Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoding Resting-state EEG to Predict Visual Field Defect with Convolutional Neural Network in Stroke

Stroke is one of the leading factors of human being's death and disability. One-third of stroke patients may suffer partial visual field loss in both eyes. The relationship between brain oscillation after a unilateral occipital stroke and visual field defect is worth investigating. Decoding resting-state Electroencephalography (EEG) to predict patients' visual field distribution could be an essential reference for a better understanding of the compensation of visual functions after a stroke. The result could be beneficial for clinical diagnostics and treatment. This paper proposed a frequency spectrum-based 2D convolutional neural network(CNN) and brain connectivity-based 3DCNN model to predict the visual field defect. The results show that the frequency spectrum-based 2DCNN achieved a higher accuracy on visual field location than the connectivity-based 3DCNN model. Simultaneously, the percentage seems to be not predictable for both domains, and therefore we also explored the patterns of electrophysiological data for feature visualization and interpretation.
Motor Speed Synchronization of Mobile Robot Using PI Controller

Mobile robots due to their ability of movement have proven to find their applications in different fields over the years. Their applications in different fields are due to easy design and manufacturing of the custom platform. Manufacturing of the custom chassis/platforms may result in synchronization issue of motors resulting in detouring of the robot chassis from the path or complexity of the algorithm. To address this issue, design and tuning of a PI controller for a custom built three wheeled mobile robot for synchronization of the motors is discussed in this paper. Results achieved are very close to the desired response of the motors to move the mobile robot in a straight path. Paper highlights the method of designing and tuning a PI controller for wheeled mobile robots.
Measurement and Evaluation of Electric Signal Transmission Through Human Body by Channel Modeling, System Design, and Implementation

Human body communications (HBCs), employing the human body as a signal transmission medium, can provide efficient and intuitive methods to form a network in the body. This article presents a comprehensive study for a highly reliable HBC system, including body channel modeling, transceiver design, and performance evaluation through implementation, in consideration of practical sensor network environments for wearable and implantable devices applicable to healthcare and biosignal acquisition. Body channel characteristics based on capacitive couplings, such as root mean square delay spread and mean path gain (MPG), were explored by measuring the body impulse responses under 12 experimental conditions, determined by the variation in body postures and device locations between the wrist and positions assumed under the scalp through a customized experimental setup with micropig-derived biomembranes (MBMs) used for wrapping the devices to emulate an implantable environment. The proposed transceiver design for digital transmission, including a preamble structure and signal modulation method supporting a maximum data rate of 1 Mb/s, was verified through the performance evaluations conducted for examining the frame detection probability and bit error rate (BER) in the body channel model at multiple operating frequencies of 32, 42, and 64 MHz. The proposed system reliability was demonstrated by the achievement of a BER of below ${4.7\,\times \, }{10}^{-8}$ through battery-powered implemented devices with dimensions of ${30\times 30 }\,\,{\text {mm}}^{2}$ , comprising a digital signal processing module (DSPM) for signal generation and detection processes, and an analog front-end module (AFEM) for recovering the received signal from signal deterioration by severe path loss and time-dispersive effect in the body channel.
A Backscatter Channel Sounder Using Tunneling RFID Tags

This paper introduces a backscatter channel sounder technique used for a radio-frequency identification (RFID) positioning system at 5.8 GHz. This system applies received signal phase (RSP)-based positioning and channel sounding techniques to a tunneling tag, providing sufficient information to calculate the delay spectrum for accurate positioning in a complicated multipath environment. Ultra-precise (0.45%) position estimates at long distances (100 m) are achieved using the proposed channel sounding techniques.
Pairing-Free for Public Key Encryption With Equality Test Scheme

The modular exponentiation has been proved better in terms of computational efficiency as compared to bilinear pairing. Therefore, discrete logarithm, a concept of modular exponentiation may be incorporated to present improved security schemes. With this aim, an efficient public key encryption scheme with equality test (PKEwET) is proposed in this paper. Unlike the most of existing bilinear pairing based PKEwET schemes, the presented scheme employs discrete logarithm to improve the computational efficiency. Moreover, we introduce the verification process during the test phase to validate the ciphertext and to improve the security comparative to the prevalent schemes. The performance analysis depicts that the proposed scheme is quite efficient and more practical than the existing PKEwET schemes. More precisely, compared to Lin et al.’s scheme, the time cost has been reduced by at least 39% for the decryption process. Furthermore, for the type-3 test phase, the time cost has been reduced by at least 68%.
Changes in Heart Rate Variability after Transcranial Direct Current Stimulation in Patients with Refractory Epilepsy

