Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Synthetic Training Data for Deep Learning-Based GBM Segmentation

In this work, fully automatic binary segmentation of GBMs (glioblastoma multiforme) in 2D magnetic resonance images is presented using a convolutional neural network trained exclusively on synthetic data. The precise segmentation of brain tumors is one of the most complex and challenging tasks in clinical practice and is usually done manually by radiologists or physicians. However, manual delineations are time-consuming, subjective and in general not reproducible. Hence, more advanced automated segmentation techniques are in great demand. After deep learning methods already successfully demonstrated their practical usefulness in other domains, they are now also attracting increasing interest in the field of medical image processing. Using fully convolutional neural networks for medical image segmentation provides considerable advantages, as it is a reliable, fast and objective technique. In the medical domain, however, only a very limited amount of data is available in the majority of cases, due to privacy issues among other things. Nevertheless, a sufficiently large training data set with ground truth annotations is required to successfully train a deep segmentation network. Therefore, a semi-automatic method for generating synthetic GBM data and the corresponding ground truth was utilized in this work. A U-Net-based segmentation network was then trained solely on this synthetically generated data set. Finally, the segmentation performance of the model was evaluated using real magnetic resonance images of GBMs.
Comparisons of Machine Learning Algorithms for Driving Behavior Recognition Using In-Vehicle CAN Bus Data

Driving behavior recognition is an active research topic as it has many potential applications, such as fleet management, vehicle anti-theft, and planning of car insurance policies. Nowadays, the most successful approaches to driving behavior recognition are based on machine learning algorithms. Each machine learning algorithm has its pros and cons, and no single algorithm fits all problems. Therefore, how to determine an appropriate algorithm suitable for discovering driving patterns is a critical step in driving behavior recognition. This paper aims to conduct an empirical study for driving behavior recognition and evaluate the recognition performance of popular machine-learning algorithms. The experimental results showed that many sensor values gathered from the CAN bus are either highly correlated with one another or less important attributed to driving behavior identification. Among traditional machine learning approaches, ensemble tree-based algorithms, such as Random Forests and Decision Trees have better performance when compared with other approaches.
An Intelligent Parallel Algorithm for Online Virtual Network Embedding

Network virtualization is ubiquitously an essential attribute to enable the success of the future virtualized networks (e.g. forthcoming 5G network, smart Internet of Things (IoT)). Virtual Network Embedding (VNE) is the main challenge in network virtualization that allows multiple heterogeneous Virtual Networks (VNs) to simultaneously coexist on top of a shared substrate infrastructure. Many VNE algorithms have been proposed over past decades but most of them are merely focusing on VNE node mapping and leaving link mapping task for the popular k-shortest path algorithms or multi-commodity flow (MCF) mechanism. In this paper, we propose an intelligent VNE orchestration for link mapping stage which exploits distributed parallelism to considerably reduce the processing time with high efficiency. Extensive simulations have shown that our proposed algorithm outperforms the most popular VNE algorithms.
Deep Learning Classification Models Built with Two-step Transfer Learning for Age Related Macular Degeneration Diagnosis

The objective of this study was to build deep learning models with optical coherence tomography (OCT) images to classify normal and age related macular degeneration (AMD), AMD with fluid, and AMD without any fluid. In this study, 185 normal OCT images from 49 normal subjects, 535 OCT images of AMD with fluid, and 514 OCT mages of AMD without fluid from 120 AMD eyes as training data, while 49 normal images from 25 normal eyes, 188 AMD OCT images with fluid and 154 AMD images without any fluid from 77 AMD eyes as test data, were enrolled. Data augmentation was applied to increase the number of images to build deep learning models. Totally, two classification models were built in two steps. In the first step, a VGG16 model pre-trained on ImageNet dataset was transfer learned to classify normal and AMD, including AMD with fluid and/or without any fluid. Then, in the second step, the fine-tuned model in the first step was transfer learned again to distinguish the images of AMD with fluid from the ones without any fluid. With the first model, normal and AMD OCT images were classified with 0.999 area under receiver operating characteristic curve (AUC), and 99.2% accuracy. With the second model, AMD with the presence of any fluid, and AMD without fluid were classified with 0.992 AUC, and 95.1% accuracy. Compared with a transfer learned VGG16 model pre-trained on ImageNet dataset, to classify the three categories directly, higher classification performance was achieved with our notable approach. Conclusively, two classification models for AMD clinical practice were built with high classification performance, and these models should help improve the early diagnosis and treatment for AMD.
Potential distribution on the stress grading system of high voltage rotating machines — Part II transient measurements and simulation profiles

