Miracle-dz/UniFAR-FTUs huggingface dataset:
SoiCP: A Seamless Outdoor–Indoor Crowdsensing Positioning System

Seamless outdoor–indoor positioning plays a critical role in many emerging applications, e.g., large-coverage user navigation in cities, smart buildings, and analytics of user spatial location big data. It is still challenging to construct a large-scale seamless outdoor–indoor positioning system due to the limited coverage of indoor positioning. In this paper, we propose a seamless outdoor–indoor crowdsensing positioning (SoiCP) system in which a radio map is automatically constructed based on crowdsourcing pedestrian dead reckoning (PDR) traces without professional site surveying. The constructed radio map is robust to inaccurate PDR traces and does not rely on prior knowledge of floor plans. In SoiCP, the crowdsensed radio map is obtained by a proposed three-step trace matching algorithm. This algorithm leverages building gates and WiFi fingerprints as landmarks to merge the noisy crowdsourcing traces and accurately construct the user walking paths. Moreover, following the crowdsensed radio map, SoiCP uses an enhanced particle filter to fuse PDR, GPS, and WiFi fingerprinting for seamless outdoor–indoor positioning with high accuracy. The comprehensive real-world experiments in two large-scale shopping malls demonstrate that SoiCP can effectively crowdsense the walking paths and track moving users with high accuracy.
Spectro-Temporal Feature Based Multi-Channel Convolutional Neural Network for ECG Beat Classification

Automatic classification of abnormal beats in ECG signals is crucial for monitoring cardiac conditions and the performance of the classification will improve the success rate of the treatment. However, under certain circumstances, traditional classifiers cannot be adapted well to the variation of ECG morphologies or variation of different patients due to fixed hand-crafted features selection. Additionally, existing deep learning related solutions reach their limitation because they fail to use the beat-to-beat information together with single-beat morphologies. This paper applies a novel solution which converts one-dimensional ECG signal into spectro-temporal images and use multiple dense convolutional neural network to capture both beat-to-beat and single-beat information for analysis. The results of simulation on the MIT-BIH arrhythmias database demonstrate the effectiveness of the proposed methodology by showing an outstanding detection performance compared to other existing methods.
Minimally Invasive Assessment of Mental Stress based on Wearable Wireless Physiological Sensors and Multivariate Biosignal Processing

The development of connected health technologies for the continuous monitoring of the psychophysical state of individuals performing daily life activities requires the aggregation of non-intrusive sensors and the availability of methods and algorithms for extracting the relevant physiological information. The present study proposes an integrated approach for the objective assessment of mental stress which combines wirelessly connected low invasive biosensors with multivariate physiological time series analysis. In a group of 18 healthy subjects monitored in a relaxed resting state and during two experimental conditions inducing mental stress and sustained attention (respectively, mental arithmetic and serious game), we collected simultaneously multichannel EEG, one lead ECG, respiration and blood volume pulse. From these signals, synchronous physiological time series were extracted measuring the $\delta$, $\theta$, $\alpha$, and $\beta$ EEG amplitudes, the heart period, the sampled respiratory activity and the pulse arrival time. For each condition, five minute windows of each of these seven time series were characterized with measures in the time domain (mean, standard deviation) and in the information domain (self entropy, measuring time series regularity). We show that the dynamical activity of the different physiological systems is affected in a different way by the alteration of the psychophysical state of the subjects induced by stress, and that the measures in the two domains can elicit complementary information about mental stress and sustained attention. These results advocate the feasibility of connected health technology for minimally invasive, automatic classifiers of different levels of mental stress in real life scenarios.
Variations on Branding with Text Occurrence for Optimized Body Parts Classification

The aim of this paper is to combine automatically generated image keywords with radiographs, thus enabling an enriched multi-modal image representation for body part classification. The proposed method could also be used to incorporate meta data into images for combined learning. Multi-modality is achieved by branding the radiographs via intensity markers, which denotes the occurrence of textual features. There is a need to create systems capable of adequately detecting and classifying body parts in radiology images, as the number of digital medical scans taken daily has expeditiously increased. This is a fundamental step towards computer-aided interpretation, as manual annotation is time-consuming, prone to errors and often impractical. Word embeddings are derived from keywords, automatically generated with the Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) Show-and-Tell model, and incorporated by augmentation into radiographs with Word2Vec: Deep learning systems are trained with the augmented radiographs. Using the data sets, Musculoskeletal Radiographs (MURA) and ImageCLEF 2015 Medical Clustering Task, the proposed approach obtains best prediction accuracy, with 95.78 % and 83.90 %, respectively.
Brain-vascular segmentation for SEEG planning via a 3D fully-convolutional neural network

