Miracle-dz/UniFAR-FTUs huggingface dataset:
Micro Bio Processor Development Kit for Wireless Neural Implants

Micro Bio Processor version 1.5 (MBPv15) Development Kit is specially engineered to support various function-alities of implantable devices such as bio-signal sensing, neural stimulation, and dual-band wireless connectivity & charging. It provides a convenient way to evaluate the MBPv15 chip solution as a system component by a modular design of hardware and software. As a result, MBPv15 chip solution enables to develop wireless neural implants in a mm-scale form factor with ultra-low power consumption by achieving 1.6 mW for neural spike detection and 9.8 mW for neural stimulation, respectively.
A Monolayer Composite of h-BN Doped by a Nano Graphene Domain: As Sensitive Material for SO2 Gas Detection

The development of two-dimensional materials with gas-sensitive properties has been a research focus in the field of materials science. Considering the excellent properties of graphene and hexagonal boron nitride, a monolayer composite is put forward and its potential for gas sensing has been investigated by density functional theory. Compared to common gases in the atmosphere and three other toxic gases, sulfur dioxide tends to have a strong mutual effect with this composite. Together with electronic analyses, it is proved that the composite presents excellent sensitivity toward sulfur dioxide molecules and the conductivity can be used as a vital parameter to diagnose the status of gas sensor. What’s more, a co-adsorption model demonstrates a favorable performance in high moisture working environment. Thus, we can reasonably draw the conclusion that the presented composite could be an eligible candidate for sulfur dioxide gas detection.
Missing Mass of Markov Chains

Estimation of missing mass or the total probability of unseen letters in a random sample is an important problem with several applications. The Good-Turing (GT) estimator is one of the most popular estimators for missing mass. The bias of the GT estimator is known to fall inversely with the number of samples when they are independent and identically distributed. When the samples form a Markov chain, very little is known about the convergence of the GT estimator even though it is widely used for smoothing in language models that are mostly Markovian. In this work, we initiate and make progress towards characterizing bias of the GT estimator for missing mass in Markov chains. We develop a useful ‘multi-letter’ characterization of the bias, which leads to sufficient conditions on the transition probability matrix for convergence of the bias of the GT estimator to zero.
Differentiating between Parkinson’s disease patients and controls using variability in muscle activation during walking

Surface electromyography (sEMG) of the lower limb muscles has been proposed to evaluate motor dysfunctions in Parkinson’s disease (PD) patients. Variability in the sEMG could be used as an indicator of poor muscle coordination, but previous studies have reported conflicting results. This study has examined the variability of muscle using the coefficients of variance of Tibialis anterior (TA) and Medial gastrocnemius (MG) lower limb muscles for 24 PD, 24 age matched controls (CO), and 24 young controls (YC), during different phases of the gait cycle. The gait intervals were measured using the inertial measurement unit (IMU). We observed a statistically significant difference between PD and control for the variability of lower limb muscle when comparing the sub-phases of the gait. It was also found that the difference was more pronounced for the TA muscle.
RR interval analysis for the distinction between stress, physical activity and no activity using a portable ECG*

Methods based exclusively on heart rate hardly allow to differentiate between physical activity, stress, relaxation, and rest, that is why an additional sensor like activity/movement sensor added for detection and classification. The response of the heart to physical activity, stress, relaxation, and no activity can be very similar. In this study, we can observe the influence of induced stress and analyze which metrics could be considered for its detection. The changes in the Root Mean Square of the Successive Differences provide us with information about physiological changes. A set of measurements collecting the RR intervals was taken. The intervals are used as a parameter to distinguish four different stages. Parameters like skin conductivity or skin temperature were not used because the main aim is to maintain a minimum number of sensors and devices and thereby to increase the wearability in the future.
Automatic Sleep Stage Classification using Marginal Hilbert Spectrum Features and a Convolutional Neural Network

In this paper, we propose a novel method of automatic sleep stage classification based on single-channel electroencephalography (EEG). First, we use marginal Hilbert spectrum (MHS) to depict time-frequency domain features of five sleep stages of 30-second (30s) EEG epochs. Second, the extracted MHSs features are input to a convolutional neural network (CNN) as multi-channel sequences for the sleep stage classification task. Third, a focal loss function is introduced into the CNN classifier to alleviate the classes imbalance problem of sleep data. Experimental results show that the proposed method can obtain an overall accuracy of 86.14% on the public Sleep-EDF dataset, which is competitive and worth exploring among a series of deep learning methods for the automatic sleep stage classification task.
Radiation Hardened Click Controllers for Soft Error Resilient Asynchronous Architectures

