Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Frailty Assessment Based on Kinematic Parameters of Daily Living Activities

Frailty is a prevailing phenomena in older people. It is an age related syndrome that can increase the risk of fall in elderly. The people with age above 65 suffers from various functional decline and cognitive impairments. Such deficiencies are conventionally measured subjectively by geriatrics using questionnaire-based methods and clinical tests. Activities of daily living are also assessed in clinical settings by analysing simple tasks performed by the subject such as sit to stand and walking some distances. The clinical methods used to assess frailty and analyse the activity of daily living are subjective in nature and prone to human error. An objective method is proposed to quantitatively measure frailty using inertial sensor mounted on healthy, frail and nonfrail subjects while performing the sit to stand test (SiSt). An artificial neural networks based algorithm is developed to classify the frailty by extracting a unique set of features from 2D -Centre of Mass (CoM) trajectories derived from SiSt clinical test. The results indicate that the proposed algorithms provides an objective assessment of frailty that can be used by geriatrics in turn to make a more objective judgement of frailty status of older people.
A System implementing Fuzzy Hypothetical Datalog⋆

This paper presents a system implementing a novel addition to a fuzzy deductive database: hypothetical queries. Such queries allow users to dynamically make assumptions on a given database instance, either by adding or removing data, without changing the instance. Further, since a fuzzy database includes fuzzy relations, these relations can also be changed with assumptions. This ability for dynamic change seamlessly enables writing "what-if" applications such as decision-support systems. Here, the new language Fuzzy Hypothetical Datalog is presented, along with an operational semantics and stratified inference. It has been implemented in a working system DES readily available on-line.
HLS Based Optimizations of an FPGA Hardware Design for Plenoptic Image Processing Algorithm

Optical 3D image processing algorithms are used in a wide range of applications, such as research, healthcare, industry automation. Generally, such algorithms consist of computation intensive instructions. The performance of general purpose processor based system is a bottleneck because of its sequential nature. FPGA based hardware solution can be an alternative for such problems. Due to its architecture, FPGA can exploit spatial and temporal parallelization and hence the performance with respect to execution time and area can be improved. However, creating hardware with the help of a hardware description language (HDL), such as VHDL, is a complex and time taking process. High-level synthesis (HLS) tools can be employed to resolve this issue. This paper discusses the approach to optimize the HLS based hardware architecture for plenoptic 3D image processing algorithm. It is an iterative process that requires low-level knowledge of the hardware workflow. The results show that the performance is improved.
Layer-wise Pre-training Mechanism Based on Neural Network for Epilepsy Detection

Epilepsy is a common brain disease that has serious negative effects on patients. Electroencephalogram (EEG) is widely used for detecting epileptic signal. Because epilepsy EEG data set is generally small, in this paper, we use a shallow convolutional neural network (CNN) to classify the raw data. To improve the performance of the model, we propose a layerwise pre-training mechanism. In our experiment, we validate the effectiveness of the method on the public epilepsy EEG data set provided by University of Bonn.
Online Recursive ICA Algorithm Used for Motor Imagery EEG Signal

Electroencephalogram (EEG) signals are important to study the activities of human brains. The independent component analysis (ICA) algorithm is a practical blind source separation (BSS) technique that can separate EEG sources from artifacts effectively. However, most traditional ICA algorithms assume that the mixing process is instantaneous and off-line. In this paper, a novel framework based on the extension of the online recursive ICA algorithm (ORICA) is proposed to apply for motor imagery (MI) EEG recording. The contributions are as follows. Firstly, we show ORICA’s adaptability to accurate and effective source separation used for artifact-contaminated MI EEG recording. Secondly, to identify EOG signals on the output of source separation, the topographic map is presented to distinguish the target signals. The experimental results show that the proposed framework is able to be applied to process MI EEG recording in real-time situations.
3D MR image denoising using a modified adaptive high order singular value decomposition method*