The authors developed a system that utilizes a Pockels sensor to measure the potential distribution along the stress grading (SG) system of a form wound coil. Potential distributions on a bar coil under a 50 Hz sinusoidal wave, 1 kHz rectangular wave, 1 kHz repetitive impulse, and two level pulse width modulation (PWM) voltage were measured using this system. The results are discussed using an equivalent circuit of the bar coil, which consists of a distributed constant type RC circuit. Calculated potential distributions with this model were quantitatively consistent with the measured results. When the penetration length of the high frequency component of the applied voltage was shorter than the length of the corona armor tape (CAT), a high-potential gradient developed in the CAT while the low-frequency component transmitted to the stress grading tape (SGT) region. In addition, the electric field distribution and power dissipation in the SG system were computed based on the finite element method (FEM). It was evident from the computed results that a frequency component of more than 200 kHz had little influence on the power dissipation in the SGT in this system.
Geometry optimization of helical swimming at low Reynolds number

This paper aims to improve magnetic helical swimming varying the cross-section geometry and considering a conical geometry for helical robot. Both parameters have not been extensively studied in the literature. The conical helix is generated when the helix radial pitch varies constantly and due to that, it could be used in delivery tasks of big-sized millimetric objects. To conceive the whole study, experimentation, and finite-element-method (FEM) simulations are performed in order to corroborate the experimental data in the literature. The influence on the propulsion of the helix cross-section geometry in a cylindrical helix, and the number of turns in a conical helix was demonstrated. Results show that helices with triangular cross-sections provide better propulsion than circular ones, these latter widely used in various micro-and nanoswimmer designs in literature. This result could change the way that helical swimmers are manufactured today. Besides, conical helices with 1-turn and 1.5-turn have shown better performances than multi-turn ones in terms of speed and propulsion force. From this part of the study, the main contribution has been the novel design proposed for delivery tasks.
Interval-valued pre-aggregation functions: a study of directional monotonicity of interval-valued functions

Pre-aggregation functions have extended the framework of valid functions to fuse data with respect to aggregation functions, in the sense that the monotonicity requirements are relaxed. Moreover, this class of functions has proven to be useful in classification problems. In this work we present the concept of directional monotonicity for interval-valued functions and use it to define the concept of interval-valued pre-aggregation functions. Additionally, we present some relevant properties of directionally monotone interval-valued functions and a method to construct interval-valued pre-aggregation functions from standard pre-aggregation functions.
Cuff-less Blood Pressure Measurement Based on Deep Convolutional Neural Network

Cuff-less blood pressure (BP) monitoring is increasingly being needed for cardiovascular events management in clinical. Many of the existing methods, however, are based on manual feature extraction, which cannot characterize the complex relationship between the physiological signals and BP. In this study, the 16-layer VGGNet was used to construct cuff-less BP from electrocardiogram (ECG) and pressure pulse wave (PPW) signals, with no need extract features from raw signals. The deep network architecture has the ability of automatic feature learning, and the learned features are the higher-level abstract description of low-level raw physiological signals. Eight-nine middle-aged and elderly subjects were enrolled to evaluate the performance of the proposed BP estimation method, with oscillometric technique-based BP as a reference. Experimental results indicate that the proposed method had a commendable accuracy in BP estimation, with a correlation coefficient of 0.91 and an estimation error of -2.06 ± 6.89 mmHg for systolic BP, and 0.89 and -4.66 ± 4.91 mmHg for diastolic BP. This study shows that the proposed method provided a potential novel insight for the cuff-less BP estimation.
Ultrasound-Based Regularized Log Spectral Difference Method For Monitoring Microwave Hyperthermia

The feasibility of using normalized cumulative difference attenuation (NCDA) map for tracking the spatial and temporal evolution of temperature during microwave hyperthermia experiment on in-vitro phantoms is explored in this study. The NCDA maps were estimated from the beamformed ultrasound radio frequency (RF) data using a regularized log spectral difference (RLSD) technique. The NCDA maps were estimated at different time instants for the entire period of the experiment. The contour maps of the NCDA and the ground truth temperature map, obtained using an infra-red(IR) thermal camera corresponding to the ultrasound imaging plane, showed that NCDA was able to locate the axial and lateral co-ordinates of the hotspot with the error of < 1.5 mm axially and < 0.1 mm laterally. The error in the estimated hotspot area was less than 8 %. This preliminary in-vitro study suggests that NCDA maps estimated using RLSD may have potential in evaluating the spatio-temporal evolution of temperature and may help in the development of ultrasound-based image-guided temperature monitoring system for microwave hyperthermia.
Diagnosis of Obstructive Sleep Apnea during Wakefulness Using Upper Airway Negative Pressure and Machine Learning

