Miracle-dz/UniFAR-FTUs huggingface dataset:
Poster: Radiometric Signatures for Wireless Device Identification over Dynamic Channels

Radiometric signatures have been shown effective in identifying wireless devices based on imperfections in their electronics, also known as fingerprinting. Previous work mainly considered static channel conditions. In this work, we experimentally study the impact of movement and dynamic channel conditions on the radiometric signatures. We demonstrate the feasibility of fingerprinting when channels are dynamic.
Risk Stratification of Neonates using Machine Learning Techniques

The process of classifying newly born babies into high-risk and low-risk is called risk stratification. Having a platform to stratify neonates according to severity of risk is the key to the success of any Neonatal Intensive Care Unit (NICU). The premature neonates are at a higher risk of developing the disabilities which could affect their future growth. However, the extent at which this can affect their entire life, strongly depends on how early they were born, the quality of care they received during and around birth and the days they follow in NICU. Establishing a decision support tool using machine learning algorithms will be useful for identifying neonates who are at high risk for proper diagnosis and efficient monitoring of neonates at NICU. The paper is focused on risk stratification of neonates using machine leaning algorithms such as Artificial Neural Network (ANN), K Nearest Neighbors (KNN) and Support Vector Machine (SVM). Furthermore, various evaluation parameters were used for comparing the results of the algorithms on the 66 cases of neonates admitted at Kasturba Medical College, Manipal. Based on Area Under Curve (AUC), ANN (0.91) performed better than KNN (0.83) and SVM (0.84). The result indicates the significant contribution of ANN with improved performance in identifying neonates who are at high risk better than other algorithms
Investigating the SoC of gel Battery using Mathematical Model

In this paper, a novel mathematical model based on RC circuit for investigating the gel battery is presented. The proposed mathematical model consists of a polynomial term and an exponential term which can present the discharging behavior of a battery. The polynomial term represents the stable discharging behavior and the exponential term represents the unstable discharging behavior. The proposed model proved that the aging of a battery increases with cycle numbers and reflects the variation in the parameters of the model in state of charge (SoC) of a battery. In addition, a simpler algorithm that estimates part of the discharging characteristics to predict SoC of a battery also presented in this work. The algorithm has shorter computing time, higher efficiency and greater convenience. Through the experiments that practical operations, maximum absolute percentage error of SoC for the relationship between measurement and fitting data is 1.83%. The results mean that the proposed model has been verified, demonstrating its effectiveness and accuracy.
Implementation of Time-Optimal Motion Planning for SCARA Robots

The SCARA robot is a robotic arm with a simple structure, high speed, and high precision. It is widely used in the industry. This paper aims to study the optimal motion planning of a SCARA robot. The Euler-Lagrange method is used to derive the dynamic model of the system. The chosen paths are based on the straight line and the cubic spline interpolation method. The minimum-time path is planned using the phase-plane method, which can meet the torque constraints, and the phase-plane method is realized in MATLAB to obtain path commands. In a Linux-based environment, Gazebo is used to construct and simulate the dynamic model of the SCARA robot. The angular commands of every joint of a planned path are given to the plugin of Gazebo to obtain the dynamic responses. Finally, this motion planning method is tested on an EPSON’s SCARA robot for the performance validation.
Transmission of radiology images over an Unsecure Network Using Hybrid Encryption Schemes

In this paper. , we propose a hybrid encryption scheme to transmit the medical image dataset securely in radiology networks. The proposed methodology uses the RSA (Rivest-Shamir-Adleman) encryption technique. , XOR technique, and the digitally reconstructed Radiograph (DRR) image from the 3D volume of MRI scan images. As a first step, the volume of interest (VOI) was segmented and then computed the DRR image on the segmented volume in the sagittal direction. The pixels of the DRR image was XORed with all the image slices. All the images and the DRR image were encrypted separately using the RSA technique and transmitted. At the receiver, the XOR was applied to all the received images, the original slices were retained, VOI was segmented again, and the DRR was recomputed. Now, the received DRR and the recomputed DRR were compared for the changes in the image content through histogram comparison, MSE, and Mean absolute deviation. The data integrity violation was tested by adding an image, deleting an image, and modifying the pixels of the image before sending it. The method was applied to fifty (n=50) samples. In all the above test cases performed, the method identified the data integrity violation correctly.
3D Feature Detector-Descriptor Pair Evaluation on Point Clouds

