Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Evaluation Of Convolutions And Atrous Convolutions In Deep Networks For Retinal Disease Segmentation On Optical Coherence Tomography Volumes

The deterioration of the retina center could be the main reason for vision loss. Older people usually ranging from 50 years and above are exposed to age-related macular degeneration (AMD) disease that strikes the retina. The lack of human expertise to interpret the complexity in diagnosing diseases leads to the importance of developing an accurate method to detect and localize the targeted infection. Approaching the performance of ophthalmologists is the consistent main challenge in retinal disease segmentation. Artificial intelligence techniques have shown enormous achievement in various tasks in computer vision. This paper depicts an automated end-to-end deep neural network for retinal disease segmentation on optical coherence tomography (OCT) scans. The work proposed in this study shows the performance difference between convolution operations and atrous convolution operations. Three deep semantic segmentation architectures, namely U-net, Segnet, and Deeplabv3+, have been considered to evaluate the performance of varying convolution operations. Empirical outcomes show a competitive performance to the human level, with an average dice score of 0.73 for retinal diseases.
An Efficient Lens Structures Segmentation Method on AS-OCT Images

Lens structures segmentation on anterior segment optical coherence tomography (AS-OCT) images is a fundamental task for cataract grading analysis. In this paper, in order to reduce the computational cost while keeping the segmentation accuracy, we propose an efficient segmentation method for lens structures segmentation. At first, we adopt an efficient semantic segmentation network in the work, and used it to extract the lens area image instead of the conventional object detection method, and then used it once again to segment the lens structures. Finally, we introduce the curve fitting processing (CFP) on the segmentation results. Experiment results show that our method has good performance on accuracy and processing speed, and could be applied to CASIA II device for practical applications.
Efeito da temperatura sobre a expressão de fatores miogênicos no músculo estriado esquelético do pacu (Piaractus mesopotamicus)

Erro! Indicador nao definido. 1. INTRODUCAO 9 1.1. Organizacao da musculatura estriada esqueletica nos peixes 9 1.2. Miogenese nos peixes 11 1.3. Controle molecular da miogenese 14 1.4. Crescimento muscular pos-embrionario em peixes 17 1.5. Controle molecular do crescimento muscular 21 1.6. Fatores de regulacao miogenica (MRFs) 21 1.7. Miostatina 22 1.8. Fatores que afetam o crescimento muscular 26 2. OBJETIVOS 34 3. MATERIAL E METODOS 34 3.1. Cultivo do pacu (Piaractus mesopotamicus) 34 3.2. Processamento das amostras: 35 3.3. Extracao do RNA total 36 3.4. Tratamento do RNA com DNase 37 3.5. Transcricao Reversa (RT) do RNA total 37 3.6. Expressao quantitativa dos genes MyoD, miogenina e miostatina 37 4. BIBLIOGRAFIA 40 CAPITULO 1 52
Convolution Pyramid Network: A Classification Network on Coronary Artery Angiogram Images

With the development of Convolutional Neural Network, the classification on ordinary natural images has made remarkable progress by using single feature maps. However, it is difficult to always produce good results on coronary artery angiograms because there is a lot of photographing noise and small class gaps between the classification targets on angiograms. In this paper, we propose a new network to enhance the richness and relevance of features in the training process by using multiple convolutions with different kernel sizes, which can improve the final classification result. Our network has a strong generalization ability, that is, it can perform a variety of classification tasks on angiograms better. Compared with some state-of-the-art image classification networks, the classification recall increases by 30.5% and precision increases by 19.1% in the best results of our network.
Performance Comparison Of Enhanced Direct Torque Control Of Two Level And Three Level Inverter Fed Induction Motor using Two Estimators Each