Background and Rational: Obstructive Sleep Apnea (OSA) is a common disorder, affecting almost 10% of adults, but very underdiagnosed. This is largely due to limited access to overnight sleep testing using polysomnography (PSG). Our goal was to distinguish OSA from healthy individual using a simple maneuver during wakefulness in combination with machine learning methods. Methods: Participants have undergone an overnight PSG to determine their ground truth OSA severity. Separately, they were asked to breathe through a nasal mask or a mouth piece through which negative pressure (NP) was applied, during wakefulness. Airflow waveforms were acquired and several features were extracted and used to train various classifiers to predict OSA. Results and Discussion: The performance of each classifier and experimental setup was calculated. The best results were obtained using Random Forest classifier for distinguishing OSA from healthy individuals with a very good area under the curve of 0.80. To the best of our knowledge, this is the first study to deploy machine learning and NP with promising path to diagnose OSA during wakefulness.
Effect of EKG Sampling Rate on Heart Rate Variability Analysis

We studied the effect of EKG sampling rate on heart rate variability (HRV) analysis. We acquired EKG from four term hypoxic-ischemic encephalopathic infants undergoing therapeutic hypothermia. The EKG signal was acquired continuously for 4 days from the cardiorespiratory monitor through the analog port. The following HRV metrics were calculated: normalized low-frequency (nLF), normalized high-frequency (nHF), low-frequency (LF), high-frequency (HF), short-term detrended fluctuation analysis (DFA) exponent (αs), long-term DFA exponent (αL), root mean square (RMS) short (RMSS), and RMS long (RMSL). In addition, heart rate was used. These metrics were calculated for EKG acquired at 1 KHz (served as reference, EKGref) as well as from EKGs downsampled at 500 Hz (EKG500), 250 Hz (EKG250), and 125 Hz (EKG125). The bedside monitors were simultaneously sending the EKG to a data warehouse, storing the EKG (EKGDWH) at 250 Hz. All HRV metrics were also calculated for the EKGDWH. The comparison between HRV metrics calculated from EKGref and downsampled EKG (EKG500, EKG250, EKG125) was made with intraclass correlation coefficient (r). The comparisons of HRV metrics between EKG250 and EKGDWH were also made with ICC. Our results show that HRV calculated with EKGref and from downsampled EKG were highly correlated (r>0.8 for all comparisons, P<0.001). HRV metrics from EKG250 and EKGDWH were also significantly correlated (r=0.7, P<0.001) for all metrics except for HF (r=0.276). These data show that HF power is compromised in the EKGDWH signal and caution must be exercised in interpreting the HF power calculated from this EKG.
Epileptic State Classification based on Intrinsic Mode Function and Wavelet Packet Decomposition

The scalp electroencephalogram (EEG) signal based epileptic seizure analysis has been comprehensively studied in the past. But existing researches are generally concerned with the seizure/non-seizure detection. Few attention has been paid to the epileptic preictal state classification, which is found to be evidently more important to the injury prevention. In this paper, we study the epileptic preictal state classification for seizure prediction. The one hour preictal EEG signal is divided into non-overlapped equilong segments. Statistical features of the first intrinsic mode function (FIMF) of the empirical mode decomposition (EMD) of the EEG signal as well as the 4-level wavelet packet decomposition (WPD) of the FIMF are extracted for the EEG signal representation. A five-state classification problem is formulated, including one interictal, three preictal, and one seizure states. Experiments on the benchmark epilepsy EEG database collected by the Children’s Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) using several popular classifiers are provided for the effectiveness demonstration.
A Closed-Loop Method to Identify EMG-Torque Dynamics in Human Balance Control

Human balance control requires continuous modulation of ankle torque by central and spinal activation of the ankle muscles combined with the intrinsic mechanical stiffness of the joint. These components appear together and cannot be measured separately. This work presents a novel multiple-input, single-output, closed-loop identification method that decomposes the ankle torque in human balance control into its central, stretch reflex, and intrinsic components. The method models separate transfer functions for each EMG-torque relation for central and stretch reflex mechanisms and estimates the ankle intrinsic torque from the residuals. The method uses only EMG measurements, requires no kinematic data, and has few parameters, resulting in robust performance. Application of the method to perturbed standing data from two healthy subjects demonstrated that the central and stretch reflex torques accounted for 80-93% of the ankle torque variation, while the intrinsic stiffness was responsible for most of the remaining torque.
Improved Segmentation with Dynamic Threshold Adjustment for Phonocardiography Recordings