In recent years, computer vision research has focused on extracting features from 3D data. In this work, we reviewed methods of extracting local features from objects represented in the form of point clouds. The goal of the work was to make theoretical overview and evaluation of selected point cloud detectors and descriptors. We performed an experimental assessment of the repeatability and computational efficiency of individual methods using the well known Stanford 3D Scanning Repository database with the aim of identifying a method which is computationally-efficient in finding good corresponding points between two point clouds. We also compared the efficiency of detector-descriptor pairing showing that the choice of a descriptor affects the performance of the object recognition based on the descriptor matching. We summarized the results into graphs and described them with respect to the individual tested properties of the methods.
Priority Based Localization for Anisotropic Wireless Sensor Networks

Wireless Sensor Networks (WSN) are used in a number of applications like volcanic monitoring, forest fire control systems for monitoring and control of the events. Networks are formed by the random deployment of nodes in the required fields, which are usually anisotropic. Locations of the deployed nodes are essential to act on the collected data. To estimate the locations of nodes in the network, a number of localization algorithms are developed which make use of techniques such as hop distances and centroid to estimate the positions of nodes. These algorithms showed sub-optimal results when tested in anisotropic fields with obstructions. In this work, a priority based localization algorithm is reported, which gives priority to a few reference nodes based on their Average Hop Distances (AHD). Nodes are then localized with weighted centroid method using high priority reference nodes. The simulation results show that the localization results of the reported algorithm is better than the existing weighted centroid methods in anisotropic fields.
An Attention Guided Semi-Supervised Learning Mechanism to Detect Electricity Frauds in the Distribution Systems

Electricity theft is one of the main causes of non-technical losses and its detection is important for power distribution companies to avoid revenue loss. The advancement of traditional grids to smart grids allows a two-way flow of information and energy that enables real-time energy management, billing and load surveillance. This infrastructure enables power distribution companies to automate electricity theft detection (ETD) by constructing new innovative data-driven solutions. Whereas, the traditional ETD approaches do not provide acceptable theft detection performance due to high-dimensional imbalanced data, loss of data relationships during feature extraction and the requirement of experts’ involvement. Hence, this paper presents a new semi-supervised solution for ETD, which consists of relational denoising autoencoder (RDAE) and attention guided (AG) TripleGAN, named as RDAE-AG-TripleGAN. In this system, RDAE is implemented to derive features and their associations while AG performs feature weighting and dynamically supervises the AG-TripleGAN. As a result, this procedure significantly boosts the ETD. Furthermore, to demonstrate the acceptability of the proposed methodology over conventional approaches, we conducted extensive simulations using the real power consumption data of smart meters. The proposed solution is validated over the most useful and suitable performance indicators: area under the curve, precision, recall, Matthews correlation coefficient, F1-score and precision-recall area under the curve. The simulation results prove that the proposed method efficiently improves the detection of electricity frauds against conventional ETD schemes such as extreme gradient boosting machine and transductive support vector machine. The proposed solution achieves the detection rate of 0.956, which makes it more acceptable for electric utilities than the existing approaches.
Faster independent low-rank matrix analysis with pairwise updates of demixing vectors

In this paper, we present an algorithm for independent low-rank matrix analysis (ILRMA) of three or more sources that is faster than that for conventional ILRMA. In conventional ILRMA, demixing vectors are updated one by one by the iterative projection (IP) method. The update rules of IP are derived from a system of quadratic equations obtained by differentiating the objective function of ILRMA with respect to demixing vectors. This system of quadratic equations is called hybrid exact-approximate joint diagonalization (HEAD) and no closed-form solution is known yet for three or more sources. Recently, a method that can update two demixing vectors simultaneously has been proposed for independent vector analysis. The method is derived by reducing HEAD for two sources to a generalized eigenvalue problem and solving the problem. Furthermore, the pairwise updates have recently been extended to the case of three or more sources. However, the efficacy of the pairwise updates for ILRMA has not yet been investigated. Therefore, in this work, we apply the pairwise updates of demixing vectors to ILRMA. By replacing the update rules of demixing vectors with the proposed pairwise updates, we accelerate the convergence of ILRMA. The experimental results show that the proposed method yields faster convergence and better performance than conventional ILRMA.
Signal-based and Model-based Wheel Fault Detection of Omni-directional Vehicle with Mecanum Wheel

This paper uses an omni-directional four-wheel Mecanum vehicle to study the model-based and signal-based wheel fault behavior. In order to identify and detect the actual experiment where a wheel rotation is disabled manually. The two methods discussed approached this detection problem with or without a system model. Both methods use vibrational signal placed at different positions of the vehicle while the signal based identifies frequency shift at fault scenarios and the model-based method estimates the viscous effect coefficient through a simple on-line with the vibrational signals. The feasibility of the two methods through simulation and experiment as well as verify the proposed hypothesis.
Thermodynamics and Kinetics of the Early Steps of Solid-State Nucleation in the Fluid Lipid Bilayer

