Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Dickson Switched-Capacitor Converter With Wide Conversion Ratio in 65-nm CMOS

Hybrid switched-capacitor (SC) converters have shown great promise in achieving high efficiency and power density for dc–dc conversion. By combining an SC stage with an inductor filter stage, benefits from both approaches can be realized. In this work, a hybrid Dickson SC converter with a 4:1 native conversion ratio and regulated output voltage range of 0.3–0.9 V from a 3.4 to 4.2-V lithium-ion battery and the effective switching frequency of 1 MHz was implemented in 65-nm bulk CMOS process. The converter achieves maximum output current of 1.5 A, the power density of 330 mW/mm2, and a peak efficiency of 92.6%. The converter is packaged using flip-chip technology with passive devices co-packaged through a high-density interposer to minimize the packaging parasitics and volume. A segmented gate driver is used to enhance the converter efficiency and reliability by maintaining low-voltage ringing across the power switches. The converter is integrated with closed-loop output voltage regulation, dead-time control, and active capacitor–voltage balancing to maximize the active and passive device utilizations.
A Deep Learning Approach for Street Pothole Detection

Potholes are a structural damage to the road with hollow which can cause severe traffic accidents and impact road efficiency. In this paper, we propose an efficient pothole detection system using deep learning algorithms which can detect potholes on the road automatically. Four models are trained and tested with preprocessed dataset, including YOLO V3, SSD, HOG with SVM and Faster R-CNN. In the phase one, initial images with potholes and non-potholes are collected and labeled. In the phase two, the four models are trained and tested for the accuracy and loss comparison with the processed image dataset. Finally, the accuracy and performance of all four models are analyzed. The experimental results show that the YOLO V3 model performs best for its faster and more reliable detection results.
Muscle fatigue evaluation with EMG and Acceleration data: a case study

The design of effective rehabilitation protocols relies on the ability to accurately assess the physical condition and the rehabilitative needs of the patient. Monitoring muscle fatigue can increase the usability of rehabilitative and restorative devices as it helps avoiding premature tiring and injury of patients whose resistance is already compromised. In this study, we collected EMG and accelerometer data from one healthy subject during a 30-minute walk on treadmill to determine the variations of muscle activation, and gait acceleration patterns, which, however subtle, could be interpreted as early indicators of muscle fatigue. Results show an increasing Tibialis Anterior (TA) and decreasing Soleus (SOL) and Gastrocnemius (GASL, GASM) activation towards the end of the task as compared to the beginning, as well as increasing acceleration peaks during the middle swing phase. By following the approach outlined here we can assess the efficiency and reduction of metabolic cost achieved by an exoskeleton. Furthermore, muscle fatigue may be linked to the efficacy of gait rehabilitation, where decreased muscle fatigue across sessions possibly indicates longer retention of benefits after training and increased walking capacity. This methodology can be used to benchmark novel exoskeletons, monitor fatigue to avoid premature tiring of patients, and optimize rehabilitation therapies.
A Multi-view CNN with Novel Variance Layer for Motor Imagery Brain Computer Interface

Accurate and robust classification of Motor Imagery (MI) from Electroencephalography (EEG) signals is among the most challenging tasks in Brain-Computer Interface (BCI) field. To address this challenge, this paper proposes a novel, neuro-physiologically inspired convolutional neural network (CNN) named Filter-Bank Convolutional Network (FBCNet) for MI classification. Capturing neurophysiological signatures of MI, FBCNet first creates a multi-view representation of the data by bandpass-filtering the EEG into multiple frequency bands. Next, spatially discriminative patterns for each view are learned using a CNN layer. Finally, the temporal information is aggregated using a new variance layer and a fully connected layer classifies the resultant features into MI classes. We evaluate the performance of FBCNet on a publicly available dataset from Korea University for classification of left vs right hand MI in a subject-specific 10-fold cross-validation setting. Results show that FBCNet achieves more than 6.7% higher accuracy compared to other state-of-the-art deep learning architectures while requiring less than 1% of the learning parameters. We explain the higher classification accuracy achieved by FBCNet using feature visualization where we show the superiority of FBCNet in learning interpretable and highly generalizable discriminative features. We provide the source code of FBCNet for reproducibility of results.
Challenging the Limitations of Atrial Fibrillation Detection in the Presence of Other Cardiac Arrythmias

