Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Classification based on t-Linear Combination of Tensor

A multidimensional linear algebra based image classification technique is proposed in this paper. The image on a particular class can be represented as a t-linear combination of images belong to same class whereas it is difficult to represent it using images in other classes. Motivated by this concept, the proposed method constructs tensors which provide approximate representation of each class during the training process. Lateral slices of these tensors are the images belongs to each class. In the next step, the strength of the representation coefficient is measured to determine the class where the image belongs. To achieve this, the proposed method computes a projection operator on to the set of subspaces throughout the learning process. Conventional method uses vectorization of images to form a data matrix. In contrast, the proposed method represents each class as a tensor for the effective utilization of structural information contained in the image samples. Feature extraction and classification is done in a single step. Moreover when compared to the predecessor, the proposed method requires a single projection operator which reduces the computational time to a large extent. The experimental results demonstrate the superiority of the proposed method over the existing method.
Parametric Study of the Accuracy of OpenFOAM Solvers for the Oblique Shock Wave Problem

The article is devoted to a parametric study of the accuracy of four solvers of the OpenFOAM package. As a test problem, we consider the classical two-dimensional problem of the fall of a supersonic inviscid compressible flow onto a flat plate at an angle of attack. As a result, an oblique shock wave is formed before the start of the plate. The problem is considered when varying the defining parameters of the problem, such as the angle of incidence of the flow on the plate and the Mach number. As a result of the calculations, a solution was obtained for the class of problems, where this class is determined by the ranges of variation of the determining parameters. For all solvers in the class under consideration, deviation surfaces from the known exact solution are constructed. The resulting estimates can be useful for users and developers of solvers.
OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation

The Sentinel-1 mission provides a freely accessible opportunity for urban image interpretation based on synthetic aperture radar (SAR) data with a specific resolution, which is of paramount importance for Earth observation. In parallel, with the rapid development of advanced technologies, especially deep learning, we urgently need a large-scale SAR dataset supporting urban image interpretation. This article presents OpenSARUrban: a Sentinel-1 dataset dedicated to the content-related interpretation of urban SAR images, including a well-defined hierarchical annotation scheme, data collection, well-established procedures for dataset compilation and organization as well as properties, visualizations, and applications of this dataset. Particularly, our OpenSARUrban collection provides 33 358 image patches of urban SAR scenes, covering 21 major cities of China, including 10 different target area categories, 4 kinds of data formats, 2 kinds of polarization modes, and owning 5 essential properties: large-scale coverage, diversity, specificity, reliability, and sustainability. These properties guarantee the achievement of several goals for OpenSARUrban. The first one is to support urban target characterization. The second one is to help develop well-applicable and advanced algorithms for Sentinel-1 urban target classification. The third one is to explore content-based image retrieval for these kinds of data. In addition, dataset visualization is implemented from the perspective of manifolds to give an intuitive understanding. Besides a detailed description and visualization of the dataset, we present results of some benchmarking algorithms, demonstrating that this dataset is practical and challenging. Notably, developing algorithms to enhance the classification performance on the whole dataset and considering the data imbalance are especially demanding.
ACO-based Distributed Energy Routing Protocol In Smart Grid

The bidirectional flow of power and communication, and the integration of distributed renewable energy sources in smart grid (SG) have lead to the emergence of the energy routing problem. A lot of interest has been given to energy routing problem in aim of minimizing the transmission cost and ensuring the reliability and efficiency of the smart grid. Historically, traditional techniques are used to solve this problem, such as graph theory, autonomous systems, game theory, … ect. In this paper, we attempt to solve this problem with different method, we formalized the problem of determining the energy efficient path as an optimization problem. As a solution for this problem we propose a Bio-Inspired Energy Routing Algorithm based on Ant Colony Optimization (ACO-ERP).
Body Part Level Attention Model For Skeleton-Based Action Recognition

Skeleton-based action recognition has catched more eyes recently. Many approaches model the human skeleton sequences spatio-temporal representation by Recurrent Neural Networks, while RNNs-based approaches don’t explicitly have the attention scheme. In this paper, a body part level attention method is proposed for determining which parts are discriminative. On the basis of human joints natural connection, human body is divided to disjoint parts which then are fed into corresponding modules to generate action scores. A body part level attention module which distributes different importance to different action scores is proposed to adaptively learn informative body parts. The final representative of the action video is produced in a weighted average way and it will be fed into DNNs for classification. Out method outperforms other attention model and we obtain the state-of-the-art performance compared to other methods on two popularly used benchmark NTU dataset and UT Kinect dataset.
Making addictions in standardised screening and diagnostic tools

