Miracle-dz/UniFAR-FTUs huggingface dataset:
Static Balance Characterization using a single IMU Located in the Lower Back: Preliminary Results

Balance refers to the dynamics of body posture to prevent falls. For years, researchers have tried to find out which tasks and measures provide optimal detection of balance disorders, so that they can be quantified. This paper proposes the use of an accelerometer sensor located in the lower back to measure the center of mass accelerations and to characterize the subject's static balance. For characterizing the static balance objectively, we propose using normality circles, a centroid, and a dispersion circle during the modified Clinical Test of Sensory Interaction in Balance (mCTSIB) test. The proposed methodology was tested using two groups of subjects (10 healthy and 3 unhealthy). Our methodology for the static balance was compared to the Berg Balance Scale score. The results shown that a subject with lower BBS score obtain lower dispersion circle and is outside the normality circle. Also, our method outperforms a new option since it characterizes the static balance in an objective, portable, simple, and low-cost way. Clinical Relevance— Our proposed methodology to characterize the static balance can help to simplify objectify and reduce the cost of the clinical practice for balance evaluation
Design of a 3D Printed Respiratory Motion Thoracic Phantom

External beam radiotherapy has been developed to enable precise delivery of radiation in order to effectively target cancerous tumours while sparing healthy tissue. The delivered radiation dose is typically verified using phantoms and dosimeters. However, typically the phantoms are static i.e. do not simulate respiratory motion, whereas those that do simulate motion are still simplistic while being high cost in the range of more than 30,000 USD and hence not available at most treatment centres. Currently, there are no phantoms which simulate respiratory motion in use in Malaysia. This paper describes the design of an image based 3D printed phantom which will be used with a 4D motion generation system to simulate respiratory motion. The focus of this paper is the modular design of the phantom due to the limited build volume of the 3D printer in use. Possibilities of tissue equivalence when undergoing CT scanning is also briefly discussed.
Cyber-Physical System for Predictive Maintenance in HVAC Installations in Hotels

This paper describes a framework for predictive maintenance in Heating Ventilation Air Conditioning installations in hotels using a Cyber-Physical System. Predictive maintenance implies high costs for the hotel sector, so that a low-cost energy-efficient design is essential. The proposed framework is based in the general framework proposed by the National Institute of Standards and Technology and is inspired in the three-layer IoT architecture. This fact also provides the system with interoperability and flexibility. Finally, an application of the framework in a real installation – a five-star hotel - is also presented.
A Bandwidth-Enhanced Tunable Decoupling Method for Compact Mobile Terminal Antennas

This article presents a tunable decoupling method for closely spaced multielement antenna systems, which is able to enhance the decoupling bandwidth of each tuning state. The decoupling method utilizes parasitic scatterers and tunable circuit components, and its design approach is systematically studied. The decoupling method is evaluated by performing a simulation in which the decoupling bandwidth in a tuned state for a three-element antenna system is examined. In that case, only the circuit components are optimized. As another example of this procedure, the frequency reconfigurable decoupling bandwidth for a two-element antenna system is shown to increase by optimizing the parasitic scatterer and the circuit components in the lossless and realistic cases. This study is concluded by presenting a realistic evaluation for the decoupling of a two-element antenna in different tuned states.
Creating Computer Vision Models for Respiratory Status Detection

This study aims to use computers to detect and recognize ventilation objects (masks and tubes) and their positions on the patient's face. We created two models: the You Only Look Once (YOLO) and the Transfer Learning (TL) models, to perform this computer vision task. The development processes and comparison of performance will be described in this paper. The TL model had a better performance (93%) compared to the YOLO model (93%). Clinical Relevance- Healthcare providers and researchers interested in the field of computer vision applied in medicine, specifically automatic object detection using video streams or real-time video streaming may benefit from findings reported.
Lightweight neural network based model for real-time precise HR monitoring during high intensity workout using consumer smartwatches