Three dimensional visualization of vascular structures can assist clinicians in preoperative planning, intra-operative guidance, and post-operative decision-making. The goal of this work is to provide an automatic, accurate and fast method for brain vessels segmentation in Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) dataset based on a residual Fully Convolutional Neural Network (FCNN). The proposed NN embeds in an encoder-decoder architecture residual elements which decreases the vanishing effect due to deep architecture while accelerating the convergence. Moreover, a two-stage training has been proposed as a countermeasure for the unbalanced nature of the dataset. The FCNN training was performed on 20 CE-CBCT volumes exploiting mini-batch gradient descent andthe Adam optimizer. Binary cross-entropy was used as loss function. Performance evaluation was conducted considering 5 datasets. A median value of Dice, Precision and Recall of 0.79, 0.8 and 0.69 were obtained with respect to manual annotations.
A novel bimodal stethoscope for gastric collection of heart sounds: preliminary results

Heart sound analysis is commonly used by physicians during auscultations to evaluate cardiac activity as a first line. These sounds originate from heartbeats and the resulting blood flow, and can provide important information about heart function and hemodynamics. The monitoring of heart sounds in patients suffering from chronic cardiac pathologies can be useful to detect or prevent cardiac events. For this purpose, a bimodal implanted gastric stethoscope was developed allowing home monitoring of electrophysiological and mechanical parameters.An in-vivo experiment in pigs was carried out to validate the feasibility of heart sound detection from an accelerometer embedded in a stethoscope prototype implanted in the submucosal layer of the gastric wall. Data recorded over several weeks validate the tolerance and the sensitivity of the device. These promising preliminary results confirm the interest of considering the stomach as a strategic implantation site for heart sound monitoring.
Rectangular Dielectric Rod Antenna Fed by Air-Substrate Parallel Strip Line

In this paper, a rectangular dielectric rod antenna fed by an air-substrate parallel strip line is proposed. The dielectric rod has a compact and regular structure, which is easy to fabricate. The matching of the antenna is mainly determined by the parallel strip line, which simplifies the design process. Fed by an air-substrate parallel strip line, the antenna obtains a relatively uniform magnitude distribution on the surface and a moderate phase constant. With the length of 6 wavelengths at the center frequency of 6 GHz, the antenna generates a high measured gain of 16.7 dBi. The measured 1 dB gain bandwidth is about 18.3%. Over the entire operating band from 5.4 to 6.6 GHz, a stable endfire radiation pattern and a good matching are achieved by the antenna.
Gesture Classification Using LSTM Recurrent Neural Networks

The classification of human hand gestures has gained widespread recognition as a natural and powerful way to interact intuitively and efficiently with computers. Specifically, this approach has facilitated the development of a number of important applications in the medical training field, specially as a way to objectively evaluate surgical tasks of novices compared to an expert surgeon. In this paper, 3D medical gestures, acquired by an instrumented laparoscopic forceps, were classified using Long Short Term Memory (LSTM) recurrent neural networks (RNN). Recognition results are based on gesture dynamics and a comparison of gesture trajectories between novices to an expert motion are presented. Using LSTM RNN, we were able to achieve a recognition rate of 99.1%.
An Effective Encoder-Decoder Network for Neural Cell Bodies and Cell Nucleus Segmentation of EM Images

Neural systems are complicated networks connected by a large number of neurons through gap junctions and synapse. At present, for electron microscopy connectomics research, neuron structure recognition algorithms mostly focus on synapses, dendrites, axons and mitochondria, etc. However, effective methods for automatic recognition of neuronal cell bodies are rare. In this paper, we proposed an effective encoderdecoder network, which extracted segmentation features of neural cell bodies and cell nucleus by the modified residual network and pyramid module. The framework is capable of merging multi-scale contextual information and generating efficient segmentation results by integrating multilevel features. We applied this proposed network on two segmentation tasks for electron microscope (EM) images and compared it with other promising methods as U-Net and deeplab v3+. The results demonstrated that our method achieved the state-of-the-art performance on quality metrics. Finally, we visualized two intact neural cell bodies and cell nucleus to provide a close look into these fine structures.
Visualizing Inertial Data For Wearable Sensor Based Daily Life Activity Recognition Using Convolutional Neural Network*

