Miracle-dz/UniFAR-FTUs huggingface dataset:
Bone Conduction as Sensory Feedback Interface: A Preliminary Study

Non-invasive sensory feedback is a desirable goal for upper limb prostheses as well as in human robot interaction and other human machine interfaces. Yet many approaches have been studied, none has been broadly deployed in upper limb prostheses. Bone conduction has the potential to excite an effect known as osseoperception and therefore provides a novel sensory interface. This paper presents the preliminary results of our study into the temporal parameters of a sensory feedback interface utilizing vibrotactile stimulus onto the ulnar olecranon representing a non-invasive sensory feedback interface. Three different tests are performed to establish the characterizing parameters of the interface; perception threshold, sensation discrimination and reaction time. Our results are similar to the results obtained for invasive bone conduction. The perception threshold for lower frequencies is small and therefore allows using small transducers with low power consumption. The sensation discrimination shows comparable results as reported in existing literature as well as the reaction time for the amputee is within the same range.
Effect of Contraction Parameters on Swing Support During Walking Using Wireless Pneumatic Artificial Muscle Driver: A Preliminary Study

We developed wireless pneumatic artificial muscle (PAM) driver that can control delay before contraction starts, and duration of contraction through GUI of an application running on a smartphone. The purpose of this study is to evaluate the effect of these two parameters on the joint motion in the swing phase for realizing optimal swing support. The PAMs were placed above the left hip joint in order to support the left hip flexion in the swing phase. Pressure sensor was placed under the right heel for detecting the heel contact that triggers the activation of the PAMs. Subjects walked with the support under different contraction parameters. The experimental result clearly demonstrated that the hip flexion angle in the swing phase increased in all subjects, although there was individual difference in the optimal delay between heel contact and start of the PAM contraction. In addition, the variability of the hip flexion was decreased, while the hip flexion angular velocity and the peak value of the knee flexion angle were increased by the swing support for the hip joint under the optimal delay parameter. The results of this pilot study suggested that leg swing could be instantaneously modified by the support of PAMs and the optimal parameter for realizing the most effective support differed among subjects. Our wireless PAM driver enables the most effective walking support depending on the physical ability of the patient by its functionality to adjust the contraction parameters.
Excitation Comparison between Multi-site Stimulation using Network-based tDCS and Focal Stimulation using High-definition tDCS

Transcranial direct current stimulation (tDCS) has been developed rapidly in recent years and derived various forms which serve as therapeutic tools for neurological disorders. However, differences of stimulation effect among these stimulation strategies have not been fully explored. In this pilot study, we compared the stimulation effect between High-definition tDCS (HD-tDCS) and Network-based tDCS (NB-tDCS) on a healthy subject. HD-tDCS mainly stimulated primary motor cortex with 2mA current and NB-tDCS was designed to stimulate main motor-related areas including primary motor cortex, premotor cortex and supplementary motor area with the same amount of current in total. Cortico-muscular coherence (CMC) derived from EEG/EMG, which measures the synchrony between cortical neural activity and muscles, was collected from isometric wrist extension tasks. The results demonstrated more motor-related regions excited and longer high-level lasting effect after NB-tDCS compared with HD-tDCS, which indicated the potential power of multi-site stimulation. This study might provide some guidance and hints for transcranial stimulation strategies in the future.
Reliability of an Industrial Wireless Communication System using Approximate Units

In the last decade, approximate computing has gained a lot of research attention as one of the most promising energy-efficient computing paradigms. In the meantime, the wireless communication development driven by the ongoing paradigm shift of Industry 4.0 (I40) and Industrial Internet of Things (IIoT) has been growing enormously. This paper presents a use case study of the impact of approximate adders in industrial wireless communication. The results show the uncorrelation between the traditional metrics used to optimize approximate units and the actual impact at the system level.
Modeling Time-varying Trends in ERP Data with Applications to an Implicit Learning Paradigm in Autism