ABSTRACT What counts as a drug problem? What exactly is addiction? What can be done to address it? Researchers work in a wide range of disciplines to answer these questions, often relying upon information collected via screening and diagnostic tools. Typically developed within epidemiological research, these tools establish and stabilise key criteria for identifying problematic substance use and addiction, and these criteria are then deployed to measure the extent and scale of the problems within populations. In this article, we analyse these tools to identify the logics and assumptions at work within them. Recognising that epidemiology constitutes, as much as it measures, the diseases it tracks, this article examines how addiction screening and diagnostic tools go about making the phenomena they purport to track. Our analysis suggests that through processes of reduction, expression, quantification, normalisation and populationisation, addiction screening and diagnostic tools work to establish, standardise and reify addiction as an independent, pre-existing affliction located in individuals. In so doing, they erase the complexities and subjective interpretations of individual experiences. In conclusion, we consider the material effects of defining addiction in certain ways, measuring it in certain communities and contexts, and through certain methods, and designing responses based on these strategies.
Catalytic Cerium Oxide Nanoparticles in Nanomedicine and Their Use in Liver Diseases

In this work, we aim to provide a better understanding on how to analyze the results obtained using Cerium Oxide (CeO2) nanoparticles for medical applications while showing some results for the case of liver disease. Thus, the manuscript is divided in two main sections. First, we discuss the bibliography of the safety aspects of the use of Cerium Oxide for medicine, since we have seen discrepancies between reports on its proven anti-inflammatory activity together with other reporting toxicity. The evolution of the nanoparticles inside the body make several parameters, such as aggregation state or surface evolutions, as critical determinants that need to be carefully addressed to better understand their potential clinical benefits. Second, we show the usefulness of enhancing the stability of CeO2 nanoparticles in the physiological media for the treatment of liver diseases. Liver is the organ where nanoparticles passively accumulate and thus, a logical place to start the studies of the in vivo evolution and activity of this material.
MPPT under partial shading conditions based on Perturb & Observe and Incremental Conductance

Maximum power point tracking (MPPT) under uniform and different weather conditions is the main challenge in photovoltaic (PV) system. Partial shaded conditions (PSC) causes several power peaks on a P-V curve. This paper compares two powerful MPPT algorithm of Perturb & Observe (P&O) and Incremental Conductance (INC) under PSC. The PV system is developed using MATLAB simulation software with the Boost DC/DC converter interconnected. The proposed algorithms are tested under standard test and different weather conditions to prove their performance looking at their tracking speed, convergence and the settling time around the MPP.
A Review of Energy Efficient Optimization Techniques for VM Placement in Cloud Data Centers

Cloud computing is the evolving field with new generation technologies to provide state-of-the-art services to customers and organizations situated worldwide. The data centers in the network of servers are the backbone of the Cloud computing environment with a huge capacity of processing and data storage. Nowadays, data centers are in the limelight for consuming the large amount of energy to support cloud computing services throughout the world. Although several techniques are implemented tm now to minimize the energy wastage in data centers, still optimization of designed policies is required. Virtualization approach is used to consolidate the server workload for utilization of computing resources. During the consolidation process, different Virtual Machine placement techniques can be applied with evolutionary algorithms to reduce the energy consumption of servers. In this paper, the critical review of evolutionary algorithms (Genetic Algorithm, Ant Colony optimization, and Particle Swarm optimization) used with placement approaches for energy optimization, is conducted to identify the improvement scope and future research directions in this area.
Resistance Drift and Crystallization in Suspended and On-oxide Phase Change Memory Line Cells

Resistance drift and crystallization are critical concerns for accurate distinction between different states and for data retention in phase change memory (PCM), but their underlying physical mechanisms are still not fully understood. In this work, we compared the resistance drift and crystallization of suspended and on-oxide amorphous Ge2Sb2Te5 PCM line cells. We programmed 15 cells of each type at room temperature to a resistance level of ~10 MΩ and monitored their resistance over ~3 months. The initial upward resistance drift trends were very similar for both cell types, but the later behaviour was noticeably different. The suspended cells exhibited increased variability in the upward drift after ~103 s, and at ~35-80 days some cells experienced an abrupt crystallization, some showed a saturation in the upward resistance drift, and others continued the upward resistance drift with fluctuations, possibly due to the imminent saturation. The on-oxide cells, on the other hand, demonstrated very consistent upward resistance drift during the entire measurement period. Temperature-accelerated measurements at 400 K on both type of cells also showed the early and abrupt data loss in suspended cells. The abrupt crystallization in suspended PCM cells can potentially be useful for self-destructive hardware security primitives based on the loss of the stored data after a certain time.
Event prediction via spatio-temporal sequence analysis