Mobile and wearable electronics is one of the rapidly developing areas of high technologies, which regularly appear new devices that offer new features for monitoring our health, level of physical exertion and everyday activity. From the point of view of medicine and sports, usually wearable devices are used to solve one of two tasks: round-the-clock continuous heart rate monitoring, and the control of the heart rate during workout. One of the drawbacks of such devices is the calculation errors that are caused by the motion artifacts. The most critical of them are those that arise in some cases when performing high-intensity training's, which are conjugate with intense hands movements, such as sprint running or boxing. In addition, limited resources of wearable devices do not always allow usage of very complex algorithms for processing certain events. In our work, an ultra-lightweight framework for a precise real-time heart rate monitoring during the high intensity physical exercises is developed. The model is a combination of a digital signal processing and deep convolutional and recurrent neural networks approaches. From the personalization point of view, the effect of anthropometric parameters has been studied. The model shows an average mean absolute error of 2.4±2.8 bpm during 5 fold cross-validation on an internal dataset, 2.9±3.4 bpm when evaluated on 12 IEEE SPC subjects, and 4.8±5.3 bpm when evaluated on 24 subjects from Wonkwang University dataset. Such results exceed the current state of the art solutions both in terms of the achieved accuracy of heart rate estimation and consumed computational resources. Clinical relevance— This work aims to provide a robust and lightweight neural network based framework for a real time heart rate estimation during high intensity physical exercises
A TCAD Simulation Study on the Short-circuit Performance of 650V P-pillar Offset Super-junction MOSFET

The limitation of Silicon based power MOSFET was broken by the super-junction (SJ) structure, which can provide lower specific on-resistance and higher breakdown voltage compared with the conventional power MOSFET structure. Multi-epitaxial and multi-ion-implant technology, as a mature manufacturing process of the SJ structure, has been widely used in the field of SJ-MOSFET. Therefore, this process is applied to construct the cell structure of 650V SJ-MOSFET in our study. Based on practical application, high current caused by unexpected short circuit will induce an increasing of the internal temperature of SJ-MOSFET, which leads to an irreversible damage in the SJ-MOSFET devices. However, the short-circuit robustness of SJ-MOSFET is still unstable, and the structure needs to be further improved. In our study, the electrical performance of a 650V SJ-MOSFET with offset P-pillar is theoretically investigated by means of technology computer aided design (TCAD) when the SJ-MOSFET is short circuited. The results clearly show that the optimized SJ-MOSFET can withstand the source-drain voltage of 400V for at least 10 μs in the case of the short-circuit. The thermal distribution and peak temperature of the cell structure of SJ-MOSFET are also simulated to assist in the analysis of the short circuit capable of the device. In addition, the hole current density distribution of two SJ-MOSFETs is considered to gain insight into the effect of P-pillar parameters on the short-circuit robustness. The result represents that the structure with offset P-pillar can effectively improve the short-circuit capability.
Cooperative classification of clean and deformed capnogram segments using a voting approach: A trade-off between specificity and sensitivity

Automatic discrimination of clean and deformed segments of capnogram signals is an essential requisite in capnogram-based respiratory assessment. However, improving the performance of this classification task remains challenging, particularly in terms of specificity and sensitivity. The goal of this paper is to address this issue by proposing a cooperative classification approach rather than relying solely on a single classifier. The presented method's main advantage is the vote participation of four distinct classifiers that affects the reliability of the final classification decision. MATLAB simulation was run on a dataset consisting of 200 15-seconds capnogram segments, 100 of which are clean and 100 are deformed. The results revealed a trade-off between the achieved specificity and sensitivity by adjusting the strictness of voting. Being highly strict in the sense of classifying a capnogram segment as clean if and only if all voting classifiers agreed on deciding so, provided specificity and sensitivity of 94% and 81%, respectively. On the contrary, lowering the strictness of voting by considering only one positive vote is sufficient to eventually classify the query capnogram segment as non-deformed gave specificity and sensitivity of 74% and 94%, respectively.
Error Bounds for 3D Localization and Maximum Likelihood Estimation of mm-Wave MISO OFDM Systems in the Presence of Hardware Impairments