Author(s): Hasenstab, Kyle Andrew | Advisor(s): Senturk, Damla | Abstract: Event-related potential (ERP) studies are a subset of experimental frameworks within the field of electroencephalography (EEG) that focus on ERPs, the electrical potential outputted by a subject's brain when presented with an implicit task in the form of stimuli. Data comprise an ERP repetition observed for each stimulus across electrodes on the scalp, producing a complex data structure consisting of a functional, longitudinal and spatial dimension. In typical ERP studies, the dimension of data is reduced into a single measure for each subject by cross-sectionally averaging ERP across longitudinal and spatial repetitions. Features are then extracted from the averaged ERP and analyzed using simple statistical methods, ignoring additional information that may be found in the collapsed dimensions. In this dissertation, three types of methodology are proposed for preserving and analyzing the lost dimensions of ERP data. The first method, moving average processed ERP (MAP-ERP), is a two-step approach comprised of a meta-preprocessing step to preserve longitudinal information and a weighted mixed effects regression framework to allow modeling of the resulting meta-preprocessed data. The proposed robust functional clustering (RFC) algorithm identifies substructures in features of the longitudinal ERP processes while accounting for subject-level covariance heterogeneity induced by meta-preprocessing. Finally, the proposed multidimensional functional principal components analysis (MD-FPCA) utilizes a two-stage procedure to summarize important characteristics across all three dimensions of the ERP data structure into an interpretable, low-dimensional form. Proposed methods are applied to a study on neural correlates of visual implicit learning in young children with autism spectrum disorder (ASD). Applications of the proposed methods reveal meaningful trends and substructures in the implicit learning processes of ASD children when compared to typically developing controls. Results indicate proposed methodology effectively preserves important information contained within the multiple dimensions of ERP.
Model Predictive Control of an Electric Vehicle Motor Drive Integrated Battery Charger

This paper presents a multi-objective model predictive control (MPC) approach for a three-phase, split converter-fed switched reluctance motor (SRM) integrated battery charger for Electric Vehicle (EV) application. The proposed control is based on finite control set (FCS) model predictive control which is suitable for online prediction with a unit prediction horizon. With the help of a tailored multi-objective cost function, the proposed MPC achieves unity input power factor while also equalizing the batteries’ state of charge (SoC) at the end of the charging cycle regardless their initial SoC. The performance of the proposed MPC has been verified using MATLAB/Simulink.
Efficacy of News Sentiment for Stock Market Prediction

Stock Market trend prediction will always remain a challenging task due to stochastic nature. The enormous amount of data generated by the news, blogs, reviews, financial reports and social media are considered a treasure of knowledge for researchers and investors. The present work focuses to observe fluctuations in stock prices with respect to the relevant news articles of a company. In this paper, a daily prediction model is proposed using historical data and news articles to predict the Indian stock market movements. Classifier Naïve Bayes is used to categorize the news text having negative or positive sentiment. The count of the positive and negative sentiment of news articles for each day and variance of adjacent days close price along with historical data is used for prediction purpose and an accuracy ranging from 65.30 to 91.2 % achieved with various machine learning techniques.
Plant disease identification and classification using Back-Propagation Neural Network with Particle Swarm Optimization