Atrial fibrillation (AF) affects millions of people worldwide and needs to be diagnosed in its early stage to provide proper treatment. However, the numerous wearable devices available today are not yet able to discriminate AF episodes from other cardiac arrhythmias and merely detect normal vs abnormal rhythms.In this study we investigated the performance of a traditional classifier – designed to distinguish AF and sinus rhythm (SR) using inter-beat intervals (IBI) – when confronted with other – non-AF – arrhythmias. This classifier was challenged with data of 37 patients wearing an optical heart rate monitor device during catheter ablation procedures. We first analyzed the classification performance of pure AF vs SR and then gradually introduced non-AF arrhythmias in the time windows used for classification.We obtained a high classification performance (accuracy, sensitivity and specificity of 0.979, 1.000 and 0.966) for purely AF and SR. In contrast, when increasing the maximal possible number of non-AF arrhythmias to 50%, the performance decreased to an accuracy, sensitivity and specificity of 0.886, 0.998 and 0.853. While sinus tachycardia led to false positives the classification was not impaired by the presence of extrasystoles, bigeminy, bradycardia, frequent ectopic beats or atrial flutter.Our study quantifies to what extent a traditional IBI-based classifier is not sufficient to distinguish AF from other arrhythmias. Future work should concentrate on acquiring datasets with a high diversity of arrhythmias and employing new classification features.
Thermography for the detection of Secondary Raynaud's Phenomenon by means of the Distal-Dorsal Distance

Raynaud's phenomenon (RP) is a disease characterized by a transient ischemic process, in an exaggerated vascular response to cold or emotional stress. Thermography is a resource applied to support diagnosis of changes in the circulatory system. The aim of the study was to use the DistalDorsal Thermography Difference (DDD) in thermographic images to assess thermal behavior in individuals with secondary RP. The research was carried out in the period between 2018 and 2019. The sample means of the Distal-consisted of 44 individuals in a control group (Control) and 44 individuals in a pathological group (RP2). The participants, after acclimatization, were submitted to the cold stress protocol. The protocol consisted of immersing hands in a container of water at a temperature of 15°C for 60 seconds. The acquisition of thermographic images was performed at the pre-test moment and at the 1st, 3rd, 5th, 7th, 10th and 15th minute. At each time, the DDD values (of all fingers - minimum, maximum and sum) between the groups were analyzed. For statistical analysis, the independent t test and Cohen's d test were used. Regarding the results, there was a difference in relation to the rate of temperature recovery between the groups. The first group showed a rate of reheating just after the first minute subsequent to the cold stress test, while the RP2 group was unable to recover the temperature over 15 minutes. DDD, regardless of the selected criterion, proved to be a valid index for verifying the temperature gradient in the study with individuals identified with secondary RP.
Interretinal Symmetry in Color Fundus Photographs

Symmetry can be defined as uniformity, equivalence or exact similarity of two parts divided along an axis. While our left and right eyes clearly have a high degree of external bilateral symmetry, it is less obvious to what degree they have internal bilateral symmetry. In this paper, we try to find approximate-bilateral symmetry in retina, one of the internal parts of our eye, which plays a vital role in our vision and also can be used as a powerful biometric. Contrary to previous works, we study interretinal symmetry from a biometric perspective. In other words, we study whether the left and right retinal symmetry is strong enough to reliably tell whether a pair of the left and right retinas belongs to a single person. For this, we focus on overall symmetry of the retinas rather than specific attributes such as length, area, thickness, or the number of blood vessels. We evaluate and analyse the performance of both human and neural network based bilateral retina verification on fundus photographs. By experimenting on a publicly available data set, we confirm interretinal symmetry.
Remote Heart Rate Estimation Based on 3D Facial Landmarks

In this paper, we propose a novel video-based remote heart rate (HR) estimation method based on 3D facial landmarks. The key contributions in our method are twofold: (i) We introduce 3D facial landmarks detection to the video-based HR estimation and (ii) we propose a novel face patch visibility check manner based on the face patch normal in the 3D space. We experimentally demonstrate that, compared with baseline methods using 2D facial landmarks, our proposed method using 3D facial landmarks improves the robustness of HR estimation to head rotations and partial face occlusion. We also demonstrate that our visibility check is effective for selecting sufficiently visible face patches, contributing to the improvement of HR estimation accuracy.
Formation and Evolution of Soliton in Two-Mode Fiber Laser

High-order mode fiber lasers are gathering the rising attentions from fundamental researches to practical applications due to their distinctive spatial and temporal properties. Here, we demonstrate a carbon nanotube mode-locked two-mode fiber laser and investigate the formation and evolution process of the soliton based on the time-stretch dispersive Fourier transform method. Stable single pulse and bound-state pulses are observed at pump powers of 32.0 and 41.3 mW, respectively. Attributing to the coexistence of fundamental mode and first group high-order modes, the optical spectrum exhibits slight intensity modulations and the autocorrelation trace shows additional small side lobes. Similar to that of single-mode fiber lasers, the buildup process of soliton includes the relaxation oscillation, beating behavior, and stable mode-locking state. However, owing to the interplay between two transverse modes in the two-mode fiber laser, the duration and fluctuation intensity of the relaxation oscillation are much larger than that of single-mode fiber lasers.
Design and Experimental Validation of Dynamic Model of Multi Robot System

