlation across sensors in a wireless sensor network. We focus on fully-connected network topologies, in which case the distributed algorithm reduces the communication bottleneck by only sharing a compressed version of the observed signals at each given node. Despite this compression, the algorithm can be shown to converge to the maximal trace ratio as if all nodes would have access to all signals in the network. We provide simulation results to demonstrate the convergence and optimality properties of the proposed algorithm.