Nowadays human activity recognition (HAR) plays an crucial role in the healthcare and wellness domains, for example, HAR contributes to context-aware systems like elder home assistance and care as a core technology. Despite promising performance in terms of recognition accuracy achieved by the advancement of machine learning for classification tasks, most of the existing HAR approaches, which adopt low-level handcrafted features, cannot completely deal with practical activities. Therefore, in this paper, we present an efficient wearable sensor based activity recognition method that allows encoding inertial data into color image data for learning highly discriminative features by convolutional neural networks (CNNs). The proposed data encoding technique converts tri-axial samples to color pixels and then arranges them for image-formed representation. Our method reaches the recognition accuracy of over 95% on two challenging activities datasets and further outperforms other deep learning-based HAR approaches.
A Novel Clustering Based Mechanism For Community Detection Using Artificial Intelligence

Community detection with the same nodes is being a challenging task for analyzing social networking data and is broadly calculated in the social networking community for fundamental graph structure. This research has proposed a novel mechanism for community detection system based on GA-NN (Genetic algorithm-Neural network). For the execution, the proposed scheme has gone through various stages, such as preprocessing, feature extraction, feature optimization, and classification. Tokenization algorithm is applied to determine the features of the uploaded test data then GA is used that has optimized the data by using appropriate fitness function that helps to identify the densely connected set of nodes with sparse interconnection among the groups. To differentiate the groups, neural network as a classifier is used. For determining the performance of the proposed community detection system, different parameters such as precision, recall, F-measure and accuracy are measured.
On Single-Image Super-Resolution in 3D Brain Magnetic Resonance Imaging

The objective of this work is to apply 3D super resolution (SR) techniques to brain magnetic resonance (MR) image restoration. Two 3D SR methods are considered following different trends: one recently proposed tensor-based approach and one inverse problem algorithm based on total variation and low rank regularization. The evaluation of their effectiveness is assessed through the segmentation of brain compartments: gray matter, white matter and cerebrospinal fluid. The two algorithms are qualitatively and quantitatively evaluated on simulated images with ground truth available and on experimental data. The originality of this work is to consider the SR methods as an initial step towards the final segmentation task. The results show the ability of both methods to overcome the loss of spatial resolution and to facilitate the segmentation of brain structures with improved accuracy compared to native low-resolution MR images. Both algorithms achieved almost equivalent results with a highly reduced computational time cost for the tensor-based approach.
Exploring intrinsic triggers for functional facial electrostimulation based on intramuscular electromyography recordings

Based on univariate intramuscular electromyography (EMG) recordings of facial muscles of patients suffering from chronic idiopathic facial palsy we propose a data-driven feature selection process for the discrimination of different mimic maneuvers. Following fundamental ideas of automatic EMG decompositions based on templates defined by motor unit action potentials, the proposed approach relies on a multiple template matching. Yet, the novel methodology utilizes templates derived from the intramuscular EMG signal itself without any supervisor interaction or a priori information by identifying abundant short signal sections (motifs). Focusing on motifs as individual, characteristical graphoelements of an EMG recording implies a high level of flexibility. In connection with facial palsy such a flexibility is necessary, since unique individual, also pathological, EMG patterns can be expected due to the high spatial variability of intramuscular recordings combined with random patterns of aberrant reinnervation. The proposed methodology is applied to EMG data of frontalis, zygomaticus, and orbicularis oculi muscle without patient- or muscle-specific adaptations.
Compact HMSIW Semi-Hexagonal Antennas with Rectangular Slot

Design of Half-Mode substrate integrated waveguide (HMSIW) based compact semi-hexagonal antennas, with the use of a tapered rectangular slot to operate for 5 GHz wireless networks has been discussed in this paper. The parent hexagonal SIW cavity is bisected to obtain half-mode structures which results in two different structures Type-1 resonating at 5.9 GHz and Type-2 resonating at 5.57 GHz. The antennas are subsequently downsized by incorporating narrow rectangular slot having resonance at 4.9 GHz and 4.77 GHz respectively. The design part of the antennas was accomplished using the Arlon AD270 substrate and the simulation deductions are analyzed with the values obtained from measurement.
Power Sharing Based Control of Hybrid Wind-Diesel Standalone Systems

Hybrid wind-diesel systems are mainly used to supply remote communities which are located far from the main electric power grid. This paper discusses the integration of hybrid wind-diesel systems through a DC common bus and control the overall system by relying on the power sharing property of this architecture. Such design is cost effective and simple in implementation. Throughout this paper, the results from an preliminary experiment are presented to verify the feasibility of such architecture.
Gender Estimation from Panoramic Dental X-ray Images using Deep Convolutional Networks