In a multi-robot system, following the leader is a challenge since the robot must detect the leader first and then responding accordingly. So, in this paper, the motion control of a group of robots is achieved based on the dynamic model of the collective motion behavior of the aggregation of Artemia, a creature that follows a spotlight. Modeling the collective behavior of Artemia that follows the spotlight will be inspiring for modeling a collective of robots that achieves the same tasks. The model is based on the newton equation and its parameters will be calculated directly from the features of the multi-robot system. Several experiments will be implemented to check the behavior of the proposed system, which is divided into four experiments according to four trajectories, the straight line, circle, zigzag and complex path pattern. The V-rep software is used to derive and simulate the proposed design, also test its performance.
Potential of Ensemble Learning to Improve Tree-Based Classifiers for Landslide Susceptibility Mapping

Ensemble learning methods have been widely used due to their remarkable generalized performance, but their potential in landslide spatial prediction application is not fully studied. To take full advantage of ensemble learning techniques, the classification and regression tree classifier and four tree-based ensemble classifiers of random forest, extremely randomized tree, gradient boosting decision trees, and extreme gradient boosting decision trees are used in this study for landslide susceptibility assessment. Specifically, a stacking ensemble learning framework coupled with embedded feature selection is presented, consisting of multiple tree-based classifiers mentioned previously as base learners and logistic regression as a metalearner in a two-layer structure. In the study area of Yongxin, China, 364 historical landslide locations were first randomly partitioned into a ratio of 7/3 for training and testing the model. Then, a spatial database of 16 landslide causative factors was constructed for landslide prediction. Meanwhile, the relative importance of these factors were quantified by using the total number of feature splits and the average Gini index during the training process, and a novel embedded feature selection method was used in the base learner of the proposed framework to further improve the computational efficiency and predictive performance by allowing each base learner to obtain its own optimal subfeature space. Finally, different methods were assessed by using several evaluation criteria. Experimental results demonstrated that the proposed ensemble learning framework had the highest area under the curve value of 0.864, and it is more effective than the conventional tree-based classifiers and other ensemble learning methods.
VLSI architectures for Delay Multiply and Sum Beamforming in Ultrasound Medical Imaging

Ultrasound medical imaging systems typically follow standard delay and sum (DAS) beamforming at the reception for image reconstruction. In DAS, the echo signals that are returned to the transducer are aligned in time and summed to form the beamformed signal. To improve the image quality and the signal to noise ratio of DAS, a non-linear beamforming named delay multiply and sum (DMAS) has been proposed in the literature, where, the signals arriving at the transducer are aligned in time and are pairwise multiplied in all possible combinations before summation. This provides better coherence, a correlation-based data-driven apodization, and consequently result in better contrast and resolution. However, the computational complexity of DMAS is higher than DAS thus restricting its real-time implementation. This paper presents two novel VLSI architectures for the implementation of DMAS, whose complexity is independent of the number of transducer elements. The proposed architectures are implemented on xc7z010c1g400-1 FPGA and the results clearly show the channel independency of the proposed architectures.
Comprehensive Analysis of Side-Placed Metamaterials in Wireless Power Transfer System

In this article, we propose the side-placed negative-permeability metamaterial (NPM) and zero-permeability metamaterial (ZPM) in the wireless power transfer (WPT) system to simultaneously enhance the level of efficiency and meet the electromagnetic field (EMF) safety regulations. A theoretical analysis, simulation, and experiment are conducted to verify the feasibility of the proposed system model. Based on the principle of magneto-inductive waves (MIWs) and the shielding theory, the transmission characteristics of the WPT system without and with two NPM slabs, two ZPM slabs, and the combination of NPM and ZPM slabs are analyzed. The results demonstrate that the ZPM slabs and the combination of NPM and ZPM slabs have a high efficiency improvement when the transfer distance is located under 40 cm and exceeds 40 cm, respectively. Both methods can control the EMF leakage. Moreover, the side-placed metamaterial exhibits tolerance to the misalignment of the coil. Finally, comparative studies on the ZPM slab and aluminum are carried out. The experimental and simulation results show that the ZPM slab performs better than aluminum.
A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

Lung cancer is one of the malignant tumor diseases with the fastest increase in morbidity and mortality, which poses a great threat to human health. Low-Dose Computed Tomography (LDCT) screening has been proved as a practical technique for improving the accuracy of pulmonary nodule detection and classification at early cancer diagnosis, which helps to reduce mortality. Therefore, with the explosive growth of CT data, it is of great clinical significance to exploit an effective Computer-Aided Diagnosis (CAD) system for radiologists on automatic nodule analysis. In this article, a comprehensive review of the application and development of CAD systems is presented. The experimental benchmarks for nodule analysis are first described and summarized, covering public datasets of lung CT scans, commonly used evaluation metrics and various medical competitions. We then introduce the main structure of a CAD system and present some efficient methodologies. For the extensive use of Convolutional Neural Network (CNN) based methods in pulmonary nodule investigations recently, we summarized the advantages of CNNs over traditional image processing methods. Besides, we mainly select the CAD systems developed by state-of-the-art CNNs with excellent performance and analyze their objectives, algorithms as well as results. Finally, research trends, existing challenges, and future directions in this field are discussed.
Privacy-preserving Spatio-Textual Skylines Based on Location Aggregation