Cathodal transcranial direct current stimulation (c-tDCS) is a non-invasive option for treatment of refractory epilepsy. However, it is still unknown whether this therapy has a positive stabilizing effect on the vegetative function of these patients. Heart Rate Variability (HRV) is considered an efficient tool to monitor the cardiac autonomic system, which has been correlated with the risk of Sudden Unexpected Death in Epilepsy (SUDEP). In this study, changes in HRV are investigated after c-tDCS of six patients ($34.50 \pm 11.10$ years) with refractory epilepsy, which have been selected at the University Hospital, LMU Munich. Patients were categorized as responders ($\mathrm{n}=2$), non-responders ($\mathrm{n}=3$) and uncategorized ($\mathrm{n}=1$). We analyzed 24 hours of electrophysiological data recorded before and after treatment, and computed HRV metrics (AVNN, SDNN, RMSD, pNN20, pNN50, LH/HF, 0V, IV, 2LV, 2UV, SD1 and SD2). All patients revealed a change in almost all HRV metrics post stimulation. Grouped all patients, there was a significant ($\mathrm{p} < 0.05$) change in RMSSD, pNN50, SD1 and LH/HF. For responders there was an increase in all time domain and non-linear metrics, which was not seen for non-responders. These results suggest that tDCS exerts significant changes in cardiovascular autonomic system in patients with refractory epilepsy. HRV metrics may also serve as biomarkers of the response to tDCS stimulation. A larger dataset is being gathered for further analysis.
TanDEM-X:Deriving InSAR Height Changes and Velocity Dynamics of Great Aletsch Glacier

Great Aletsch Glacier (Grosser Aletschgletscher), the largest glacier in the European Alps, contains 20% of the entire Swiss ice mass. Therefore, it has been selected as a super-testsite for the TanDEM-X satellite mission. Dense time series with a repeat interval down to 11 d were acquired between 2011 and 2019 using two polarizations (HH and VV) and across-track baselines of 0–1 km. To evaluate the use of interferometric single-pass synthetic aperture radar mission for glaciological applications, we implemented a processing pipeline in interactive data language (IDL) and computed 130 digital elevation models (DEMs) from bistatic radar interferograms. We present a method to circumvent a common pitfall during orthorectification of radar DEM differences. Regression analysis of DEM time series shows a height loss of up to 8 m a<inline-formula><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula> on the tongue and 1.5 m a<inline-formula><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula> when averaged over the whole glacier area. In spring 2013, we observed X-band penetration depths of 4<inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>2 m in the accumulation area. For strongly crevassed areas, the coherence drops already to 0.5 for across-track baselines <inline-formula><tex-math notation="LaTeX">$\mathbf {B_\perp }>$</tex-math></inline-formula> 200 m. With patch-based incoherent offset tracking, we obtained an almost complete 200 m resolution velocity map. Velocities reach up to 0.8 m d<inline-formula><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula>, show a seasonal variability of <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>0.05 m d<inline-formula><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula>, and agree within 0.04 m d<inline-formula><tex-math notation="LaTeX">$^{-1}$</tex-math></inline-formula>(root mean square error) with field measurements. Copolar phase differences suggest an approximation of snow accumulation. We demonstrate orthorectification of the backscatter intensity using simultaneously acquired TanDEM-X interferograms, which allows for the decoupling of horizontal velocity estimates from phase-center height changes due to penetration and ice melt.
Considering the Design and Operation of a Voice Controlled Laboratory for Teaching and Learning in Electronic Engineering

In this paper, the design and operation of a voice controlled electronic engineering laboratory for the teaching and learning of semiconductor device current-voltage (IV) characteristics are presented. This is targeted to students studying electronic engineering in a higher education setting. The presented case study experiment operation, based on a Schottky diode forward and reverse bias IV characteristics, is presented. This uses a computer-based interface to an Arduino UNO platform that acts as the experiment test and measurement equipment. The computer side software developed is based on a Python script along with a speech recognition library and the online Google Speech Recognition service for identifying voice commands.
Evaluation of Deep Brain Stimulation Effect on Motor Signs of Individuals with Movement Disorders Through Gaussian Models

In late stages of Parkinson's Disease (PD) or Essential Tremor (ET), surgical treatments, such as Deep Brain Stimulation (DBS), are promising. However, conventional DBS devices are open-loop systems in which the stimulation intensity is constant regardless of the dynamic behavior of motor manifestations. In this sense the seek to objective strategies that can assess the efficacy of DBS is relevant for the development of precise medicine, promoting the implementation of closed-loop DBS. This study proposes the use of individualized Gaussian models to evaluate the motor signs of people with PD and ET considering DBS in ON and OFF states. Inertial data were collected from the left and right hands and forearms of the participants. After data preprocessing, amplitude and variability-based features were extracted to estimate specific Gaussian models for each individual. An objective metric that is sensitive to the distance of models from DBS in ON and OFF states was proposed. The specific Gaussian models allowed for the estimate of tremor probability in the DBS states. The estimated values of the proposed metric discriminated between the DBS ON and OFF states, and the verification of the DBS effect on the motor sign of each individual. The specific Gaussian models can be used as part of a closed-loop DBS system to estimate the tremor severity of patients.
An Improved WBSN Key-Agreement Protocol Based on Static Parameters and Hash Functions