Magnetic resonance (MR) images are generally degraded by random noise governed by Rician distributions. In this study, we developed a modified adaptive high order singular value decomposition (HOSVD) method, taking consideration of the nonlocal self-similarity and weighted Schatten p-norm. We extracted 3D cubes from noise images and classified the similar cubes by the Euclidean distance between cubes to construction a fourth-order tensor. Each rank of unfolding matrices was adaptively determined by weighted Schatten p-norm regularization. The latent noise-free 3D MR images can be obtained by an adaptive HOSVD. Denoising experiments were tested on both synthetic and clinical 3D MR images, and the results showed the proposed method outperformed several existing methods for Rician noise removal in 3D MR images.
TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG

Deep learning has become popular for automatic sleep stage scoring due to its capability to extract useful features from raw signals. Most of the existing models, however, have been overengineered to consist of many layers or have introduced additional steps in the processing pipeline, such as converting signals to spectrogram-based images. They require to be trained on a large dataset to prevent the overfitting problem (but most of the sleep datasets contain a limited amount of class-imbalanced data) and are difficult to be applied (as there are many hyperparameters to be configured in the pipeline). In this paper, we propose an efficient deep learning model, named TinySleepNet, and a novel technique to effectively train the model end-to-end for automatic sleep stage scoring based on raw single-channel EEG. Our model consists of a less number of model parameters to be trained compared to the existing ones, requiring a less amount of training data and computational resources. Our training technique incorporates data augmentation that can make our model be more robust the shift along the time axis, and can prevent the model from remembering the sequence of sleep stages. We evaluated our model on seven public sleep datasets that have different characteristics in terms of scoring criteria and recording channels and environments. The results show that, with the same model architecture and the training parameters, our method achieves a similar (or better) performance compared to the state-of-the-art methods on all datasets. This demonstrates that our method can generalize well to the largest number of different datasets.
Formal Algebraic Specification of an IoT/Fog Data Centre for Fat Tree or Leaf and Spine architectures

Fog computing is an evolution of cloud computing paradigm, whose key point is the location of computing resources at the edge of the network. Data center facilities at the fog level are smaller than those at the cloud level, but nevertheless, they may share similar topologies, such as fat tree or leaf and spine architectures. In this paper, a formal algebraic specification of an IoT/Fog environment based on each of both architectures is presented, where users may be moving around and their associated computing assets are meant to migrate among hosts in order to follow their respective users so as to be as close as possible to them.
InSAR Phase Unwrapping Based on Square-Root Cubature Kalman Filter

In this article, we propose a novel phase unwrapping method termed the square-root cubature Kalman filter phase unwrapping (SCKFPU) method. Specifically, as the system model of the interferometric synthetic aperture (InSAR) phase unwrapping can be formulated as the discrete Markovian jump system for tracking a maneuvering target, a phase unwrapping method is proposed by combining the SCKF algorithm with a phase gradient estimator. Moreover, we design a phase quality estimate function, and present an optimal path-following strategy. The strategy ensures that the SCKFPU method simultaneously performs noise suppression and unwraps InSAR images along the pixels with high-reliance to the pixels with low-reliance. Simulation results based on synthetic data, true InSAR data, and measured (real) InSAR data show that the proposed InSAR phase unwrapping method can achieve better performance compared with other existing phase unwrapping methods.
Deep Learning based Quantification of Ovary and Follicles using 3D Transvaginal Ultrasound in Assisted Reproduction