To achieve cost-savings, data owners outsource their spatio-textual query services to public cloud, which, however, may bring serious privacy issues. In this paper, we define and study the problem of privacy-preserving spatio-textual skylines in cloud environments. To address the problem, we first transform the locations and texts of each data object and query request into vectors and encrypt the vectors based on an vector-based encryption method to protect the data privacy. Exploiting the group of query locations to accelerate the query processing, we further present a location-aggregation-based query request generation method. Based on encrypted aggregated query requests, we present a corresponding query processing algorithm for privacy-preserving spatio-textual skylines. Finally, we present analysis to show the security guarantee of the proposed methods, and conduct thorough experiments on real datasets to show the performance of our algorithms.
Movement Related Cortical Potentials in Parkinson’s Disease Patients with Freezing of Gait*

Parkinson’s disease (PD) is a complex neurode-generative disorder that results into poverty of movements. Freezing of gait (FOG) is a common and severely incapacitating symptom of PD. However, the underlying neurophysiological mechanism of FOG is still unclear. Electroencephalogram (EEG) as a non-invasive brain signal measurement method has been repeatedly used to reveal the pathological reasons behind PD. Since PD affects movement abilities, one particular type of EEG pattern called Movement-related cortical potential (MRCP), which was shown to be related to the generation of movement intention, can be a useful tool in PD research. However, most MRCP studies in PD area focus on the upper-limb movement like finger movement, to the best knowledge of the authors, no study explored MRCP characteristics during lower extremity movement in PD patients, particularly PD patients with FOG. This paper intends to investigate the relationship between the characteristics of MRCP and FOG by comparing MRCP signals among three groups: healthy controls group, PD patients without FOG group, and PD patients with FOG group. These three groups of participants were recruited based on age-matching and gender proportion matching criteria. During the experiment, the Electromyography (EMG) from the tibialis anterior (TA) muscle and EEG were recorded when performing ankle dorsiflexion (AD) by the dominant foot. MRCP signals were extracted from EEG data based on the muscle activities in the TA muscle of the dominant foot. The results of this study show that, during lower limb activities, MRCP shares considerable similarities between PD patients and healthy participants. However, the amplitude of MRCP in PD patients (with and without FOG) is lower compared to the healthy participants. In addition, direct observations from the results show that MRCP from PD patients with FOG has higher amplitude but more variability in terms of amplitude and latency than those from PD patients without FOG.
Graph-Based Semi-Supervised Learning for Induction Motors Single- and Multi-Fault Diagnosis Using Stator Current Signal

Supervised learning has been commonly used for induction motor fault diagnosis, and requires large amount of labeled samples. However, labeling recorded data is expensive and challenging, while unlabeled samples are available abundantly and contain significant information about motor conditions. In this paper, a graph-based semi-supervised learning (GSSL) approach using both labeled and unlabeled data is proposed. Experimental data for two 0.25 HP induction motors under healthy and faulty conditions are used. Discrete Wavelet Transform (DWT) is employed to extract features from recorded stator current signals. Three GSSL algorithms (local and global consistency (LGC), Gaussian field and harmonic function (GFHF), and greedy-gradient max cut (GGMC)) are evaluated in this study, and GGMC shows superior performance over other two. They are also compared with a supervised learning algorithm, support vector machine (SVM). As induction motors often operate under variable loadings, curve fitting equations are developed based on experimental data to generate training data for untested motor loadings.
Fuzzy c-Means with Improved Particle Swarm Optimization

Fuzzy clustering algorithm has become a relevant research field of unsupervised learning due to that the uncertainties between patterns can be described more accurately. Based on objective function, fuzzy clustering algorithm uses a constrained optimization mathematical problem to represent the clustering problem. It then determines the division of data sets and fuzzy clustering results by solving the optimization problem. Fuzzy c-means (FCM) is one of the best known for its simplicity and efficiency. However, it shows some weaknesses, particularly its tendency to fall into local optima and dependence on initial values. Particle Swarm Optimization (PSO) is one of the heuristic methods that usually implemented on function optimization problems since it has a robust global search capability. In this paper, a new concept of worst position is introduced to PSO that gives a chance for particles to change flying directions. Moreover, new hybrid algorithms based on FCM and improved PSO with worst position (PSOWP) both in L1 norm and L2 norm are proposed, which avoid falling into local optimum with faster convergence speed.