Agriculture is the culture of land and rearing of plants to supply food to nourish and enhance life. Different types of plants are farmed every year based on environmental conditions and it is one of the main economic sources in India. These plants are prone to many diseases which hinders normal growth of the plants; these diseases are caused by seasonal changes, environmental variations, and cultivation procedures. To protect the plants from such damages, diseases need to be identified and properly diagnosed on time. Hence, innovation of feasible and powerful methods for identification and classification of plant diseases is an urgent need. There are lots of classifiers which are good in the classification of plant diseases: Back-propagation Neural Network (BPNN), Probabilistic Neural Network (PNN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) but only using these methods do not make the best tradeoff between time and accuracy. So to remove this constraint, in this paper we have given an image processing solution to distinguish and classify plant diseases efficiently and accurately. In our proposed method, for classification first, we use back-propagation algorithm to get the weights of neural network (NN) connections and then we optimize these weights using Particle Swarm Optimization (PSO) to come out of the problems like local optima and overfitting which are very common in conventional NN training methods. We have used images of leaves affected by different bacterial and fungal diseases: Alternaria Alternata, Anthracnose, Bacterial Blight and Cercospora Leaf Spot in our experiment and our proposed method achieves 96.2% accuracy.
Identification of midbrain dopamine neurons using features from spontaneous spike activity patterns*

Although dopamine neurons are considered essential in various brain functions, their specific roles are under debate, partially due to the difficulty to identify dopamine neurons among surrounding neurons deep in the brain based only on the extracellularly recorded electric activities. Thus, a handy method to identify dopamine and non-dopamine neurons based on the spontaneous activity patterns is desired. Here we tried to discriminate optogenetically-identified dopamine neurons from other types of neurons and obtained 86.0% success.
Detection of Music Preferences using Cerebral Blood Flow Signals

Music has been used for entertainment as well as for medical treatments that remedies physical conditions or psychiatric disorders. It is reported that better outcome can be expected when the music was selected along with personal preferences for these therapies. However it is difficult to find out personalized favorite music if the patient has difficulty in verbal communication due to ageing or developmental disorders. Therefore, this study aimed to develop an objective classification method of music preference based on cortical hemodynamic activities upon listening to the music clips. Fifteen healthy young adults listened their favorite and non-favorite music with their cortical activity measured with 38-channel functional near infrared spectroscopy (fNIRS). Eleven features were extracted from the time-courses of fNIRS signals from the left primary auditory area, the superior temporal gyrus, and the subcentral area. One- to four- dimensional features were individually selected using a leave-one-out cross-validation with two classification algorithms of Fisher linear discriminant analysis (LDA) and support vector machine. The best mean accuracy rate of 92.2 ± 1.7% was obtained when an LDA classifier with four features derived from oxy-hemoglobin signals were adopted showing that our proposed method is valid to classify individual music preference.
Power Spectrum and Cross Power Spectral Density Based EEG Correlates of Intensive Care Delirium

Full montage EEG recordings of 15 ICU patients (altogether 23 recordings) were analysed to find EEG correlates of delirium. CAM-ICU assessment results were used as the reference. Time period from 7 to 30 minutes at the beginning of the recordings was analysed in 10 sec segments with 5 sec overlap. Relative power in the conventional frequency bands of the EEG signal at 20 electrode locations with common reference was calculated. In the state of delirium the relative power of frequencies above 8 Hz was lower; this effect was predominantly observed at central and parietal lobes. Also, the absolute cross power spectral density of 20 – 30 Hz was significantly lower in delirium between almost any two electrode locations.
Near Infrared Spectrometric Investigations on the behaviour of Lactate

In patients with life-threatening illnesses, the metabolic production and disposal of lactate are impaired, which leads to a build-up of blood lactate. In critical care units, the changes in lactate levels are measured through intermittent, invasive, blood sampling and in vitro assay. Continuous monitoring is lacking, yet such monitoring could allow early assessment of severity and prognosis to guide therapy. Currently, there is no routine means to measure lactate levels continuously, particularly non-invasively. The motivation of this study was to understand the interaction of lactate with light in the Near Infra Red (NIR) region of the electromagnetic spectrum. This was to create an opportunity to explore the possibility of a non-invasive sensing technology to monitor lactate continuously.In vitro studies were performed using solution samples with varying concentration levels of sodium lactate in isotonic Phosphate Buffer Solution (PBS) at constant pH (7.4). These samples were prepared using stoichiometric solution compositions and spectra for each sample were taken using a state-of-the-art spectrometer in the NIR region. The spectra were then analysed qualitatively by 2D correlation analysis, which identified the regions of interest. Further analysis of these regions using linear regression at four randomly selected wavelengths showed bathochromic shifts, which, moreover, showed systematic variation correlating with lactate concentration.
Relationship between Dynamics of Physiological Signals and Subjective Quality of Life and Its Lifestyle Dependency