Event prediction often refers to the process of inferring dynamic information such as human pose change, object motion trajectory and event development direction from static images or partial video, which has broad application prospects in security monitoring, automatic driving, human-computer interaction and other fields. This article proposes the means of deep learning to conduct research on sequence images’ event prediction in time and space dimensions. To solve the problem of missing semantic information and lack of external information in the current event prediction research, we propose to adopt human skeleton constraint as guiding information, then complete the task from two aspects: skeleton detection and prediction in two-dimensional sequence images, image generation under skeleton guidance. The sequence images event prediction and generation model can generate realistic images with good continuity and perfect details on the basis of reasonable prediction in a short time. To verify the feasibility of our algorithm, we carried out a series of experiments on multiple deep learning network structures. The experimental results of the traditional motion detection dataset and the automatic driving dataset show the theoretical and practical value of the proposed algorithm in the field of sequence image event prediction, which has great potential for development.
Three-Dimensional Absolute Attitude Reconstruction of a Rigid Body Based on Multi-Station HRRP Sequences

This study proposes a three-dimensional (3-D) absolute attitude reconstruction algorithm of a rigid body target based on multi-station high-resolution range profile (HRRP) sequences. The existing 3-D reconstruction algorithms rely on the target motion form and the reconstructed attitude is inaccurate due to the mismatch of the micro-motion model. In this study, the coordinates of the scattering centers are first calculated from the HRRP sequences of each radar using the factorization method. Then, the multi-station data are associated with the reconstructed scattering center coordinates, and the absolute coordinates is estimated by the relationship between the associated radar line-of-sights (LOSs) and the absolute LOSs. The 3-D reconstruction algorithms for the stable scattering centers and the sliding scattering centers are studied. Numerical simulations verify that the proposed algorithm can reconstruct the 3-D absolute attitude of a rigid body target and is not affected by the mismatch of the target micro-motion model.
Identification of Solar PV Array Partial Shading Patterns using Machine Learning

Mismatch losses due to partial shading can limit the energy generation of solar photovoltaic (PV) systems. Isolating the shaded PV modules through electrical reconfiguration can potentially improve the power output of the PV array. To do this, the shaded modules need to be identified before the PV array can be reconfigured to produce the optimum output power. In this study, an algorithm was developed to identify the partial shading pattern of a PV array using machine learning. Measurements from the current sensor integrated into the switching circuit of each module and the solar irradiance from a pyranometer were utilized as input to the machine learning algorithm.The algorithm was trained using the voltage and current readings of an off grid PV system composed of nine 10-W PV modules arranged in a 3 $\times$ 3 array in series-parallel configuration. Three machine learning techniques were used, namely SVC, Random Forest, and K-Nearest Neighbors, resulting in 80 %, 86 %, and 66 %, respectively, in terms of accuracy, precision, recall, and f-1 score. Thus, the Random Forest algorithm was found suitable for this type of problem as it can reliably distinguish the shading patterns on the array.
MaligNet: Semisupervised Learning for Bone Lesion Instance Segmentation Using Bone Scintigraphy

One challenge in applying deep learning to medical imaging is the lack of labeled data. Although large amounts of clinical data are available, acquiring labeled image data is difficult, especially for bone scintigraphy (i.e., 2D bone imaging) images. Bone scintigraphy images are generally noisy, and ground-truth or gold standard information from surgical or pathological reports may not be available. We propose a novel neural network model that can segment abnormal hotspots and classify bone cancer metastases in the chest area in a semisupervised manner. Our proposed model, called MaligNet, is an instance segmentation model that incorporates ladder networks to harness both labeled and unlabeled data. Unlike deep learning segmentation models that classify each instance independently, MaligNet utilizes global information via an additional connection from the core network. To evaluate the performance of our model, we created a dataset for bone lesion instance segmentation using labeled and unlabeled example data from 544 and 9,280 patients, respectively. Our proposed model achieved mean precision, mean sensitivity, and mean F1-score of 0.852, 0.856, and 0.848, respectively, and outperformed the baseline mask region-based convolutional neural network (Mask R-CNN) by 3.92%. Further analysis showed that incorporating global information also helps the model classify specific instances that require information from other regions. On the metastasis classification task, our model achieves a sensitivity of 0.657 and a specificity of 0.857, demonstrating its great potential for automated diagnosis using bone scintigraphy in clinical practice.
An Advanced Harmony Search Algorithm Based on Harmony Anchoring and Reverse Learning

In this paper, we propose a new and effective multi-objective optimization algorithm based on a modified harmony search. The proposed method employs reverse learning in the harmony vector updating equation in order to enhance the global searching ability. Moreover, it adopts a harmony anchoring scheme so that unnecessary exploration is avoided. Experimental studies carried on eight benchmark problems show quite satisfactory results and indicate the higher performance of the proposed algorithm in comparison with traditional multi-objective optimization algorithms. Finally, it has been applied to solve the image segmentation problem.
An Electric Drive System Modelling Method Based on Module Behavior