Temperature-scanning (10−70 °C) calorimetric, densitometric, and acoustic measurements have been performed in aqueous dispersions of dipalmitoylphosphatidylcholine multilamellar vesicles. In the biologically relevant liquid-crystalline state close to the chain freezing point (within 42−50 °C, in our case), the measured quantities display anomalous deviations from the “ideal” shape of their temperature dependence curves. The deviations indicate the early steps of gel-state nucleation within the stable liquid-crystalline state (Frenkel's heterophase fluctuations). An earlier proposed kinetic model of nucleation (Kharakoz et al. J. Phys. Chem. 1993, 97, 9844−9851) has been refined to achieve more clear physical substantiation. When the deviations are analyzed through the refined model, both the thermodynamic and kinetic characteristics of nucleation are determined. The fluid−solid interface line tension energy is about 0.7 kT per boundary lipid molecule. The tension is strong enough for the phase transition .
Learning without Forgetting for Decentralized Neural Nets with Low Communication Overhead

We consider the problem of training a neural net over a decentralized scenario with a low communication over-head. The problem is addressed by adapting a recently proposed incremental learning approach, called ‘learning without forgetting’. While an incremental learning approach assumes data availability in a sequence, nodes of the decentralized scenario can not share data between them and there is no master node. Nodes can communicate information about model parameters among neighbors. Communication of model parameters is the key to adapt the ‘learning without forgetting’ approach to the decentralized scenario. We use random walk based communication to handle a highly limited communication resource.
Multi-subject Resting-State fMRI Data Analysis via Generalized Canonical Correlation Analysis

Functional magnetic resonance imaging (fMRI) is one of the most widespread methods for studying the functionality of the brain. Even at rest, the Blood Oxygen Level Dependent (BOLD) signal reflects systematic fluctuations in the regional brain activity that are attributed to the existence of resting-state brain networks. In many studies, it is assumed that these networks have a common spatially non-overlapping manifestation across subjects, defining a common brain parcellation. In this work, we propose an fMRI data generating model that captures the existence of the common brain parcellation and present a procedure for its estimation. At first, we employ generalized Canonical Correlation Analysis (gCCA) – a well-known statistical method, which can be used for the estimation of a common linear subspace – and recover the subspace that is associated with the common brain parcellation. Then, we obtain an estimate of the common whole-brain parcellation map by solving a semi-orthogonal nonnegative matrix factorization (s-ONMF) problem. We test our theoretical results using both synthetic and real-world fMRI data. Our experimental findings corroborate our theoretical results, rendering our approach a very competitive candidate for multi-subject resting-state whole-brain parcellation.
Analyzing the Potential of Pre-Trained Embeddings for Audio Classification Tasks

In the context of deep learning, the availability of large amounts of training data can play a critical role in a model’s performance. Recently, several models for audio classification have been pre-trained in a supervised or self-supervised fashion on large datasets to learn complex feature representations, socalled embeddings. These embeddings can then be extracted from smaller datasets and used to train subsequent classifiers. In the field of audio event detection (AED) for example, classifiers using these features have achieved high accuracy without the need of additional domain knowledge. This paper evaluates three state-of-the-art embeddings on six audio classification tasks from the fields of music information retrieval and industrial sound analysis. The embeddings are systematically evaluated by analyzing the influence on classification accuracy of classifier architecture, fusion methods for file-wise predictions, amount of training data, and initial training domain of the embeddings. To better understand the impact of the pre-training step, results are also compared with those acquired with models trained from scratch. On average, the OpenL3 embeddings performed best with a linear SVM classifier. For a reduced amount of training examples, OpenL3 outperforms the initial baseline.
ELF Analyzer Demo: Online Identification for IoT Malwares with Multiple Hardware Architectures

This demonstration presents an automatic IoT runtime platform with a web interface, ELF Analyzer, where suspicious ELF files uploaded by users could be executed and dynamically analyzed for malicious behavior identification. The key component of our platform is a crafted IoT sandbox, where multiple hardware architectures are emulated using QEMU. With the introduction of strace functionality, we demonstrate that system call and traffic logs of an uploaded ELF file with different hardware architectures can be generated successfully. After proper analysis, malicious ELF files can be identified.
Soybean crop disease classification using machine learning techniques

Machine learning is very widely used for many applications like classification and regression. Diseases in the soybean crop are classified using machine learning techniques. Physic crop properties and weather parameters are used as a attributes for classification. K nearest neighbor, naive Bayes, decision tree, neural network algorithms are used for classification. The result is compared with the ensemble classifier called bagging.
Robust Adaptive Fuzzy Control Using Genetic Algorithm for Dynamic Positioning System

