Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesising motion sensor data from biomechanical simulations to investigate motion sensor placement and orientation variations

We propose a motion sensor data synthesis approach to investigate the performance effect of sensor placement and orientation variation on health marker estimation. Using OpenSim, we simulate walking motion of patients after stroke and synthesise inertial sensor data. We analyse 384 sensor positions with 192 sensors simulated at each leg’s thigh. To demonstrate how synthesised sensor data could be used to analyse the performance of functional ability estimation, we estimated scores from the Lower-Extremity Fugl-Meyer-Assessment (LE-FMA) using regression methods. We evaluated our approach using a public dataset, including 8 stroke patients and showed that LE-FMA scores could be estimated with an error below 0.12 score points on average, compared to manually derived scores. We further show that sensors should be preferably placed at the thigh front. Our approach demonstrates the potential of combining biomechanical simulations and motion sensor data synthesis with algorithms for health marker estimation, thus providing rapid insight into sensor positioning and orientation variation.
Channel-wise Attention in 3D Convolutional Networks for Violence Detection

Aiming at the problems of low computational efficiency and insufficient precision for traditional violent behavior recognition methods, we propose a SELayer-3D Convolutional Neural Network (C3D). Firstly, the C3D model is adopted to extract the spatio-temporal feature information in the video block. Secondly the obtained spatio-temporal features are assigned weights according to the importance degree by SELayer. Finally, the output is predicted by the Softmax classifier. In the test experiment on the CrowdViolence dataset, our method achieves an accuracy of 98.08%, which is 6.08% higher than that of the Deeper 3D Convolutional Neural Network (D3D) model. In the test experiment on the HockeyFight dataset, our method achieves 99.0% accuracy, which is 2.0% higher than that of the FightNet model. The speed can reach 500 FPS or so compared to the artificial feature extraction method of 20–25 FPS. Moreover, experiments show that the proposed method has higher detection accuracy and efficiency.
SLA-Based Agile VM Management in Cloud & Datacenter

Cloud Computing is the main root technology to provide various services over the internet in the current scenario, which is basically powered via virtual machines (VMs) over commodity hardware. As soon as a task arrives in the cloud or data center that task is assigned to a VM as per the task scheduler and resource allocation policy. Whenever cloud or data center do not have sufficient spare resources over VM to assign a task, a new VM is created over host machine which have spare resource capacity in the cloud and/or data center. The VM creation should be in such a manner that will follow the Service Level Agreement (SLA) to maintain the Quality of Service (QoS) for the client. We have proposed SLA-based agile VM management algorithm to minimize the VM creation time and VM response time. We use ghost VM to increase the efficiency of cloud, allocation of new VM is from the ghost VM, and using the average creation time is reduced up to 11.98%. We propose two algorithms first for Admission control and second for Rescheduling of VM’s. Our aim is to reduce the time taken in the rescheduling of the VM. Currently, we are assuming excess VM which are eligible to Garbage Collection do not put overhead over the cloud and they can be handled via background service, and with do not affect foreground services directly and they are handled via the separate management service.
Skin Disease detection based on different Segmentation Techniques

The outer integument of the human body is skin. The skin pigmentation of human beings varies from person to person and human skin type can be dry, oily, or combination. Such a variety in the human skin provides a diversified habitat for bacteria and other microorganisms. Melanocytes in the human skin, produces melanin which can absorb harmful ultraviolet radiation from sunlight which can damage the skin and result in skin cancer. The necessary tools needed for early detection of these diseases are still not a reality in most third world communities. If the symptoms of skin diseases such as acne, dermatomyositis, candidiasis, cellulitis, Scleroderma, chicken pox, ringworm, eczema, psoriasis, etc. are left untreated in its early stage then they can result in numerous health complications and even death. Image segmentation is a technique which aids with the detection of these skin diseases. In this paper, image processing techniques like adaptive thresholding, edge detection, K-means clustering and morphology-based image segmentation have been used to identify the skin diseases from the given image set. The acquired image set was pre-processed by deblurring, noise reduction and then processed. Depending on the definite pattern (pertaining to a distinct disease) present in the processed image the disease is detected at the output for a corresponding input image.
Adaptive Identification Method of Vehicle Modeling According to the Fluctuation of Road and Running Situation in Autonomous Driving

In recent years, study on autonomous driving has been actively conducted. The model-based control is used as one of control methods of autonomous driving. The model-based control in automated driving requires simple and accurate vehicle model that can accurately represent the state of the vehicle and has low processing load on computer memory. Therefore, we represented the modeling error due to fluctuation of road and dynamic characteristics of the vehicle as the deviation of the front wheel steering angle of the kinematics bicycle model which is a simple model. We propose a method to adaptively identify the vehicle model while estimating the modeling error in real time using Neural Network. In this paper, we apply the vehicle model including modeling error to the model predictive control as an example of model-based control, and verify the usefulness of this study in simulation using CarSim.
Protein Subcellular Localization Prediction Based on Internal Micro-similarities of Markov Chains