Ensuring modern VLSI systems are resilient to soft errors resulting from radiation effects continues to be a challenging problem. Traditional Radiation Hardened by Design (RHBD) approaches typically have high costs in terms of area, power, and/or performance overheads. In recent years, pairing RHBD with asynchronous design has emerged as a potential solution to reduce these overheads and improve efficiency. In this paper, a generalized method for hardening asynchronous Click-based controllers is introduced, where a combination of spatial redundancy and Guard Gate (GG) is used to mitigate Single Event Transients (SET) and Single Event Upsets (SEUs). Two Click controllers that can benefit from the RHBD methodology are presented, each one targeting a recently proposed soft error resilient asynchronous architecture, the Soft Error Resilient Asynchronous Design (SERAD) and the Asynchronous Full Error Detection and Correction (AFEDC). We have implemented the different controllers using a 130nm cell library. Results show the correctness and resilience to SET and SEU events. Additionally, the proposed RHBD templates require less area and power overheads when compared to the Triple Modular Redundancy (TMR) implementation for equivalent fault coverage.
Use of multilayer network modularity and spatiotemporal network switching rate to explore changes of functional brain networks in Alzheimer’s disease

Alzheimer’s disease (AD) is progressive neurodegenerative disease. It is important to identify effective biomarkers to explore changes of complex functional brain networks in AD patients based on functional magnetic resonance imaging (fMRI). Recently, four fMRI brain network parameters were frequently used, including regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (f/ALFF) and degree centrality (DC). However, these parameters only present the changes of brain networks in a full time quantum, but ignore changes over a short period of time and lack space information. In this study we propose a new brain network parameter for fMRI, called multilayer network modularity and spatiotemporal network switching rate (stNSR). This parameter is calculated combing Pearson correlation sliding Hamming window and the Louvain algorithm. To verify the efficiency of stNSR, we selected 61 AD patients and 110 healthy controls (HC) from Xuanwu Hospital, Beijing, China. First, we used two-sample t test to identify regions of interest (ROI) between AD patients and HCs. Second, we calculated the stNSR values in these ROIs, and compared them with ReHo, ALFF, f/ALFF, and DC values between AD and HC groups. The results showed that, stNSR values in left calcimine fissure and surrounding cortex, left Lingual gyrus and left cerebellum inferior significantly increased, while stNSR values significantly decreased in left Para hippocampal gyrus, left temporal and superior temporal gyrus. As a comparison, changes in these ROIs could not be observed using ReHo, ALFF, f/ALFF, and DC. The results indicated that stNSR may reflect differences of brain networks between AD patients and HCs.
The Capacity of Multidimensional Permutations with Restricted Movement

We study the multidimensional constrained systems of ${{\mathbb{Z}}^d}$ -permutations with restricted movement. We show a correspondence between these restricted permutations and perfect matchings. We use the theory of perfect matchings to investigate several two-dimensional cases, for which we compute the exact capacity of the constrained system, and prove the existence of a polynomial-time algorithm for counting admissible patterns. We prove that the capacity of ${{\mathbb{Z}}^d}$-permutations restricted by a set with full affine dimension depends only on the size of the set. We use this result in order to compute the exact capacity for a class of two-dimensional constrained systems.
Improving Automated GUI Testing by Learning to Avoid Infeasible Tests

Most modern end-user software applications are controlled through a graphical user interface (GUI). When it comes to automated test selection, however, GUIs present two major challenges: (1) It is difficult to automatically identify feasible, non-trivial sequences of GUI interactions (test cases), and (2) each attempt at a test case execution can take a long time, eliminating the possibility of rapidly attempting large numbers of alternatives. In this paper we present an iterative approach that infers state-machine models from previous test executions, and increases the utility of tests by learning which sequences to avoid. The approach is evaluated on a selection of Java applications, and the results indicate that our approach is successful at achieving higher code coverage and longer sequences than the state of the art, albeit with a time-overhead caused by the repeated invocation of a Machine Learner.
Classification of Relative Object Size from Parietooccipital Hemodynamics Using Type-2 Fuzzy Sets