This paper aims to develop a genetic algorithm to adjust an fuzzy controller for Vessels’ Dynamic Positioning System (Vessels’ DPs). It is well-known that nonlinearities affecting the control accuracy of the DPs are related to the arrangement of different types of thrusters in the vessels, such as azimuth thrusters (electric, L-drive, and Z-drive), bow thrusters, stern thrusters, water jets, and propulsion propellers with rudders. Compared with the traditional fuzzy control methods, the proposed Robust Adaptive Fuzzy Control using Genetic Algorithm (RAFC-GA) not only overcomes the influence of nonlinearities in the DPs, but also eliminates the impact of parameter uncertainties. Therefore, the tracking performance is excellent and robustness is maintained. The RAFC-GA control method is superior to the conventional fuzzy control methods in the two following aspects: 1) to find the optimal values for the fuzzy structure parameters to satisfy the robust condition under the effect of disturbances and nonlinearities in the DPs without weakening the output tracking performance and robustness, 2) to improve the quality of the system by optimizing values for the fuzzy structure using genetic algorithm which dynamically adjusted the coverage domain width and the overlap degree of membership functions. Simulation results of the RAFC-GA are evaluated in comparison with other methods. The RAFC-GA performs the desired transient response of DPs better than others in three case studies, which proves the effectiveness of the proposed solution.
Semi-automated estimation of spinal curvature from scoliosis radiographs using difference matrix

Scoliosis is a medical condition in which there is a deformity and bending of the spine. Early detection and corrective measures can prevent severe cases, mainly in early childhood. The gold standard method for the diagnosis of scoliosis is the measurement of spine curvature using Cobb angle. In this paper, a semi-automated approach for accurate estimation of Cobb angles from scoliosis radiographs using a difference matrix is proposed. Given the landmark points of the vertebral columns in the spine, the spinal mid-line is determined by curve fitting of the 4th degree polynomial. Using a difference matrix created by calculating slopes in key points along the curve, Cobb angles are calculated. Data used in this work is obtained from the Accurate Automated Spinal Curvature Estimation (AASCE) Challenge 2019. The method is evaluated using mean absolute error and symmetric mean absolute percentage error. The proposed method gives promising results and could be used in accurate estimation of Cobb angle for the diagnosis of scoliosis.
Optimal Scheduling of Grid Transactive Home Demand Responsive Appliances Using Polar Bear Optimization Algorithm

The conventional power system has been evolving towards a smart grid system for the past few decades. An integral step in successful realization of smart grid is to deploy renewable energy resources, particularly rooftop photovoltaic systems, at smart homes. With demand response opportunities in smart grid, residential customers can manage the utilization of their demand responsive appliances for getting economic benefits and incentives in return. In this regard, this paper proposes an effective home energy management system for residential customer to optimally schedule the demand responsive appliance in the presence of local photovoltaic and energy storage systems. For efficient home-to-grid energy transactions in home energy management system, the stochastic nature of photovoltaic power generation is modeled with the beta probability distribution function for solar irradiance. The main contribution of this paper is the application of polar bear optimization (PBO) method for optimally solving the scheduling problem of demand responsive appliances in home energy management system to minimize electricity consumption cost as well as peak-to-average ratio. The effectiveness of the proposed metaheuristic optimization technique is proven by performing different case studies for a residential consumer with different base load, uninterruptible deferrable, and interruptible deferrable appliances under a real-time energy price program. Comparative results with different metaheuristic techniques available in the literature show that the electricity consumption cost and peak-to-average ratio are effectively optimized using the proposed PBO algorithm.
Dementia Classification using Acoustic Descriptors Derived from Subsampled Signals

Dementia is a chronic syndrome characterized by deteriorating cognitive functions, thereby impacting the person’s daily life. It is often confused with decline in normal behavior due to natural aging and hence is hard to diagnose. Although, prior research has shown that dementia affects the subject’s speech, but it is not studied which frequency bands are being affected, and up to what extent, that in turn might influence identifying the different stages of dementia automatically. This work investigates the acoustic cues in different subsampled speech signals, to automatically differentiate Healthy Controls (HC) from stages of dementia such as Mild Cognitive Impairment (MCI) or Alzheimer’s Disease (AD). We use the Pitt corpus of DementiaBank database, to identify a set of features best suited for distinguishing between HC, MCI and AD speech, and achieve an F-score of 0.857 which is an absolute improvement of 2.8% over the state of the art.