An improved Direct Torque Control based on the sine pulse is studied in this work. The chosen technique proves its effectiveness by decreasing torque/flux ripples and fixing the switching frequency. In the other hand, the use of an NPC three level inverter to feed the asynchronous motor will improve the quality of signals and minimize the harmonics. Moreover, eliminating mechanical speed sensor and adopting software estimators will more improve the system reliability. The control strategy uses two types of estimators; The Direct Calculation Method and the observer based on Sliding Mode. In order to evaluate the suggested systems performance, four comparisons with MATLAB/SIMULINK platform will be done.
Parametric Analysis of Angular Rotation for Microstrip Patch Antenna with Elliptical Patch and Parasitic Elements

We design two microstrip patch antennas in order to investigate the impact of parasitic elements. Both antennas have an elliptical patch while one antenna has two additional elliptical parasitic elements. Then we explore the effect of angular rotation of the patch and the parasitic elements for the antenna with the parasitic elements. Defined range of angular rotation is between 0°–60° in 15° increments. We provide return loss and radiation pattern results for each rotation angle. We evaluate that the inclusion of the parasitic elements causes a downshift in −10 dB impedance bandwidth, and an increase in fractional bandwidth. We obtain the highest radiation levels and fractional bandwidth for the rotation angles of 30° and 15°, respectively. Maximum gain is 4.16 dBi for the rotation angle of 30°. Both antennas (with and without the parasitic elements) could be used for S-band radar applications, and appropriate antenna for a specific application could be chosen in terms of antennas' performances and application needs.
Quantification of feature importance in automatic classification of power quality distortions

Automatic classification of power quality distortions has been studied extensively. Many studies adopted the Stockwell Transform as an appropriate signal processing technique. In this paper, features extracted from the Stockwell Transform are used in two classification techniques. Some of these features have not been seen before in any study on power quality classification. The contribution of this paper is the analysis of these features to determine their importance in classification results. This analysis is not common in power quality studies. As a result, a feature based on computing the contour of the third harmonic was found to be the most discriminant feature. For the study, datasets at different noise levels were generated using a public model. They were uploaded to a public repository to be reused by any interested researcher.
Screening and analysis of specific language impairment in young children by analyzing the textures of speech signal

A child having a delayed development in language skills without any reason is known to be suffering from specific language impairment (SLI). Unfortunately, almost 7% kindergarten children are reported with SLI in their childhood. The SLI could be treated if identified at an early stage, but diagnosing SLI at early stage is challenging. In this article, we propose a machine learning based system to screen the SLI speech by analyzing the texture of the speech utterances. The texture of speech signals is extracted from the popular time-frequency representation called spectrograms. These spectrogram acts like a texture image and the textural features to capture the change in audio quality such as Haralick’s feature and local binary patterns (LBPs) are extracted from these textural images. The experiments are performed on 4214 utterances taken from 44 healthy and 54 SLI speakers. Experimental results with 10-fold cross validation, indicates that a very good accuracy up to 97.41% is obtained when only 14 dimensional Haralick’s feature is used. The accuracy is slightly boosted up to 99% when the 59-dimensional LBPs are amalgamated with Haralick’s features. The sensitivity and specificity of the whole system is up to 98.96% and 99.20% respectively. The proposed method is gender and speaker independent and invariant to examination conditions.
Reconstructing the Perceived Faces from Brain Signals without Large Number of Training Samples*

Reconstructing the perceived faces from brain signals has become a promising work recently. However, the reconstruction accuracies rely on a large number of brain signals collected for training a stable reconstruction model, which is really time consuming, and greatly limits its application. In our current study, we develop a new framework that can efficiently perform high-quality face reconstruction with only a small number of brain signals as training samples. The framework consists of three mathematical models: principle component analysis (PCA), linear regression (LR) and conditional generative adversarial network (cGAN). We conducted a functional Magnetic Resonance Imaging (fMRI) experiment in which two subjects’ brain signals were collected to test the efficiency of our proposed method. Results show that we can achieve state-of-the-art reconstruction performance from brain signals with a very limited number of fMRI training samples.
Limitations of Feature-Classifier Strategies on Pedestrian Detection for Self Driving Cars