Quantification of ovarian and follicular volume and follicle count are performed in clinical practice for diagnosis and management in assisted reproduction. Ovarian volume and Antral Follicle Count (AFC) are typically tracked over the ovulation cycle. Volumetric analysis of ovary and follicle is manual and largely operator dependent. In this manuscript, we have proposed a deep-learning method for automatic simultaneous segmentation of ovary and follicles in 3D Transvaginal Ultrasound (TVUS), namely S-Net. The proposed loss function restricts false detection of follicles outside the ovary. Additionally, we have used multi-layered loss to provide deep supervision for training the network. S-Net is optimized for inference time and memory while utilizing 3D context in the 2D deep-learning network. 66 3D TVUS volumes (13,200 2D image slices) were acquired from 66 subjects in this Institutional Review Board (IRB) approved study. The segmentation framework provides approximately 92% and 87% average DICE overlap with the ground truth annotations for ovary and follicles, respectively. We have obtained state-of-the-art results with a detection rate of 88%, 91% and 98% for follicles of size 2-4mm, 4-12mm and >12mm.
Comparison of Two Hybrid Global Maximum Power Point Algorithms for Photovoltaic Module Under Both Uniform and Partial Shading Condition

Power vs. Voltage (P-V) characteristics of a photovoltaic module (PV) show multiple peaks under partial shading conditions (PSCs). Most conventional maximum power point tracking (MPPT) techniques can accurately locate the single point under uniform conditions but fail under PSCs. Intelligent algorithms can locate the global point (GMPP) among the local ones (LP) but incur more computational cost. Combining both types as hybrid GMPPT provides more effective performance under different environmental conditions. This paper aims to analyze and compare the performance of two hybrid GMPP techniques under both uniform conditions and partial shading. In the proposed approach, the genetic algorithm (GA) and particle swarm optimization (PSO) are integrated with the perturb and observe algorithm (P&O). The simulation results in Matlab/Simulink confirm that both hybrid algorithms can track the GMPP. Furthermore, they show the ability to differentiate between different environment changes occurrences.
3D Human Knee Flexion Angle Estimation Using Deep Convolutional Neural Networks*

An Anterior Cruciate Ligament (ACL) injury can cause a serious burden, especially for athletes participating in relatively risky sports. This raises a growing incentive for designing injury-prevention programs. For this purpose, the analysis of the drop jump landing test, for example, can provide a useful asset for recognizing those who are more likely to sustain knee injuries. Knee flexion angle plays a key role within these test scenarios. Multiple research efforts have been conducted on engaging existing technologies such as the Microsoft Kinect sensor and Motion Capture (MoCap) to investigate the connection between the lower limb angle ranges during jump tests and the injury risk associated with them. Even though these technologies provide sufficient capabilities to researchers and clinicians, they need certain levels of knowledge to enable them to utilize these facilities. Moreover, these systems demand special requirements and setup procedures which make them limiting. Due to recent advances in the area of Deep Learning, numerous powerful 3D pose estimation algorithms have been developed over the last few years. Having access to relatively reliable and accurate 3D body keypoint information can lead to successful detection and prevention of injury. The idea of combining temporal convolutions in video sequences with deep Convolutional Neural Networks (CNNs) offer a substantial opportunity to tackle the challenging task of accurate 3D human pose estimation. Using the Microsoft Kinect sensor as our ground truth, we analyze the performance of CNN-based 3D human pose estimation in everyday settings. The qualitative and quantitative results are convincing enough to give an incentive to pursue further improvements, especially in the task of lower extremity kinematics estimation. In addition to the performance comparison between Kinect and CNN, we have also verified the high-margin of consistency between two Kinect sensors.
Gray-Wyner and Slepian-Wolf Guessing

We study the guessing variants of two distributed source coding problems: the Gray-Wyner network and the Slepian-Wolf network. Building on the former, we propose a new definition of the Rényi common information as the least attainable common rate in the Gray-Wyner guessing problem under the no-excess-rate constraint. We then provide a variational characterization of this quantity. In the Slepian-Wolf setting, we follow up the work of Bracher-Lapidoth-Pfister with the case where the expected number of guesses need not converge to one but must be dominated by some given exponential.
Produção e purificação de DNA plasmidial a partir de Escherichia coli recombinante