Elucidating protein subcellular localization is an essential topic in proteomics research due to its importance in the process of drug discovery. Unfortunately, experimentally uncovering protein subcellular targets is an arduous process that may not result in a successful localization. In contrast, computational methods can rapidly predict protein subcellular targets and are an efficient alternative to experimental methods for unannotated proteins. In this work, we introduce a new method to predict protein subcellular localization which increases the predictive power of generative probabilistic models while preserving their explanatory benefit. Our method exploits Markov models to produce a feature vector that records micro-similarities between the underlying probability distributions of a given sequence and their counterparts in reference models. Compared to ordinary Markov chain inference, we show that our method improves overall accuracy by 10% under 10-fold cross-validation on a dataset consisting of 10 subcellular locations. The source code is publicly available on https://github.com/aametwally/MC MicroSimilarities.
Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography

The adrenal glands are important endocrine glands in humans. They are in complex environments with thin vessels around them. It’s meaningful to get the accurate dissection before surgery. However, images used in hospitals are now unable to help doctors with many surgeries, which are produced by digital subtraction angiography. In this study, we used a 3D U-Net model to segment the adrenal tumor vessels in 3D computed tomography angiography slices. The model was evaluated by dice similarity coefficient (DSC) and mean intersection over union (MIoU) with the manually labeled ground truth. The DSC in this model is 94.69% and the MIoU is 90.22%.
Accurate Cross-Section Estimation of Blood Vessels in Choroidal Haller’s Layer: An Iterative Method based on 3D Tensor Voting

Various eye diseases, including polypoidal choroidal vasculopathy (PCV) and age-related macular degeneration (AMD), affect choroidal vasculature early, but possibly minutely. However, due to the complex networked structure of the vasculature, it becomes hard to visualize, analyze and detect such changes in 2D OCT B-scan images. In contrast, algorithmic evaluation of cross-section facilitates clinicians in tracing minute variations in the vessel network, and quantifying those correlated with pathologies, potentially leading to early diagnosis. In this context, we proposed a novel method of estimating vessel cross-sections in choroidal Haller’s layer. Accuracy of our method was evaluated on synthetic as well as clinical data by trained optometrists, and earned a confidence score of 90%, marking about 60% improvement over estimates based on a well-known tree-based method.
Structural Analysis of Complex Atrial Intramural Microstructure from A Multi-layer Model Based on Siamese Network

Quantitative analysis of complex atrial intramural microstructure is a crucial step towards understanding the mechanism behind atrial fibrillation (AF) maintenance. Siamese network was adopted to extract features from computationally simulated multi-layer fibrosis structure. Through analysis of the features produced by the feature extractor, the difference between Non-sustained and Sustained simulations was comprehended intuitively and electrophysiologically. Complex conduction pathway marked by the feature extractor might be an indicator for AF radio-frequency ablation clinically.
Automatic Pavement Crack Detection Using HMRF-EM Algorithm

Cracks have always been a key issue in road maintenance. The shortage of funds and human resources puts high demands on the automatic detection of cracks. In this paper, we present an efficient and accurate crack detection method. There are two main contributions. First, the combination of the adaptive line detector and the hidden Markov random field model and its expectation-maximization (HMRF-EM) algorithm improves precision. Second, the quantitative description of crack region’s credibility and conditional connection increase the integrity and continuity of the crack. The experimental results demonstrate that this method is effective and accurate. And the crack obtained by this method has higher integrity and continuity compared with other existing crack detection methods.
Improving The Performance of Motor Imagery Based Brain-Computer Interface Using Phase Space Reconstruction

In recent decades, motor imagery (MI) based brain-computer interface (BCI) is served as a control system or rehabilitation tool for motor disabled people. But it has limited applications because of its lower classification performance (classification accuracy, Cohen’s kappa coefficient and etc.). The performance depends on the feature extraction techniques and extraction of relevant features from the brain is challenging task. The existing techniques have low classification performance and are computationally inefficient. This paper introduces phase space reconstruction (PSR) to detect various MI activities and improve the performance of the system. First, raw signals were decomposed into multiple frequency sub-bands using filter bank technique. Second, PSR was applied to each sub-band and dynamical behavior of the brain activities has been analyzed. The optimal parameters (time delay and embedding dimension) of PSR were calculated by average mutual information (AMI) and false nearest neighbors (FNN) methods. The time delay and embedding dimension extracted significant features related to MI activities. The significant features were fed into multi-class support vector machine (SVM) and performance of the classifier was evaluated. The performance of the system is based on classification accuracy (%CA) and Cohen’s kappa coefficient (K). The proposed algorithm and classifier were tested on BCI competition-2005, MI dataset-III-a. The results show that the proposed technique increases the classification accuracy by 3.7% and achieved higher performance (%CA = 89.20% and K= 0.85).
Combining Statistical and Machine Learning Techniques in IoT Anomaly Detection for Smart Homes