In terms of preventive healthcare, how adopted lifestyle maintains healthy states or leads to pathological states is an essential issue. In this study, for the healthy subjects, their daily patterns of heart rate variability (HRV) and behavioral activity dynamics were analyzed in association with subjective quality of life (QOL), and how the relationship between the physiological signal dynamics and QOL is differentiated by the chronotype (sleep-wake pattern) was investigated. As a result, the morning type subjects were found to show higher sequential variation in R-R interval (rMSSD) and behavioral activity, and to have higher sleep quality and non-depressive tendency. Even the evening type subjects could have higher sleep quality under the condition that rMSSD shows a dip around early afternoon and keep a higher level of activity during the daytime, which may be due to realization of steady entrainment to the circadian time cues. Through this study, fundamental knowledge for preventive healthcare was obtained for us to help people’s choice of an appropriate lifestyle for their health.
Skin Lesion Segmentation Based on Improved U-net

Melanoma is one of the most common and dangerous skin cancers, accounting for 75% of deaths associated with skin cancer. Detection of melanoma in early stages can significantly improve the survival rate. Automatic segmentation of melanoma is an important and essential step for accurate detection of melanoma. Many existing works based on traditional segmentation methods and deep learning methods have been proposed for high-resolution dermoscopy images. However, due to the intrinsic visual complexity and ambiguity among different skin conditions, automatic melanoma segmentation is still a challenging task for existing methods. Among these methods, the deep learning methods have obtained more attention recently due to its high performance by training an end-to-end framework, which needs no human interaction. U-net is a very popular deep learning model for medical image segmentation. In this paper, we propose an efficient skin lesion segmentation based on improved U-net model. Experiments conducted on the 2017 ISIC Challenge dataset towards melanoma detection shows that the proposed method can obtain state-of-the-art performance on skin lesion segmentation task.
IoT & Big Data in Smart Healthcare (ECG Monitoring)

Healthcare (Cardiovascular diseases) has now become a major concern in the economic as well social regiment of almost everyone. Apparently, the monetary implications of the clinics and hospitals are recent apprehensions globally. Also, the increasing world population is leading to the occurrence of various chronic heart diseases. Consequently, more people fall ill, hospitals are not vacant hence increasing the death rate. Consequently, a medical system which includes point-of-care is the need of the hour. The medical system should be able to handle long-term and continuous monitoring, inconspicuous monitoring and should be highly sensitive to the patient’s ECG signal. Therefore, to resolve this matter, we can club together the concept of Nanoelectronics, Internet of Things (IoT) and Big Data. Essentially, the nanomaterial enabled ECG sensors/devices have certainly improved conductivity, electrical properties, the toxicity of the devices. It has also reduced the cost, it is easily and abundantly available, and can be effortlessly integrated with the latest technological devices and concepts like IoT and Big Data. IoT helps the ECG signals to get transmitted through the sensor via a gateway using communication protocols like Bluetooth, Zigbee, 3G/4G, Wi-Fi, LAN, etc. Then this data can be sent to numerous places like the doctor’s end, caregiver or the cloud-storage for analysis or processing, etc. The doctor at the remote location can view the patient’s report on various smart devices, all thanks to the computing technology. This helps in dealing with the emergencies. Leading on to this, Big Data plays a pivotal role in this system as it provides with data analysis, decision-making, extracting useful information via algorithms, intelligent storage etc. The combination of these three technologies will make the world a more secure place to live.
Regression-based analysis of front crawl swimming using upper-arm mounted accelerometers