Cultures of recombinant Escherichia coli containing the plasmid pD2 were grown in two medium TB (Terrific broth) or LBG (Luria Bertani with glucose). Three velocity of agitation (120, 160 and 200 rpm) and five kanamycin concentrations (10, 20, 30, 40 and 50 μg mL ) were the parameters studied for to assess their effects of cultural conditions on plasmid DNA production and stability for plasmid-based gene therapy. The velocity of agitation that provided highest results in relationship biomass and plasmid DNA production was 200 rpm. While the concentrations 30 to 50μg mL of kanamycin provided similar results for all the studied conditions and the culture medium TB medium which showed of better results for the biomass production and DNA. The plasmid was (82%) stable in TB medium.
MAPSYNE: Miniaturized micropipette system combined with high-density microelectrode arrays for automated manipulation of neuronal networks in-vitro

We present MAPSYNE, a miniaturized and automated system combining a high-density microelectrode array (HD-MEA) and a movable micropipette for studying, monitoring, and perturbing neurons in vitro. The system involves an all-electrical approach to automatically move a glass micropipette towards a target location on the HD-MEA surface, without the need for a microscope. Two methods of performing blind navigation are employed, (i) stop-measure-go approach wherein the pipette moves for a predefined distance before measuring its location then the process is repeated until the pipette reaches its destination, and (ii) predictive approach wherein the pipette is continuously tracked and moved. This automated system can be applied for unsupervised single-cell manipulation of neurons in a network, such as electroporation and local delivery of compounds.
Short-wave Infrared Neural Stimulation Drives Graded Sciatic Nerve Activation Across A Continuum of Wavelengths

Infrared neural stimulation (INS) is an optical stimulation technique which uses coherent light to stimulate nerves and neurons and which shows increased spatial selectivity compared to electrical stimulation. This could improve deep brain, high channel count, or vagus nerve stimulation. In this study, we seek to understand the wavelength dependence of INS in the near-infrared optical window. Rat sciatic nerves were excised ex vivo and stimulated with wavelengths between 700 and 900 nm. Recorded compound nerve action potentials (CNAPs) showed that stimulation was maximized in the 700 nm window despite comparable laser power levels across wavelengths. Computational models demonstrated that wavelength-based activation dependencies were not a result of passive optical properties. This data demonstrates that INS is both wavelength and power level dependent, which inform stimulation systems to actively target neural microcircuits in humans.
SVT-AVS3: Scalable Video Technology of AVS3

In this demo, a novel AVS3 8K encoder is presented, namely SVT-AVS3. Based on the Intel® Scalable Video Technology (SVT) architecture, the proposed encoder achieves an optimal trade-off among coding performance, encoding speed and visual quality. In particular, SVT-AVS3 carefully considers multidimensional parallelism and perceptual based rate-distortion optimization. Simulation results show that SVT-AVS3 can support 8K-UHD real-time encoding at 10-bit depth with bit rate up to 100Mbps and significantly outperforms the state-of-the-art x265 encoder. Furthermore, a series of preset levels are delicately designed to satisfy the increasing diversity of services, especially for the 8K-UHD scenarios.
A Flexible Patch-Type Strain Sensor Based on Polyaniline for Continuous Monitoring of Pulse Waves

A flexible, patch-type strain sensor is described for continuous monitoring of pulse waves. The proposed sensor exploits the piezo-resistivity of the conductive polymer, polyaniline (PANI), to detect dynamic volume changes in blood vessels owing to pulse waves. The proposed PANI film was fabricated through electrodeposition, which is considered as a suitable low-cost technique for mass production in the sensor manufacturing industry. Thus, it is prospective for a disposable wearable sensing system solution in remote healthcare applications. Besides, a flexible sensor packaging can be achieved by laminating the PANI films and an ECOFLEX elastomer to the film bandage. The proposed PANI sensor has high sensitivity (gauge factor of 74.28) and linearity (R2 = 0.99). It also showed a high correlation with commercially available photoplethysmography (PPG) sensor with the small bias and confidence interval to the PPG sensor: bias < 0.1% and confidence interval < 3% for all subjects. Moreover, the proposed PANI sensor was tested for prospective circulatory system-related applications such as measuring heart rate, stiffness index, and pulse transit time. Finally, the proposed study suggests that the proposed PANI sensor is a promising candidate for continuous, long-term, unobtrusive pulse wave monitoring, which can provide real-time insights into an individual’s health status.
3D Gait Tracking by Acoustic Doppler Effects