Wireless telecommunications systems have expanded rapidly over the past few years. Wireless Body Sensor Network (WBSN) is a relatively novel area of research and development in healthcare systems. However, it has multiple constraints and challenges regarding human health, social interactions, coverage radius, energy consumption, and communication reliability. In addition, communications between nodes can contain highly sensitive personal information, while hostile environments will impose a wide range of security risks. Therefore, designing authenticated key agreement (AKA) protocols is a crucial challenge in these networks. The current study, considering the security issues of the Li et al. scheme and some of their new extensions, proposes an improved AKA protocol with anonymity and unlinkability of the sensor node sessions. The results of theoretical analysis compared with similar schemes indicated that the proposed scheme could reduce average energy consumption and communication cost by 41 percent. It also reduced the average computation time by 61 percent. Furthermore, it was shown by formal/informal analyses that, on top of unlinkability and anonymity features, other central security features in the current scheme were similar and comparable to those in the recent and similar schemes.
Characterizing the Area of Significant Brain Activation Region to Spectrally-degraded Music: A Functional Near-Infrared Spectroscopy Study

Cochlear implants (CIs) are the only auditory prosthesis for treating deafness, yet the ability of implanted patients to music perception is inferior. The present work studied the effect of spectrally-degraded processing (via a noise-vocoding process to simulate CI speech processing) on the area of significant activation in music processing in auditory cortex using functional near-infrared spectroscopy (fNIRS). The music stimulus was processed by 32-channel and 16-channel noise-vocoders, and presented to normal-hearing listeners to perceive. The fNIRS-based results in this work showed that compared with the original music condition, spectrally-degraded processing with a large number (i.e., 32) of vocoded channels had a significant influence on the area of significant activation in the left auditory hemisphere, and further decreasing the number of vocoded channels remarkably reduced the area of significant activation in the right auditory hemisphere.
Test-Retest Reliability of Graph Metrics in Functional Brain Network

The statistical link between spontaneous fluctuations occurring in different parts of the brain can provide insights into its functional organization. Here, we used high-quality resting-state fMRI (rs-fMRI) data acquired with a test-retest (TRT) paradigm to assess the reliability of graph metrics. After applying time/frequency methods to generate FC matrices, we restricted our focus on global, local and central graph measure through different statistical measures, including but not limited to the intraclass correlation coefficient (ICC). We found that full correlation and magnitude square coherence yielded more reproducible measurements than the other metrics, as revealed by ICC values. These results have important implications when choosing metrics for quantifying FC in rs-fMRI studies, adding novel information to the current panorama of information on TRT reliability topic.
Demonstration and Modification of the pH Profile of Polyphenol Oxidase in Solanum Lycopersicum

Polyphenol oxidase (EC.1.14.18.1) catalyzes the hydroxylation and oxidation of phenols to o-quinones leading to browning in plants. For polyphenol oxidase activity two pH optimum were observed, respectively at 6.7 and 8, that probably belong to at least two isoenzyme, so we named them ISOIPPO and ISOIIPPO respectively. These isoenzymes have different kinetics properties. Activation of PPO by SDS occurred below 1.3 mM SDS. ISOIPPO was much more active (86 times) with pyrogallol as substrate (4.9 unit/mg protein) in comparison of catechol (0.057 unit/mg protein) in presence of SDS. This comparison is 143 times in absence of SDS (3.3 and 0.023 unit/mg protein for pyrogallol and catechol, respectively). SDS activation probably alters both its enzymatic and physical characteristics. ISOIIPPO activity gradually increased during development of tomatoes from green to ripe, but ISOIPPO activity is nearly constant during development. PPO are believed to play physiological roles both in preventing insects and microorganisms from attacking plant and as part of the wound response of plant by its phenolic content, so these roles are more important for ISOIIPPO than ISOIPPO during changes from green to red tomatoes.
A Time-Varying Method for Brain Effective Connectivity Analysis of Emotional EEG Data

Due to individual differences, different subjects have different response to different emotions. Only using the electroencephalogram (EEG) data that can best describe emotions, can the constructed brain networks better reflect the brain connection patterns. This paper proposes a window selection method of time-varying sequence to select critical EEG data that are most relevant to emotions. The generalized orthogonalized partial directed coherence (gOPDC) method is used to construct effective brain networks based on critical EEG data, which can explore the statistical relationship and the information flow between nodes. At the same time, the false connections caused by volume conduction in EEG channels can also be overcome. We extract network features for brain effective connectivity analysis and emotion recognition. The results of the experiments indicate that emotion is mainly related to the frontal region of the brain, and the parietal lobe also plays an important role in low valence emotions. The connection of brain network is the densest in negative emotions and the least in calm and relaxed emotions. The average classification accuracy reaches 78.78% by using KNN classifier on critical EEG data, and the classification results are better than using all data and the intermediate data.
On Workload-Aware DRAM Failure Prediction in Large-Scale Data Centers