During the past two decade researchers have been exploring the mechanism of object shape and depth perception using EEG and fMRI. However, the underlying cortical process of perceiving different object sizes from a constant visual distance has never been explored. This paper provides a novel understanding of relative object size classification based on direct measure of parieto-occipital hemodynamics using functional near infrared spectroscopy (fNIRS). The cortical response is recorded from subjects engaged in visual perception task of relative object size. The signal is preprocessed (artifact removal) for construction of 176 features, which are thus reduced to 22 features using particle swarm optimization (PSO) technique. The reduced features are subsequently fed into an interval type -2 fuzzy set to classify the perceived objects (based on the underlying hemodynamic data) into three different classes: LARGE, MEDIUM and SMALL. Experimental analysis shows that the proposed feature-selection and classification framework attain higher classification accuracy which reaches over 87% in the classification of large objects. Analysis, further undertaken to know the underlying neurovascular mechanisms, reveals a distinct dorso-ventral shift (shall-medium-to-large) in parieto-occipital hemodynamic load which can be observed from the topographic brain activation. The average activation shifts are measured as 73.35 degrees in the right hemisphere compared to 93.71 degrees in the left hemisphere. The experimental outcomes could provide a novel measure in cortical hemodynamic features based perception of object size. In future, it could provide justification towards the visually challenged persons with perceptual difficulties.
The Routing Control in Mobile Ad hoc Network Using Intelligent Optimization Algorithms

MANET (Mobile Ad hoc Network) made up of a combination of nodes that are randomly moving with a certain speed in any direction without relying on the communication infrastructure. It is useful for creating a low-cost network and communication during a disaster and to avoid the failure of communication. It is necessary to improve many of the criteria metrics that affect performance. In the traditional the protocol of routing AODV (Ad hoc On-Demand Distance Vector), the data packets are sent toward the adjacent node only by the shortest path method and cannot meet the multi-objective approach. This article utilized the FOA (Fruit fly Optimized Algorithm) to control the problem and find a suitable routing instead of the shortest method. The results found is compared with the particle swarm optimization (PSO) approach and traditional AODV routing protocol, the proposed (FOA) method offers the fastest and most accurate pathway. The numerical simulations indicate that the suggested approach achieved better performances in the case of delay time and improve the efficiency of the system.
Bayesian statistic model for nurse call data considering time-series, individual patient variabilities and massive zero-count call data

Analysis of nurse call data is important to evaluate nursing management, because nurse calls reflect the fundamental demand of patients. However, the nurse call data include time-series properties and individual patient variabilities. In addition, the calls do not necessarily follow the common single distributions such as normal and Poisson distribution. These characteristics of the nurse call data cause the difficulty of applying traditional frequent statistics. To resolve this problem, we introduced Bayesian statistics and proposed a model including three elements: 1) transition, which represents time-series change of nurse calls, 2) random effect, which handles individual patient variabilities, and 3) zero inflated Poisson distribution, which is suitable for nurse call data including massive zero data. To evaluate the model, nurse call dataset containing total 3324 patients in orthopedics ward was used and the differences of nurse calls between the patients who had undergone orthopedics surgery and those who had undergone other surgeries were analyzed. The result in comparing all combinations of elements suggested that our model including all elements was the most fitting model to the dataset. In addition, the model could detect longer duration of nurse call difference existence than the other models. These results indicated that our proposed model based on Bayesian statistics may contribute to analyzing nurse call dataset.
Region Specific Weight Measuring System for Bedridden patients

Weight measurement is one important aspect, especially for monitoring long term bedridden patients or for post-operative subjects. Presently, though overall weight is measured regularly, periodicity is in the order days owing to practical difficulties like portability of equipment and shifting of patients. Even in this case, region-wise weight measurement is not done. This problem is addressed in the proposed method, where localization of region that contributes to weight change can be identified along with amount of weight change. Since no movement of subject is required, observation interval can be reduced to hours from days. This is achieved by dividing the mild steel sheet holding the subject and placing load sensors beneath the sheets. This is done after understanding and critically reviewing load and height distribution in human body. Load sensors, along with artificial neural network is able to sense the change up to 1 gram with information about location. Entire experiment was done using human phantom model. Result of the proposed method exhibits accuracy of 96% and above with linearity in measurement. Our present efforts are towards making this prototype into a real time weight measuring system.
Facial Complexion Recognition of Traditional Chinese Medicine Based on Computer Vision