In this paper, a security solution is proposed for IoT smart homes based on constructing behavioral device templates. These templates are being calculated by combining statistical and machine learning techniques according to their network behavior, captured within a smart home. The generated statistical metrics are being processed in order to produce the appropriate features, which are then used for constructing clusters of devices. The main idea relies on the fact that during an abnormal event, the device will be moved away from the center of the cluster, generating an alert that can be further used for proposing mitigation actions. The methodology followed in the proposed approach is given in detail, while validation is performed on a real smart home dataset. This work is part of a transparent Cyber security framework developed under EU H2020 Project GHOST.
Role of If Density on Electrical Action Potential of Bio-engineered Cardiac Pacemaker: A Simulation Study

Due to the inevitable drawbacks of the implantable electrical pacemaker, the biological pacemaker was believed to be an alternative therapy for heart failure. Previous experimental studies have shown that biological pacemaker could be produced by genetically manipulating non-pacemaking cardiac cells by suppressing the inward rectifier potassium current (IK1) and expressing the hyperpolarization- activated current (If). However, the role of If in such bio-engineered pacemaker is not clear. In this study, we simulated the action potential of biological pacemaker cells by manipulating If-IK1 parameters (i.e., inhibiting IK1 as well as incorporating If) to analyze possible mechanisms by which different If densities control pacemaking action potentials. Our simulation results showed different pacing mechanism between the bioengineered pacemaking cells with and without If. In addition, it was shown that a greater If density might result in a slower pacing frequency, and excessive of it might produce an early-afterdepolarizations-like action potential due to a sudden release of calcium from sarcoplasmic reticulum into the cytoplasm. This study indicated that when IK1 was significantly suppressed, incorporating If may not enhance the pacing ability of biological pacemaker, but lead to abnormal dynamics of intracellular ionic concentration, increasing risks of dysrhythmia in the heart.
Reliability of the Balance Quality Tester (BQT) for balance quality measurement

Balance quality measurement is a key element in the evaluation of numerous conditions, including frailty. Four parameters were extracted from the balance quality assessment for older subjects: Rising Rate (RR), Duration of the stabilization segment (ZD), Stabilogram Area (SA) and Average Velocity of the Trajectory (TV). These are then scored and weighted, thus creating an overall indicator of balance quality. The reliability, the absolute reliability and the minimum difference of the four parameters were evaluated using the intra-class correlation coefficient (ICC), the standard error measurement (SEM) and the Minimal Detectable Change (MDC), respectively. Reproducibility was very high, with ICC values of 0.83, 0.85, 0.88 and 0.95 for RR, ZD, SA and TV, respectively. These results revealed that the parameters are a reliable measure for evaluating balance quality measurement.
Anatomy of Leaf Classification Techniques

There are number of categories of flora and fauna on earth. Vegetation is important for the existence of life. Leafs are the essential part of this vegetation. It is important to recognize them so that we can preserve them for the future. Classification is a process to distinguish the leaf on the basis of features that are extracted from the leaf samples. In this paper different classification techniques based on NN, SVM, K-NN, and the hybrid approaches like SVM-BDT, DHGN using KNN, Incremental, Adaboost classifier are presented which has been used for leaf classification.
Air Quality Forecasting Using SVR with Quasi-Linear Kernel

Air pollution has threaten people’s health. It is urgent for the government to strengthen and enhance air pollution monitoring capacity. In this paper, we propose an air quality prediction model to infer air pollutant concentrations, such as CO, NOx, NO2. The idea is to design a sophisticate piecewise linear model by using a gated linear network. A top k% winner-take-all autoencoder is first built to generate a set of binary sequences as the gate control signals, so as to perform the input space partitioning. The piecewise linear model is then identified in an exact same way as a standard support vector regression (SVR) with a quasi-linear kernel composed by using the gate control signals. Results of our experiments shows that our proposed SVR prediction model outperforms other state-of-the-art methods.
Distributed Trust-Region Method With First Order Models

In this paper, we introduce the trust region concept for distributed optimization. A large class of globally convergent methods of this type is used efficiently in centralized optimization, both constrained and unconstrained. The methods of this class are built on the idea of modeling the objective function at each iteration and taking the new iteration as the minimizer of the model in a certain area, called the trust region. The trust region size, the minimization method and the model function depend on the properties of the objective function. In this paper we propose a general framework and concentrate on the first order methods, i.e., the gradient methods. Using the trust-region mechanism for generating the step size we end up with a fully distributed method with node varying step sizes. Numerical results presented in the paper demonstrate the efficiency of the proposed approach.
Second Higher-Order-Mode Microstrip Leaky-Wave Antenna With I-Shaped Slots for Single Main Beam Radiation in Cross Section