Evolutionary enhancements are involved by deep learning in computer vision for getting better performance at human-computer interaction. One of the subjects that are known as Pedestrian Detection (PD), is criticized with a lot of problems that are needed to be solved for autonomous cars. Although a significant amount of work has been done for solving these problems, the outcomes have not satisfied the needs of PD. The shortcomings are mainly attributed to datasets, which are believed to be extended significantly to cover real-life scenarios, and utilizing systems, which seem to fail to cover challenging cases due to high dependence on parameters and low generalization capacity. For solving these problems, extensive datasets are collected and existing annotations are updated. More complex and advanced detection/classification systems are developed. Although higher accuracies can be achieved, such datasets and models cause further problems in real-time operation. Accordingly, this study focuses on PD and provides insights from multiple challenging perspectives. First, the main goal is building models for Alpha Development Board, which is constructed for Advanced Driver Assistance Systems. Since the use of deep models is still not easy to be executed on dedicated hardware, as a second step, one of the most used approaches to boost PD performance, well-established hand-crafted feature-classifier combinations, are implemented. Third, the implemented methods are applied to recent datasets to observe the performance as well as inter-dataset dependency. The results show that, albeit being complementary, different feature-classifier pairs can only provide acceptable accuracy for cases that do not include any challenging scenarios.
A “Power Sharing Model” (PSM) for Buildings of the Public Administration

The new concept of renewable energy communities introduced by the Revised European Directive on the promotion of renewable sources (2018/2001) has opened new possibilities for microgrids. In fact, it permits to enhance the value of the energy produced by renewable sources sharing it inside an “energy community” and to increase the social welfare. In the present paper, the authors investigated about the actual legislation framework on energy communities at European and Italian level, highlighting regulatory problems and barriers that are delaying their constitutions. The authors propose a “power sharing model” (PSM) useful for energy communities and based on the sharing of renewables and other energy services. PSM is suitable both at building level and for larger communities. PSM has been analyzed through a case study that regards the preliminary study of a smart microgrid that should be realized in Campobasso, Italy, to connect buildings of the public administration. This scenario was investigated through a simulation conducted in Simulink environment were the control strategy was implemented, and the results were compared to a traditional configuration for renewable sources integration.
Design of Intelligent Vehicle Based on Dynamic Wireless Charging

The design is a wireless charging system with the microcomputer of MSP430 as the main controller, a super capacitor group as the energy storage device, and a dynamic wireless charging as the core technology. The system is divided into main controller based on the chip of MSP430, wireless charging module, the module for detecting charge, the module for vehicle to track, super capacitor group and the module for detecting the voltage of capacitor. The wireless charging piles are laid on the driving path of the vehicle. After the vehicle enters the charging area, the charging detection module sends a charging signal to the main controller to make the vehicle slow to obtain sufficient charging time. Experimental data shows that dynamic wireless charging can improve the endurance of intelligent vehicle.
InvUnet:Inverse the Unet for Nuclear Segmentation in H&E Stained Images

Nuclear segmentation is the starting point for most cancer pathological analysis. Recently,A series of Unet-like deep neural networks have achieved good result in this task. These models are variants of encoder-decoder architecture and can be traced from FCN, which is designed for semantic segmentation task of natural images. However, compared with natural images, the target regions of H&E images are more salient and have lower entropy. In light of the characteristics of H&E stained images and inspired by the two-stage framework of traditional pure image processing methods, we propose an end-to-end network named InvUnet, which considers nuclear segmentation as a bottom-up stage-wise noise reduction process. The key insight is to take lower-level feature maps of CNN as the output of coarse segmentation, and assume that abstract semantic information from higher layers could guide the denoising process. InvUnet is U-shaped and consists of a contracting path like Unet and a denoising path. Contrary to the top-down structure of Unet’s expansive path, the denoising path is bottom-up, starting from the bottom features and combining higher level information to refine the segmentation step-by-step. We conduct experiments on the public avaliable nuclear segmentation dataset. On pixel level segmentation task, InvUnet achieves cleaner segmentation results than Unet with only 1.26M parameters. On instance-level segmentation task, InvUnet combined with weight map for implicit contour learning achieves the best results in separating touching nuclei.
Linear Network Error Correction Coding Revisited