Rehabilitation and physical therapies can recover people suffering from neurological disorder. Due to limited medical personnels, there are not enough medical personnels help patients with their posture diagnosis. In this paper, we propose a 3D gait tracking method to help medical personnels monitor patients. Based on acoustic signals, our approach derives displacement by only one integration of velocity. When one walks, his feet move back and forth, causing relative movements to our acoustic sensors, which we call self-Doppler effect. We utilize three buzzers and one microphone mounted on feet to collect the frequency shifts caused by relative movements and measure 3D trajectories. We validate through simulations that this approach would perform very well. In real experiments, due to the existence of noise and the limitation of hardware, we observe an average error of 0.1669 m in step length estimation and 0.0867 m in step height estimation.
A quality metric for heart rate variability from photoplethysmogram sensor data

Heart rate variability (HRV) measures the regularity between consecutive heartbeats driven by the balance between the sympathetic and parasympathetic branches of the autonomous nervous system. Wearable devices embedding photoplethysmogram (PPG) technology can be used to derive HRV, creating many opportunities for remote monitoring of this physiological parameter. However, uncontrolled conditions met in daily life pose several challenges related to disturbances that can deteriorate the PPG signal, making the calculation of HRV metrics untrustworthy and not reliable. In this work, we propose a HRV quality metric that is directly related to the HRV accuracy and can be used to distinguish between accurate and inaccurate HRV values. A parametric supervised approach estimates HRV accuracy using a model whose inputs are features extracted from the PPG signal and the output is the HRV error between HRV metrics obtained from the PPG and the ECG collected during an experimental protocol involving several activities. The estimated HRV accuracy of the model is used as an indication of the HRV quality.
On Top-k Selection from m-wise Partial Rankings via Borda Counting

We analyze the performance of Borda counting algorithm on noisy m-wise ranking data to accurately select the top-k items from a total of n items. This generalizes a previous result of a similar nature reported by Shah et al. on the noisy pairwise comparison data. We show that the associated score separation Δk between the k-th item and the (k+1)-th item plays an important role: if Δk is greater than a threshold depending on (n, k) and the scoring system in Borda counting, then the top-k selection is accurate asymptotically almost surely; if Δk is below a threshold, then the top-k selection will not be accurate with at least a constant probability. This separation between the two thresholds depends on m and the scoring systems in the Borda counting procedure.
Site specific prediction of PCI stenting based on imaging and biomechanics data using gradient boosting tree ensembles

Cardiovascular diseases are nowadays considered as the main cause of morbidity and mortality worldwide. Coronary Artery Disease (CAD), the most typical form of cardiovascular disease is diagnosed by a variety of imaging modalities, both invasive and non-invasive, which involve either risk implications or high cost. Therefore, several attempts have been undertaken to early diagnose and predict either the high CAD risk patients or the cardiovascular events, implementing machine learning techniques. The purpose of this study is to present a classification scheme for the prediction of Percutaneous Coronary Intervention (PCI) stenting placement, using image-based data. The proposed classification model is a gradient boosting classifier, incorporated into a class imbalance handling technique, the Easy ensemble scheme and aims to classify coronary segments into high CAD risk and low CAD risk, based on their PCI placement. Through this study, we investigate the importance of image based features, concluding that the combination of the coronary degree of stenosis and the fractional flow reserve achieves accuracy 78%.