This paper presents a second higher-order-mode microstrip leaky-wave antenna (LWA) for single main beam radiation. Different from its conventional second higher-order leaky-mode counterpart with two symmetric radiation beams, the proposed antenna with an array of I-shaped slots radiates only one main radiation beam at the cross-sectional center plane. By introducing a transversally loaded slot array along the microstrip line, the cross-polarization of the antenna can be largely enhanced, while a longitudinally loaded slot array interrupts the current flow and thus effectively reduces the original two radiation beams. Subsequently, an array of I-shaped slots, including transversal- and longitudinal-slot portions, is etched on the strip conductor and its unique characteristics are then analyzed to obtain a single main radiation beam in the center plane. To validate this proposal, a prototype of single main beam LWA with second higher-order mode is designed, implemented, and fabricated. Owing to larger leakage constant, the LWA presented in this paper radiates higher power within a shorter length in terms of guided wavelength, resulting in a higher gain. The measured results show a beam-scanning capability from 20° to 55° as the frequency varies from 5.1 to 4.6 GHz, while the maximum gain of 14.6 dBi is achieved at 4.9 GHz.
Intelligent mobile object monitoring by unmanned aerial vehicles

The progress in technical characteristics of unmanned aerial vehicles (UAV) creates a background for their expanding use in applications where human capabilities are limited. Such applications as object monitoring in hard-to-get environment, extremal sport rally, rescue operation, require object surveillance in large regions and complex conditions. To provide high level of UAV autonomy three tasks should be solved: intelligent mission planning, object recognition and on the fly mission modifying according to changing conditions. The paper presents algorithms for these tasks based on artificial intelligence techniques.
Design and simulation of an energy harvester utilizes blood pressure variation inside superior vena cava

Pacemaker is a vital electronic medical device that manages the abnormal heart rhythm. One of the technical problems that pacemaker suffers from is its power source which is a lithium-ion battery with short life compared to patient life. So, this work is concerned with designing and simulation of a novel way to harvest energy from pulsatile pressure inside superior vena cava (SVC) to recharge pacemaker battery. The working principle depends on covering the pacemaker lead (which is a wire inserted through SVC during pacemaker implantation surgery) with a layer of polydimethylsiloxane (PDMS) and creates some cavities in this layer. These cavities would be covered with a thin diaphragm of PDMS which will be in contact with pulsatile blood pressure inside SVC. Under this diaphragm, there will be a lead zirconate titanate (PZT) layer with an area of 2.5 × 6 mm2 and thickness of 0.028 mm working as a cantilever that is fixed in cavity wall from one side and free from other side. When the pressure varies inside SVC in a harmonic manner, the PDMS diaphragm deflects up and down causing the PZT layer to deform and hence generate pulsations of electricity for each heartbeat. The harvesting process was simulated and showed that power of 52 nW could be harvested for each cavity which could be used to recharge the pacemaker battery and avoid replacing it.
Exploring Practical Methodologies for the Characterization and Control of Small Quantum Systems

We explore methodologies for characterizing and controlling small quantum systems. We are interested in starting with a description of a quantum system, designing estimators for parameters of the system, developing robust and high-fidelity gates for the system using knowledge of these parameters, and experimentally verifying the performance of these gates. A strong emphasis is placed on using rigorous statistical methods, especially Bayesian ones, to analyze quantum system data. Throughout this thesis, the Nitrogen Vacancy system is used as an experimental testbed. Characterization of system parameters is done using quantum Hamiltonian learning, where we explore the use of adaptive experiment design to speed up learning rates. Gates for the full three-level system are designed with numerical optimal control methods that take into account imperfections of the control hardware. Gate quality is assessed using randomized benchmarking protocols, including standard randomized benchmarking, unitarity benchmarking, and leakage/loss benchmarking.
Model Predictive Control for a PUC5 based Dual Output Active Rectifier

In this paper, an effective finite-control-set model predictive controller (FCS-MPC) is proposed for an active dual output rectifier. The topology under study is a 5-level packed U cells (PUC5) rectifier. The optimized cost function is designed based on the capacitors’ voltages and current errors. The Capacitors’ reference voltages and the peak value of the reference grid current are used to normalize the errors within the cost function. The peak value of the current reference is generated by a PI controller using the capacitors’ voltage errors. The presented results show a proper tracking of the rectifier’s dual output voltages while ensuring a unity power factor.