We consider linear network error correction (LNEC) coding when errors may occur on the edges of a communication network of which the topology is known. In this paper, we first revisit and explore the framework of LNEC coding, and then unify two well-known LNEC coding approaches. In LNEC coding, LNEC maximum distance separable (MDS) codes are a type of most important optimal codes. However, the minimum required field size for the existence of LNEC MDS codes is an open problem not only of theoretical interest but also of practical importance, because it is closely related to the implementation of such coding schemes in terms of computational complexity and storage requirement. In this paper, we obtain an improved lower bound on the required field size by developing a graphtheoretic approach. The improvement over the existing results is in general significant. Furthermore, by applying the graphtheoretic approach to the framework of LNEC coding, we obtain a significantly enhanced characterization of the capability of an LNEC code in terms of its minimum distance.
Lung Region Segmentation in Chest X-Ray Images using Deep Convolutional Neural Networks

Lung cancer is, by far, the leading cause of cancer death in the world. Tools for automated medical imaging analysis development of a Computer-Aided Diagnosis method comprises several tasks. In general, the first one is the segmentation of region of interest, for example, lung region segmentation from Chest X-ray imaging in the task of detecting lung cancer. Deep Convolutional Neural Networks (DCNN) have shown promising results in the task of segmentation in medical images. In this paper, to implement the lung region segmentation task on chest X-ray images, was evaluated three different DCNN architectures in association with different regularization (Dropout, L2, and Dropout + L2) and optimization methods (SGDM, RMSPROP and ADAM). All networks were applied in the Japanese Society of Radiological Technology (JSRT) database. The best results were obtained using Dropout + L2 as regularization method and ADAM as optimization method. Considering the Jaccard Coefficient obtained (0.97967 ± 0.00232) the proposal outperforms the state of the art.Clinical Relevance— The presented method reduces the time that a professional takes to perform lung segmentation, improving the effectiveness.
Evaluation of Selectivity and Sensitivity of Heterostructure Junction-Less DG-MOSFET Based Biosensor Considering Heating Effect

In this article, the sensitivity and selectivity of a Si-Ge Hetero-structure Junction-less Double Gate MOSFET (DG-MOSFET) based biosensor has been analyzed by introducing the self-heating issue, when the bio-particles get trapped inside the underlap or cavity region of the sensor. Sensitivity of the proposed device has been analyzed in terms of threshold voltage variation by considering the dielectric modulation method and also by considering the temperature variation. However, the prime focus is to accurately select the temperature range to avoid the performance degradation due to the self-heating issue. Simulation results are obtained by using SILVACO ATLAS tool. So, change in threshold voltage of the device has been considered as the sensing element to study the existence of bio-particles being trapped in the cavity region of the device. Sub-threshold operation has been considered here while analyzing the sensor performance.
MMT-HEAR: Multiple Moving Targets Heartbeats Estimation and Recovery Using IR-UWB Radars