Wearable accelerometers can be used to quantify movement during swimming, enabling objective performance analysis. This study examined arm acceleration during front crawl swimming, and investigated how accelerometer-derived features change with lap times. Thirteen participants swam eight 50m laps using front crawl with a tri-axial accelerometer attached to each upper arm. Data were segmented into individual laps; lap times estimated and individual strokes extracted. Stroke times, root mean squared (RMS) acceleration, RMS jerk and spectral edge frequencies (SEF) were calculated for each stroke. Movement symmetry was assessed as the ratio of the minimum to maximum feature value for left and right arms. A regularized multivariate regression model was developed to estimate lap time using a subset of the accelerometer-derived features. Mean lap time was 56.99±11.99s. Fifteen of the 42 derived features were significantly correlated with lap time. The regression model included 5 features (stroke count, mean SEF of the X and Z axes, stroke count symmetry, and the coefficient of variation of stroke time symmetry) and estimated 50m lap time with a correlation coefficient of 0.86, and a cross-validated RMS error of 6.38s. The accelerometer-derived features and developed regression model may provide a useful tool to quantitatively evaluate swimming performance.
Generic Dry-Contact Ear-EEG

Generic dry-contact ear-EEG allows for discreet, user-friendly, unobtrusive, cost-effective and convenient recordings of EEG in real-life settings. In this study we introduce a new generic earpiece design with larger internal ear electrode distances, resulting in an increased spatial coverage compared to previous generic earpiece designs. The signal quality of ear-Fpz, within-ear (the measuring and reference electrode located in the same ear) and cross-ear (the measuring electrodes located in one ear and the reference electrode in the opposite ear) electrode configurations of the developed generic earpiece was evaluated with auditory steady-state responses (ASSR) and compared to dry-contact cEEGrid. Ten subjects with different ear sizes were included. The recordings were performed in a sleep setup, where the subjects were lying on a bed and the effect of sleeping position (back vs. sides) was investigated. We found that the generic earpiece attained statistically significant ASSRs with ear-Fpz, within-ear and cross-ear electrode configurations. However, the dry-contact cEEGrid achieved significantly higher average ASSR signal-to-noise ratio (SNR) compared to the generic earpiece. Additionally, this study showed no significant difference between back and side positions for the ear-EEG.
Techniques for Real-World Implementation of a MANET

To fulfill the demands of present scenario cellular or Ad-Hoc communication is desired. Mobile ad-hoc networks (MANETs) are composed of wireless motile nodes, which actively interchange data among themselves in the absence of a permanent set-up. A Test-bed is a structure which supports testing, contrasting % computing designs and protocols in the physical-world. Some essential requirements of MANETs, like power utilization or radio propagation, are naturally difficult to frame correctly in Simulators. One of the challenging issues in this field of investigation is to prove the truth and provide a clarification which is beneficial. This paper presents the review cum analysis of various Test-bed set up’s along with their associated features, with the help of basic analytical determination, design and conventions for Ad-Hoc networks and real-world experiments. In addition, various evaluation techniques viz. simulation % emulation have been discussed for MANETs. As per our study % analysis, we can conclude that, still research is going on for establishing an absolutely successful test-bed architecture, which fulfills all the desired characteristics of mobile ad-hoc networks.
Comparison and Analyzation of Different Feature Parameters for Alzheimer’s disease Identification

In this paper, we compare the performance of the derived anatomical features and the extracted feature parameters in Alzheimer’s disease (AD) identification. The correlation relationship between them and clinical mini-mental state examination (MMSE) score is analyzed. Based on these feature parameters, the highly correlated combined feature vectors are built and used as variables for the presented modified elastic net (EN) classifier. Experimental results show that the extracted feature parameters can obtain similar identification performance with the cortical thickness and the volume of gray matter (GM) in AD identification. The highly correlated combined feature vectors show the best identification performance among all of feature parameters using the modified EN-based classifier.