In this study with the intent to segment phonocardiography (PCG) recordings, an algorithm that processes a feature vector obtained by jointly using wavelet transform and mel scaled energy spectrum of the PCG signal is proposed. The feature vector is processed by a peak detection algorithm which results in a set of peaks that meet some certain criteria and will be exploited in the succeeding stages of the algorithm. Heart sounds are labeled by convolving circularly a template and a fragment of the feature vector which is picked up with the guidance of the peaks. At the final stage, the algorithm tries to detect and correct erroneous labels. The performance of the algorithm was tested on both normal heart sounds and abnormal heart sounds; 80 records in total. As a result of these tests, for normal heart sounds S1 and S2 sounds were detected with 99,51% recall and 97,28% precision, while with 97,59% recall and 92,53% precision for abnormal heart sounds.
Real-Time Information Security Incident Management: A Case Study Using the IS-CHEC Technique

Information security recognised the human as the weakest link. Despite numerous international or sector-specific standards and frameworks, the information security community has not yet adopted formal mechanisms to manage human errors that cause information security breaches. Such techniques have been however established within the safety field where human reliability analysis (HRA) techniques are widely applied. In previous work we developed Information Security Core Human Error Causes (IS-CHEC) to fill this gap. This case study presents empirical research that uses IS-CHEC over a 12 month period within two participating public and private sector organisations in order to observe and understand how the implementation of the IS-CHEC information security HRA technique affected the respective organisations. The application of the IS-CHEC technique enabled the proportions of human error related information security incidents to be understood as well as the underlying causes of these incidents. The study captured the details of the incidents in terms of the most common underlying causes, selection of remedial and preventative measures, volumes of reported information security incidents, proportions of human error, common tasks undertaken at the time the incident occurred, as well as the perceptions of key individuals within the participating organisations through semi-structured interviews. The study confirmed in both cases that the vast majority of reported information security incidents relate to human error, and although the volumes of human error related incidents pertaining to both participating organisations fluctuated over the 12 month period, the proportions of human error remained consistently as the majority root cause.
Deep Learning-Based Radiomics Models for Early Recurrence Prediction of Hepatocellular Carcinoma with Multi-phase CT Images and Clinical Data

Hepatocellular carcinoma (HCC) is the fifth most common malignancy in the world and the second most common cause of cancer-related death. By surgically removing hepatocellular carcinoma, the patients may have the early recurrence within one year. Recently, CT radiomics signatures have been demonstrated to be effective on predicting early recurrence of HCC. However, the radiomics signatures are based on hand-crafted low-level features, such as density and texture. In this paper, we propose a deep learning-based radiomics approach for predicting early recurrence of hepatocellular carcinoma with multi-phase computed tomography (CT) images. We also propose several models to combine the high-level radiomics features with clinical data to improve the prediction accuracy. The area under the curve (AUC) of receiver operating characteristic curve (ROC) is 0.825.
An Underactuated, Tendon-Driven, Wearable Exo-Glove With a Four-Output Differential Mechanism

Soft, underactuated, and wearable robotic exo-gloves have received an increased interest over the last years. These devices can be used to improve the capabilities of healthy individuals or to assist people that suffer from neurological and musculoskeletal diseases. Despite the significant progress in the field, most existing solutions are still heavy and expensive, they require an external power source to operate, and they are not wearable. In this paper, we focus on the development of an affordable, underactuated, tendon-driven, wearable exo-glove equipped with a novel four-output differential mechanism that provides grasping capabilities enhancement to the user. The device and the differential mechanism are experimentally tested and assessed using three different types of experiments: i) grasping tests that involve different everyday objects, ii) force exertion capability tests that assess the fingertip forces for different types of grasps, and iii) tendon tension tests that estimate the maximum tendon tension that can be obtained by employing the proposed differential. The device considerably improves the grasping capabilities of the user with a weight of 690 g and an operation autonomy of a whole day.
A Comparative Study of Features for Acoustic Cough Detection Using Deep Architectures*

Automatic cough detection is key to tracking the condition of patients suffering from tuberculosis. We evaluate various acoustic features for performing cough detection using deep architectures. As most previous studies have adopted features designed for speech recognition, we assess the suitability of these techniques as well as their respective extraction parameters. Short-time Fourier transform (STFT), mel-frequency cepstral coefficients (MFCC) and mel-scaled filter banks (MFB) were evaluated using deep neural networks, convolutional neural networks and long-short term models. We find experimentally that, by regarding each cough sound as a single input feature instead of multiple shorter features, better performance can be achieved. Longer analysis windows also provide enhancement in contrast to the classic 25 ms frame. Although MFCC performance is improved by sinusoidal liftering, STFT and MFB lead to better results. Using MFB and the optimum segment and frame lengths, an improvement exceeding 7% in the area under the receiver operating characteristic curve across all classifiers is achieved.