Populations around the world are rapidly ageing. Age-friendly environments address the significance of continuous inhome vital sign monitoring. Impulse Radio Ultra-WideBand (IR-UWB) radar serves as a household healthcare assistance, providing non-contact vital sign monitoring without privacy issues and illumination limitation. However, the body movements bring difficulty in extracting heartbeat from radar signals, let alone obtaining complete information with body occlusions among multiple targets. This paper proposes a Multiple Moving Targets Heartbeat Estimation And Recovery (MMT-HEAR) approach to extract vital signs using IR-UWB radars. CLEAN and Joint Probability Data Association (JPDA) algorithms are firstly performed on each radar to estimate target-to-antenna distances of multiple targets. Considering signal obstruction and attenuation for targets occluded by others, the location-based distance optimization is proposed to refine these distances by combining information from all radars. Then the mapping from signal amplitudes to refined distances is introduced and combined with the Variational Nonlinear Chirp Mode Decomposition (VNCMD) to extract vital signs with body movements. To the best of our knowledge, this is the first attempt to monitor vital signs of multiple moving targets with radars. The averaging accuracy for two moving targets heartbeat monitoring during a 20-minutes observation is 85.93% with MMT-HEAR. Compared to two other conventional methods, the MMT-HEAR approach yields improvements of 16.11% and 10.16%, revealing the efficiency and robustness of this proposed approach.
High-Density Surface Electromyogram-based Biometrics for Personal Identification

Surface electromyogram (sEMG) has been widely applied in neurorehabilitation techniques such as human-machine interface (HMI). The individual difference of sEMG characteristics has long been a challenge for multi-user HMI. However, the individually unique sEMG property indicates its high potential as a biometrics modality. In this work, we propose a novel application of high-density sEMG (HD-sEMG) for personal identification. HD-sEMG can decode the high-resolution spatial patterns of muscle activations, besides the widely studied temporal features, thus providing more sufficient information. We acquired 64-channel HD-sEMG signals on the dorsum of the right hand from 22 subjects during finger muscle isometric contractions. We achieved an accuracy of 99.5% to recognize the identity of each subject, demonstrating the excellent performance of HD-sEMG for personal identification. To the best of our knowledge, this is the first study to employ HD-sEMG for personal identification.Clinical relevance—Our work has proved the huge individual difference of HD-sEMG, which may result from the individually unique bioelectrophysiological activity of human body, deriving from both neural and biomechanical factors. The investigation of subject-specific HD-sEMG pattern may contribute to a better design of subject-specific clinical rehabilitation robots and a deeper understanding of human movement mechanism.
Exploring the underlying mechanism of pain-related disability in hypermobile adolescents with chronic musculoskeletal pain

Abstract Objectives A significant proportion of adolescents with chronic musculoskeletal pain (CMP) experience difficulties in physical functioning, mood and social functioning, contributing to diminished quality of life. Generalized joint hypermobility (GJH) is a risk factor for developing CMP with a striking 35-48% of patients with CMP reporting GJH. In case GJH occurs with one or more musculoskeletal manifestations such as chronic pain, trauma, disturbed proprioception and joint instability, it is referred to as generalized hypermobility spectrum disorder (G-HSD). Similar characteristics have been reported in children and adolescents with the hypermobile Ehlers-Danlos Syndrome (hEDS). In the management of CMP, a biopsychosocial approach is recommended as several studies have confirmed the impact of psychosocial factors in the development and maintenance of CMP. The fear-avoidance model (FAM) is a cognitive-behavioural framework that describes the role of pain-related fear as a determinant of CMP-related disability. Content Pubmed was used to identify existing relevant literature focussing on chronic musculoskeletal pain, generalized joint hypermobility, pain-related fear and disability. Relevant articles were cross-referenced to identify articles possibly missed during the primary screening. In this paper the current state of scientific evidence is presented for each individual component of the FAM in hypermobile adolescents with and without CMP. Based on this overview, the FAM is proposed explaining a possible underlying mechanism in the relations between GJH, pain-related fear and disability. Summary and outlook It is assumed that GJH seems to make you more vulnerable for injury and experiencing more frequent musculoskeletal pain. But in addition, a vulnerability for heightened pain-related fear is proposed as an underlying mechanism explaining the relationship between GJH and disability. Further scientific confirmation of this applied FAM is warranted to further unravel the underlying mechanism. In explaining disability in individuals with G-HSD/hEDS, it is important to focus on both the physical components related to joint hypermobility, in tandem with the psychological components such as pain-related fear, catastrophizing thoughts and generalized anxiety.