Millimeter-wave (<inline-formula> <tex-math notation="LaTeX">$mm$ </tex-math></inline-formula>-wave) multiple-input single-output (MISO) systems are expected to be extremely advantageous for the fifth generation (5G) cellular systems. In fact, these systems are considered key enablers of centimeter-level localization accuracy, even in the case of a single base station (BS). However, there are still fundamental issues that need to be addressed when applying <inline-formula> <tex-math notation="LaTeX">$mm$ </tex-math></inline-formula>-wave MISO systems to practical scenarios, namely the effects of hardware impairments (HWIs). In this study, the 3D localization accuracy of <inline-formula> <tex-math notation="LaTeX">$mm$ </tex-math></inline-formula>-wave MISO OFDM systems is investigated when there are HWIs at both the BS and the mobile station (MS). The localization is performed by estimating the downlink channel parameters of the line-of-sight (LOS) path using only a maximum likelihood (ML) estimator at the MS. We then transform these channel parameters into ones for localization. The Fisher information matrix (FIM) is employed to assess the accuracy of the estimation processes, considering also any non-LOS (NLOS) paths. The limit of localization is calculated in terms of the position error bound (PEB). Computer simulations demonstrate the destructive impacts of HWIs on the localization process. Moreover, it was proven that the effect of NLOS paths from unknown scatters on the localization process is related to the ratio between LOS and NLOS path gains.
Characterizing Water Deficiency induced stress in Plants using Gabor filter based CNN

With the growing population, it has become necessary to increase the agriculture yield too. Chickpea is one of the most important crops in developing countries like India and is an important source of proteins and carbohydrates for a significant part of the population. Abiotic stresses like that produced due to lack of water greatly effect the health of the chickpea plants and the respective yields. As such, being able to monitor the plants for any water related stress can play a major role in enhancing their health and the overall yield. Plant phenotyping approaches that combine non-invasive image analysis and machine learning have been effectively employed to identify and quantify plant health and illnesses in the last decade. In this work, we propose a Gabor filter based CNN model to classify the chickpea plants into different water stress conditions and compare the results with the previous proposed methods. From the experimental results, we conclude that a 12-layer Gabor based CNN model have accuracies of 79% and 77% for the JG and Pusa varieties of chickpea, respectively, that is better than the 23-layer CNN model proposed in a recent work.
Transfer Learning for Automated COVID-19 B-Line Classification in Lung Ultrasound

Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung pathologies. B-lines are a predominant feature in COVID-19, however LUS requires a skilled clinician to interpret findings. To facilitate the interpretation, our main objective was to develop automated methods to classify B-lines as pathologic vs. normal. We developed transfer learning models based on ResNet networks to classify B-lines as pathologic (at least 3 B-lines per lung field) vs. normal using COVID-19 LUS data. Assessment of B-line severity on a 0–4 multi-class scale was also explored. For binary B-line classification, at the frame-level, all ResNet models pretrained with ImageNet yielded higher performance than the baseline nonpretrained ResNet-18. Pretrained ResNet-18 has the best Equal Error Rate (EER) of 9.1% vs the baseline of 11.9%. At the clip-level, all pretrained network models resulted in better Cohen's kappa agreement (linear-weighted) and clip score accuracy, with the pretrained ResNet-18 having the best Cohen's kappa of 0.815 [95% CI: 0.804-0.826], and ResNet-101 the best clip scoring accuracy of 93.6%. Similar results were shown for multi-class scoring, where pretrained network models outperformed the baseline model. A class activation map is also presented to guide clinicians in interpreting LUS findings. Future work aims to further improve the multi-class assessment for severity of B-lines with a more diverse LUS dataset.
An improved zero-voltage and zero-current-switching PWM full-bridge DC-DC converter