DRAM failures are one of the major hardware threats to the reliability of large-scale data centers since the uncorrectable errors in DRAMs may cause servers to shut down. Existing works try to solve this problem by predicting DRAM failures in advance with Machine Learning models. In these works, correctable errors (CEs) are generally deemed as the most important feature. The major reason behind CEs’ emergence is the accumulated stress caused by intensive workloads. Moreover, defective DRAMs will not manifest themselves as system errors until the defective cells are accessed by some specific workloads. Therefore, the running workloads on a server are also important for DRAM failure prediction. In this paper, we focus on the impact of workloads on DRAM failures. We design the workload features from both macroscopical and microscopical aspects, i.e. node-level performance metrics and cell-level DRAM access pattern, respectively. Furthermore, we propose Hierarchical DRAM Error Code (HiDEC) to represent the DRAM access pattern. We leverage several Decision Tree-based models for DRAM failure prediction to highlight the generality of our designed features. Experiments are carried out based on the dataset collected from a real-world commercial data center. The results show that both macroscopic and microscopic features can bring significant improvements to the prediction performance.
A Non-Linear Lumped Model for the Electro-Mechanical Coupling in Capacitive MEMS Microphones

This work provides an analytical non-linear model for the capacitive transduction in MEMS transducers with perforated counter-electrodes, especially applicable to capacitive MEMS microphones. Starting from an electrostatic description of a perforated unit cell of the transducer, analytical formulations of the variable capacitance and electrostatic forces are derived, accounting for the deflection profile of a clamped circular plate. A lumped implementation into conventional circuit simulations tools is enabled via behavioral modeling based on hardware description languages, such as Verilog-A. Therefore, the analytical model is approximated via Taylor series expansions, allowing for a stable and non-linear behavioral implementation. The resulting model finally enables both a small- and large-signal analysis of capacitive MEMS microphones, precisely accounting for non-linearities in the capacitive transduction. This allows to simulate the harmonic distortion of the microphone’s output signal and to account for electrostatic spring-softening in simulations of its bias voltage dependent sensitivity. [2020-0378]
Host-Based Intrusion Detection Model Using Siamese Network

As cyberattacks become more intelligent, the difficulty increases for traditional intrusion detection systems to detect advanced attacks that deviate from previously stored patterns. To solve this problem, a deep learning-based intrusion detection system model has emerged that analyzes intelligent attack patterns through data learning. However, deep learning models have the disadvantage of having to re-learn each time a new cyberattack method emerges. The time required to learn a large amount of data is not efficient. In this paper, an experiment was conducted using the Leipzig Intrusion Detection Data Set (LID-DS), which is a host-based intrusion detection data set released in 2018. In addition, in order to evaluate and improve the performance of the system, a host-based intrusion detection model consisting of pre-processing, vector-to-image processing, training and testing steps is proposed. In the training and testing steps, a Siamese Convolutional Neural Network (Siamese-CNN) is constructed using the few-shot learning method, which shows excellent performance by learning a small amount of data. Siamese-CNN determines whether the attack type is the same based on the similarity score of each cyberattack sample converted to an image. The accuracy was calculated using the few-shot learning technique. The performance of the Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN are compared to confirm the performance of Siamese-CNN. As a result of measuring the accuracy, precision, recall, and F1-score indicators, it was confirmed that the recall of the Siamese-CNN model proposed in this study increased by about 6% compared to the Vanilla-CNN model.
Frequency Estimation Based on Linear Predictor using Unbiased WLS Algorithm

An unbiased weighted least square (UWLS) adaptive algorithm is developed and proposed to cooperate with a second-order linear predictor to estimate the frequency of a single tone embed in noise. A new objective function and updating equations are introduced to improve the performance of previous WLS algorithm. Extensive simulations are drawn to verify the performance of the proposed estimator.
Single-layer Chipless RFID Sensor for Metal Crack Detection

This paper presents a development of the chipless RFID tag for the metal crack detection. A circular microstrip patch antenna (CMPA) resonator was employed as a main part of the chipless RFID tag. Slots was creased within the CMPA resonator in order generate the resonant frequencies used to indicate the bit ID of tags. The performance of the chipless RFID sensor used to detect the metal crack was evaluated via simulations. The simulation results in term of RCS spectra of the tag sensors were shown. The RCS spectra of the tag attached to the metal plate in order to detect the crack was investigated. The results shown the notch of the RCS spectra was shifted when there exists the crack on the metal.