This paper makes an attempt to develop an automated facial complexion recognition method for objective and quantitative facial diagnosis. In TCM diagnosis, some regions of the face like Ting, Jia and Mingtang, can provide the most valuable information, so we use deep learning technique to determine the 68 landmarks of face and use their location to segment the regions of interest (ROI). The statistical characteristics of color histograms in multiple color space and texture features, lip color features are then introduced to describe the facial complexion. Finally, several machine learning methods including KNN, SVM and BPNN are used for classification. To verify the validity of our method, we collected a dataset of 575 face images from professional TCM medical institutions. The results show that the process of ROIs’ segmentation can improve the accuracy efficiently, higher than unsegmented image. The proposed method by fusing all three features achieves an accuracy of 91.03% which is higher than the existing methods and proves the effectiveness of the proposed method for facial complexion recognition. We confirm that extracting the complexion features particularly from the regions of interest of the face image achieves higher classification accuracy than characterizing the overall complexion directly from the unsegmented images. We show that the facial color features provide the most important clues for complexion classification among all the used features, which is consistent with the TCM diagnosis. Finally, we prove that the facial texture feature and lip color feature can be used as complementary clues and fused with the facial color features for further improving the complexion classification accuracy.
EEG-based Classification of Imaginary Mandarin Tones

Speech imagery based brain-computer interface (BCI) has the potential to assist patients with communication disorders to recover their speech communication abilities. Mandarin is a tonal language, and its tones play an important role in language perception and semantic understanding. This work studied the electroencephalogram (EEG) based classification of Mandarin tones based on speech imagery, and also compared the classification performance of speech imagery based BCIs at two test conditions with visual-only and combined audio-visual stimuli, respectively. Participants imagined 4 Mandarin tones at each condition. Common spatial patterns were applied to extract feature vectors, and support vector machine was used to classify different Mandarin tones from EEG data. Experimental results showed that the tonal articulation imagination task achieved a higher classification accuracy at the combined audio-visual condition (i.e., 80.1%) than at the visual-only condition (i.e., 67.7%). The results in this work supported that Mandarin tone information could be decoded from EEG data recorded in a speech imagery task, particularly under the combined audio-visual condition.
Three-Dimensional Pattern Features in Finger Tapping Test for Patients with Parkinson’s disease

Finger tapping test is an important neuropsychological test to evaluate human motor function. Most recent researches simplified the finger tapping motion as a scissors-like motion, though the rotation axis of the thumb was different from that of the forefinger. In this paper, we proposed a three-dimensional (3-D) finger tapping measurement system to obtain 3-D pattern features in finger tapping test for patients with Parkinson’s disease (PD). The proposed system collected the motion of the thumb and the forefinger by nine-degrees-freedom sensors and calculated 3-D motion of finger tapping by an orientation estimation method and a 3-D finger-tapping kinematic model. We further extracted 3-D pattern features, i.e. motor coordination and relative thumb motion, from 3-D Finger Tapping motion. Moreover, we used the proposed system to collect the finger-tapping motion of 43 PD patients and 30 healthy controls in horizontal tasks and vertical tasks. The results indicated that 3-D pattern features showed a better performance than one-dimensional features in the identification of mild PD patients.Clinical Relevance— These three-dimensional pattern features could be used to evaluate finger tapping motion in a novel way, which could be used to better identify mild Parkinson’s disease patients. Furthermore, the results showed that a combination of horizontal tasks and vertical tasks might be a better way to identify mild Parkinson’s disease patients.
An Improved LSF-based Algorithm for Sinusoidal Frequency Estimation that Achieves Maximum Likelihood Performance

In this paper we propose a method for sinusoidal frequency estimation that improves upon our previously proposed LSF-based algorithm that used at most 5p candidate points, where p is the number of sinusoids present. In this paper we propose the following improvements: (i) reduced the number of candidate frequencies to at most 2p points, (ii) reduced the method’s threshold to equal that of ML, and (iii) reduced the computational burden by switching to methods like ESPRIT when the SNR is above threshold. Since neither the SNR nor the threshold is known, we estimate them from the data. The proposed reduction-in-threshold step can be applied to EPUMA (proposed Qian et al.), with which we compare our results. For the well-known two-sinusoid example the proposed method has the same threshold as that of ML; ML performance is also achieved when tested on a new, three-sinusoid example.
Optimization for Large-Scale Fuzzy Joins Using Fuzzy Filters in MapReduce

A fuzzy or similarity join is one of the most useful data processing and analysis operations for Big Data in a general context. It combines pairs of tuples for which the distance is lower than or equal to a given threshold ε. The fuzzy join is used in many practical applications, but it is extremely costly in time and space, and may even not be executed on large-scale datasets. Although there have been some studies to improve its performance by applying filters, a solution of an effective fuzzy filter for the join has never been conducted. In this paper, we thus extend our previous work by proposing a novel fuzzy filter to optimize fuzzy joins. This filter is a compact, probabilistic data structure that supports very fast similarity queries by maintaining a bit matrix, with small false positive rate and zero false negative rate. We show that our proposal is more efficient than others because of eliminating redundant data, reducing computation cost and avoiding duplicate output.