An improved isolated zero-voltage and zero-current switching full-bridge pulse width modulation (ZVZCS-FB-PWM) DC-DC converter having dual voltage outputs of 42 V and 5 V are presented. The leading leg achieves zero voltage switching, and the lagging leg with a series diode and active snubber circuit achieves zero current switching. A phase-shifted (PS) PWM technique is used to obtain ZVZCS by equalizing the small dead times. Also, PS-PWM strategy with a robust average current-mode control can regulate the output voltage to obtain stability and reliability. Furthermore, the active snubber circuit is replaced with a flyback converter operated in the ZVS discontinuous conduction mode due to the ringing voltage of the fast recovery diodes in the secondary side. The steady-state analysis of the topology is illustrated. In this paper, simulated and experimental results are shown to verify the validity of the proposed concept. Moreover, this configuration and results in lower conduction losses in the primary side are well suited for applications above a few kilowatts.
MaskFuzzer: A MaskGAN-based Industrial Control Protocol Fuzz Testing Framework

Industrial control network security is undoubtedly important for an industrial control system. Fuzzy testing is an important method to detect network protocol program security vulnerabilities. In order to perform protocol fuzzing effectively, test data must be generated under the guidance of protocol forma, and the protocol needs to be analyzed before the fuzzy test to generate high-quality fuzzy test cases. In this article, we propose a fuzzy testing framework called MaskFuzzer to solve the problems. A generation adversarial network model is used to automatically learn the data structure of system communication, to generate false messages conforming to protocol specifications. In order to prove the availability of our method, we used MaskFuzzer to test the Modbus-Tcpemulator and successfully find some vulnerabilities. In addition, compared with the GAN-based test case generation method and Peach, our method is best.
Design of Wide-Area Damping Controller Based on Modulation of Active Power Demand

Low-frequency oscillations (LFOs) are one of the significant threats to power system stability. They should be appropriately damped in order to avoid severe contingencies in the power system. Various methodologies are adopted to tackle these situations. In the modern power system scenario, management of the distribution side, i.e., load side, is a competent way to make the system stable. However, they are either deployed on the generation or transmission sides. Hence we propose a wide-area damping controller (WADC), which modulates active power on the load side to damp critical LFOs. This system is linearized at a stable operating point, and critical LFOs are identified using modal analysis. Furthermore, geometric measures are utilized to find suitable feedback signals for them. The WADC structure is considered as a one-stage power system stabilizer (PSS). The parameters of WADC are tuned by the metaheuristic particle swarm optimization (PSO) technique to achieve the desired damping ratio. To demonstrate the efficacy of the proposed methodology, dynamic simulations are conducted on 4-machine 11-machine system. Results are generated on MATLAB/SIMULINK® platform, which suggests that the system is able to achieve the desired damping performance with the proposed WADC design.
Deep Formality: Sentence Formality Prediction with Deep Learning

Formality analysis is a very important task in natural language processing because formality is a dimension of linguistic variation that language users draw on strategically to achieve effective communications under different situations. However, to our best knowledge, there has been no research that conducts formality prediction with deep learning techniques. Most existing work captures formality with statistical methods based on lexical features and human perception of formality. To fill in this gap, our work focuses on automatic text formality prediction with deep learning methods. In this paper, we proposed two deep learning models, Formality-LSTM and Formality-BERT, for formality prediction that do not need feature engineering. Formality-LSTM is a LSTM-based deep neural network that takes the text and corresponding part-of-speech tag as inputs and outputs the formality score. Formality-BERT is a BERT-based end-to-end deep neural network that takes the original text as input and outputs the formality score. Instead of using different statistical lexical features, the two proposed methods use the sentence content and context to predict formality. We applied both Formality-LSTM and Formality-BERT on a public dataset that contains four genres of text and the results of these models outperform state-of-the-art results for all genres. Formality-BERT outperforms existing models by 14 points on the Spearman correlation between predicted formality and human-labeled formality for four genres.
Detecting sepsis from photoplethysmography: strategies for dataset preparation