Electric drive system is the key that divides electric vehicles from traditional fuel vehicles. It introduces new components such as non-linear inverter circuits, electric motors, power batteries and control circuits. Compared with traditional mechanical transmission system, the introduction of these power electronic devices greatly increases the magnitude of the electromagnetic interference, which influence the reliability of the power and communication system. Therefore, it is necessary to establish a system-level model to help engineer predict and optimize the system EMI. This paper proposes a system-level modelling method based on the behavior of the module, and validates it by comparing simulation results with test results. The model constructed by this method covers a wide frequency range and achieves high accuracy. It can help EMC engineers accurately predict EMI and perform further EMC optimization.
Li-Ion energy storage capacity estimation in residential applications with EV

Modelling the capacity estimation of batteries is essential for optimizing energy management, extending the battery cycles, reducing cost for renewable energy sources (RES) residential applications with electrical vehicle (EV). The analyzed microgrid (MG) consists in an 8 kW photovoltaic (PV) system, a grid connected PV inverter and a 48V Li-ion stationary battery interfaced with the MG by an inverter which provides 20 kWh storage capacity. The system is implemented in the Advanced Electrical Systems Research Centre of Transilvania University of Brasov R&D Institute. The main goal of this paper is to find a method for estimating the storage capacity for the analyzed Li-Ion battery, considering a specific daily load profile (DLP) for a household placed in the rural area and an average DLP for EV. Simulations were accomplished in Homer Pro software and the obtained results shows that the MG is effectively working to manage optimally the power flow from PV and battery to residential loads and EV.
Particle swarm optimization-based extreme gradient boosting for concrete strength prediction

Although concrete is one of the most widely used construction materials in civil engineering, accurate prediction of concrete strength is very difficult for its highly non-linearity of age, concrete components, and physical and chemical reaction process. To address this problem, we proposed an improved extreme gradient boosting (XGBoost) for concrete strength prediction based on parameter optimization. By aggregating multiple ‘weak’ prediction models (e.g., decision trees), XGBoost is able to build a prediction model with high execution speed and model performance. To further improve the performance of XGBoost, comprehensive learning particle swarm optimizer (CLPSO) is employed for parameter selection. Five-fold cross validation is introduced to better evaluate the prediction performance. Experimental results from concrete strength prediction have shown our proposed CLPSO-XGBoost is able to outperform other well-known machine learning models in comparison in terms of robustness and accuracy, which confirms that CLPSO-XGBoost can be a useful tool to predict the compressive strength in practical civil engineering applications.
Visual SLAM Algorithm Based on ORB Features and Line Features

The traditional point-features-based visual SLAM is difficult to track when scene light changes and the camera’s perspective is strenuous. However, in many environments, despite low textures, line-based geometric primitives can still be reliably estimated, such as in urban and indoor scenes dominated by structured edges. And sparse feature point map is also not conducive to human-computer interaction. In this work, we propose a solution to these situations, and we build a visual SLAM system that combines both points features and line features to work robustly in a wider variety of circumstances, specifically in those where point features are exiguous or not well distributed. It uses LSD algorithm to extract and match line features, reconstructs the re-projection error model based on line features, and construct a new error model combines point and line features. The experimental results show that the proposed method can improve the accuracy of the estimated trajectory by 36% compared with the original ORB-SLAM2 algorithm. It can also systematically improve its combination of points and lines in key frames without compromising the efficiency. At the same time, this method can construct a three-dimensional dense map, providing a technical basis for complex application scenarios such as dynamic obstacle avoidance and semantic map.
A 0.3-V 98-dB Rail-to-Rail OTA in $0.18~\mu$ m CMOS

A new solution for an ultra-low-voltage, ultra-low-power operational transconductance amplifier (OTA) is presented in the paper. The design exploits a three-stage structure with a Reversed Miller Compensation Scheme, where the input stage is based on a non-tailed bulk-driven differential pair. Optimization of the structure for very low supply voltage is discussed. The resulting amplifier outperforms other ultra-low-voltage OTAs in terms of a DC voltage gain and power efficiency, expressed by standard figures of merit. Experimental verification using a $0.18~\mu \text{m}$ CMOS technology, with supply voltage of 0.3-V, showed a dissipation power of 13 nW, a DC voltage gain of 98 dB, a gain-bandwidth product of 3.1 kHz and an average slew-rate of 9.1 V/ms at 30 pF load capacitance. The experimental results agree well with simulations.