Current techniques for gender estimation from X-ray images, except being time-consuming, require a highly experienced expert to perform the process. Deep convolutional neural networks have shown to be a very successful technique in many computer vision tasks, mainly because of high accuracy, stability, and processing speed. In this paper, we propose a new method to the gender estimation from panoramic dental X-ray images based on analysis of images using deep convolutional neural networks trained to perform a binary classification. Detailed insight is provided into architecture, hyperparameters and training procedure of our best performing model obtaining an accuracy of 94.3% on a test set. Further experiments have been performed to get a better understanding of anatomical structures which carry the most important information for gender estimation. The presented method requires no special knowledge or equipment to be used, and besides high accuracy, it is also extremely fast with only 18ms of processing time per image on a dedicated GPU.
Towards Natural Scene Rock Image Classification with Convolutional Neural Networks

Autonomous image recognition has numerous potential applications in the field of planetary science and geology. During exploration, geologists could encounter an unknown rock and instead of having to bring back the sample to the laboratory for analysis, a better approach would be to have a mobile device classify the image of a rock. As well, instead of waiting a long time for a planetary rover to send back an image to Earth for classification, its on-board computer could have a software that could automatically classify images of outcrops. In 2017, Shu et. al. used a Support Vector Machine (SVM) classification algorithm to classify uniform rock images into 9 different classes with the image features extracted autonomously. Through this method, they achieved a classification accuracy of 96.71%. Recent publications have shown that Convolutional Neural Networks (CNNs) perform better than other algorithms in classifying images of everyday objects, more specifically for the ImageNet dataset. In light of this development, this paper demonstrates the use of CNNs to classify the same set of rock images. With the addition of dataset augmentation, a 3-layer CNN is shown to have a significant improvement over Shu et. al.’s results, achieving an average accuracy of 99.60% across 10 trials on the test set. Having proven that CNNs can classify uniform and clean images of rocks, this research then tackles a more interesting and practical problem in classifying natural scene images of rocks where the images are taken during field exploration without a standardized method and specialized equipment. The task has been simplified into a binary classification problem where the images are classified into breccia and non-breccia. This research shows that a 5-layer CNN achieves 89.43% classification accuracy for this task.
Computationally Efficient Light Field Image Compression Using a Multiview HEVC Framework

The acquisition of the spatial and angular information of a scene using light field (LF) technologies supplement a wide range of post-processing applications, such as scene reconstruction, refocusing, virtual view synthesis, and so forth. The additional angular information possessed by LF data increases the size of the overall data captured while offering the same spatial resolution. The main contributor to the size of captured data (i.e., angular information) contains a high correlation that is exploited by state-of-the-art video encoders by treating the LF as a pseudo video sequence (PVS). The interpretation of LF as a single PVS restricts the encoding scheme to only utilize a single-dimensional angular correlation present in the LF data. In this paper, we present an LF compression framework that efficiently exploits the spatial and angular correlation using a multiview extension of high-efficiency video coding (MV-HEVC). The input LF views are converted into multiple PVSs and are organized hierarchically. The rate-allocation scheme takes into account the assigned organization of frames and distributes quality/bits among them accordingly. Subsequently, the reference picture selection scheme prioritizes the reference frames based on the assigned quality. The proposed compression scheme is evaluated by following the common test conditions set by JPEG Pleno. The proposed scheme performs 0.75 dB better compared to state-of-the-art compression schemes and 2.5 dB better compared to the $\times$ 265-based JPEG Pleno anchor scheme. Moreover, an optimized motion-search scheme is proposed in the framework that reduces the computational complexity (in terms of the sum of absolute difference [SAD] computations) of motion estimation by up to 87% with a negligible loss in visual quality (approximately 0.05 dB).
Overview of the main challenges and threats for implementation of the advanced concept for decentralized trading in microgrids

Current trends of decentralization, digitalization, decarbonization and democratization in the power sector enable new business models featuring active participation of the distributed energy resources and distributed storage systems. Moreover, individual and small market participants acting as peers want to trade electricity within local communities. Peer-to-peer decentralized electricity trading in microgrids using distributed ledger technology could be a solution for establishing local markets and accelerating the integration of distributed energy resources. In this paper, main technical, economic, social and regulatory challenges and threats for implementation of the peer-to-peer concept for electricity trading in microgrids are stated and discussed.
The formation of the new diagnostic indexes based on electro- and magnetocardiography in relation to cardiology of cardiac pathologies as part of the revelation of the myocardium with new anatomy and morphology

The task of the study is to determine the specific characteristics of the object under study and apply these parameters as diagnostic indicators. An example of the implementation of this idea on the myocardium: the revelation of the of the myocardium unfolding - the myocardial curling as a specific specific characteristic - the twisting field strength as a diagnostic indicator. Implementation for ECG: the integration of the specificity into the method of the electrocardiogram-signal processing with the detachment of the component of the myocardial twisting. Implementation for magnetocardiogram: direct field measurement - intensity and twisting of the magnetic field. This work was supported by the RFBR grants 17-07-00705 and 18-07-00227.