Sepsis is one of the most frequent causes of death in Intensive Care Units, and its prognosis greatly depend on timeliness of diagnosis. MIMIC-III database is a frequent source of data for developing method for automatic sepsis detection. However, the heterogeneity of data jeopardize the feasibility of the task. In this work we propose a selection strategy for generating high quality data suitable for training a sepsis detection system based on the utilization of only plethysmographic data. Clinical relevance A system for detecting sepsis based only on PPG may be potentially at virtually no cost in any case clinicians suspect the possibility of developing sepsis.
Spatio-Temporal Causal Transformer for Multi-Grained Surgical Phase Recognition

Automatic surgical phase recognition plays a key role in surgical workflow analysis and overall optimization in clinical work. In the complicated surgical procedures, similar inter-class appearance and drastic variability in phase duration make this still a challenging task. In this paper, a spatio-temporal transformer is proposed for online surgical phase recognition with different granularity. To extract rich spatial information, a spatial transformer is used to model global spatial dependencies of each time index. To overcome the variability in phase duration, a temporal transformer captures the multi-scale temporal context of different time indexes with a dual pyramid pattern. Our method is thoroughly validated on the public Cholec80 dataset with 7 coarse-grained phases and the CATARACTS2020 dataset with 19 fine-grained phases, outperforming state-of-the-art approaches with 91.4% and 84.2% accuracy, taking only 24.5M parameters.
Fabrication and characterization of Spin on Glass/HR-silicon Substrates for mmWave Applications

This paper proposes a coaxial line structure for mmWave integrated packaging. The structure can be fabricated to reduce the interconnect length. The simulation results show that this coaxial structure has a lower loss at 40 GHz, which is 0.6dB/mm lower than the simple vertical via loss. The return loss of the whole structure is about 20dB. This paper also proposes a three-dimensional integration process based on SOG materials, which can simplify the process steps and has good process stability. Coaxial structures with a minimum pitch of 20 μm were successfully fabricated.
Automatic myocardium strain quantification in MR synthetic images with Deep Leaning

Accurate quantification of myocardium strain in magnetic resonance images is important to correctly diagnose and monitor cardiac diseases. Currently, available methods to estimate motion are based on tracking brightness pattern differences between images. In cine-MR images, the myocardium interior presents an inhered homogeneity, which reduces the accuracy in estimated motion, and consequently strain. Neural networks have recently been shown to be an important tool for a variety of applications, including motion estimation. In this work, we investigate the feasibility of quantifying myocardium strain in cardiac resonance synthetic images using motion generated by a compact and powerful network called Pyramid, Warping, and Cost Volume (PWC). Using the motion generated by the neural network, the radial myocardium strain obtained presents a mean average error of 12.30% +- 6.50%, and in the circumferential direction 1.20% +-0.61 %, better than the two classical methods evaluated. Clinical Relevance— This work demonstrates the feasibility of estimating myocardium strain using motion estimated by a convolutional neural network
Design of a Haptic Palmar Device with Thumb Flexion and Circumduction Movements for Sensorimotor Stroke Rehabilitation

To address the clinical need for high-intensity, repetitive sensorimotor hand training after stroke, we developed in a first step a novel haptic device for practicing finger movements. Because the thumb plays a fundamental role in the loss of autonomy and prehensile functions after stroke, we present here the development of a thumb module that complements our previous design. The novelties of our device are that it reduces the complexity to a minimum from a user perspective while still allowing anatomical thumb flexion/extension and circumduction movements with a highly functional range of motion. Moreover, it enables sensorimotor training thanks to its backlash-free and backdrivable actuation that allows for high-quality haptic rendering. Our device was co-created together with clinicians to incorporate clinical and anatomical requirements, and therefore, maximize its clinical relevance.