Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling the Statistics of Microwave Radar Sea Clutter

One of the key requirements of a radar system is to detect targets against the background interference. This can be a challenging problem in the maritime domain when the sea moves in a complex manner and the characteristics are time-varying or nonstationary. Understanding the characteristics of sea clutter is therefore essential in developing effective and robust detection schemes. The first part of this tutorial provides details of the relevant statistical models from the literature which are typically used to characterize the sea clutter. Using these models enables us to represent clutter in different environments that may be difficult to observe in trials and to predict the performance of radars over a range of different conditions with more confidence. Future radar systems may well comprise transmitters and receivers which are separated by some distance. Analysis of bistatic sea clutter is therefore covered in the second part of this paper with a focus on how the statistics vary with different bistatic angles. The final part then looks at the application of radar models for clutter simulation and performance prediction for both coherent and non-coherent detection schemes.
Analysis of Joint Relay Selection and Resource Allocation Scheme for Relay-Aided D2D Communication Networks

Device-to-device (D2D) communications, as one of the promising technology for the future network, allow two devices in proximity to communicate directly with each other to alleviate the data traffic load for the base station (BS). In this paper, we analysis the joint relay selection and resource allocation scheme for relay-aided D2D communication networks. The objective is to maximize the total transmission data rates of the system while guaranteeing the minimum quality of service (QoS) requirements for both cellular users (CUs) and D2D users (DUs). We first propose a social-aware relay selection algorithm with low computational complexity to obtain the suitable relay nodes (RNs) for D2D links. Then the power control schemes are considered when a D2D link works in the D2D direct or relay mode to maximize the system transmission data rates. On basis of this, we propose a greedy-based mode selection and channel allocation algorithm, which can not only allocate the appropriate channel resource for D2D links, but also select the optimal communication mode for them. Numerical results demonstrate that the proposed scheme is capable of substantially improving the performance of the system compared with the other algorithms.
Microgrid Management with PV Power Prediction via Stochastic Distributed Optimization

This paper is concerned with a stochastic distributed model predictive control (MPC) technique for power management of a PV-installed microgrid. The PV power supply has large uncertainty because it depends on weather conditions. To keep stable power supply to the microgrid, both accurate prediction of PV power supplies and efficient energy management based on the prediction are essential. We propose a distributed MPC method for microgrid control by combining the ADMM-based distributed optimization and the randomized algorithm approach under the situation that a stochastic prediction model for the PV power prediction is available. The proposed method enables us efficient energy management in a distributed way as well as the probabilistic guarantee of the line and battery capacity constraints. We demonstrate the effectiveness of the proposed method by a numerical simulation.
Multimodal Image-Free Ultrasound Technique for Evaluation of Arterial Viscoelastic Properties: A Feasibility Study

In this article, we have presented a multimodal system and a novel probe design that was built around an image-free ultrasound technology, ARTSENS®, for measurement of arterial viscoelastic properties. ARTSENS® was extensively validated over the years, for performing measurements of arterial wall dynamics and stiffness with an accuracy that meets clinical standards. Concerning this work, several enhancements were incorporated to this basic technology that allowed high frame rate A-scan imaging (1 kHz) and integration of a pressure measuring module for automated measurements of the viscoelastic parameter (elastic index, viscous index and wall buffering function). The functionality of the developed multimodal system and probe were investigated by conducting an in-vivo on 8 young subjects (both normotensive and hypertensive were included). The beat-to-beat measurements of the viscoelastic parameters exhibited acceptable repeatability with a variability < 6.5%. It was observed that the group average for viscosity index and the wall buffering function were higher for hypertensive subjects as compared to normotensive subjects. The study observations were consistent with the reported literature. The proposed system addresses several issues associated with the traditional image-based systems and offers huge advantage of field amenability thus making it favorable for large population screening and studies.
The Forecasting of PM2.5 Using a Hybrid Model Based on Wavelet Transform and an Improved Deep Learning Algorithm

In recent years, the haze has caused serious troubles to people’s lives, with the continuous increase of PM2.5 emissions. The accurate prediction of PM2.5 is very crucial for policy makers to make predictive measures. Due to the nonlinearity of the PM2.5 time series, it is difficult to predict accurately. Despite some studies about PM2.5 being proposed, the problem of the LSTM (long short-term memory) gradient disappearance and random selection of wavelet orders and layers isn’t still solved. In this study, a novel model based on WT (wavelet transform)-SAE (stacked autoencoder)-LSTM is proposed. Firstly, six study sites from China are taken as examples and WT is used to decompose PM2.5 time series into several low-and high- frequency components based on different samples. Secondly, the decomposed components are predicted based on SAE-LSTM. Finally, the predicted results are reconstructed in view of all low-and high-frequency components and the predicted results are obtained. The results imply that: (1) the forecasting performance of SAE-LSTM is better than that of other models (e.g., BP (back propagation)) used for comparison; (2) for six different PM 2.5 samples, four orders five layers, five orders six layers, five orders seven layers, three orders six layers, five orders seven layers, and five orders six layers are the most appropriate. The conclusion that such a novel model may help to enhance the accuracy of PM 2.5 prediction can be drawn.
A Method for Multi-day Tracking of Gastrointestinal Smooth Muscle Contractile Patterns in Organotypic Culture

Gastrointestinal (GI) motility is a key component of digestive health, and it is a complex both rhythmic and arrhythmic process governed by many physiological factors. The ability to accurately track the movements of the GI tissues in vitro over multiple days would provide valuable insights into GI tract physiology. A correlational analysis tracking algorithm was developed and applied to intestinal smooth muscle tissues that were maintained in organotypic culture. Physiologically relevant metrics, such as frequency, amplitude and motility index were independently assayed to quantify smooth muscle contractions. The results were validated by manually detected frequency determined using a standard software package. The algorithm was capable of tracking the changes in contractions of the same tissues over six days. This proof-of-concept study demonstrates the feasibility of long-term tracking of GI motility in organotypic cultures over multiple days. The approach allows study of the effects on GI smooth muscle contractility of direct controlled targeting of the cells and molecules in the GI tunica muscularis.
Nightingale V2: Low-power Compact-sized Multi-Sensor Platform for Wearable Health Monitoring

Nightingale V2, a new wearable multi-sensor platform, is introduced in this paper. It can measure various bio-signals including ECG, BioZ, PPG, motion and heart sounds simultaneously at very low power consumption. Patient safety with optical and electrical sensors were carefully investigated. Preliminary data collection results as well as the sensor characterization results reveal that the Nightingale V2 would be suitable for wearable health monitoring applications.
Influence of State Ownership on M&A Performance

While the total amount of mergers and acquisitions (M&As), in terms of both number and value, is at a record high, post-M&A performance has been shown not to be necessarily positive for firms. This paper extends from past research and intends to discover the role that governments play in influencing post-M&A performance-specifically, how state ownership has an effect on post-M&A performance is the primary question intended to be answered. The results are generally supportive to our hypotheses and arguments. By incorporating eras with nationwide government policies in place, we are able to show that post-M&A performance is actually enhanced by state ownership. This finding is further strengthened during institutional change.
Effect of interphase gap duration and stimulus rate on threshold of visual cortical neurons in the rat

Stimulation threshold is a key parameter to enable an efficient design for retinal implants. Stimulation parameters such as stimulus pulse duration, pulse amplitude, pulse repetition, pulse shape and polarity have been shown to be the key factors that can influence the efficacy of retinal prosthetics. The effectiveness of these devices should best be evaluated both in the retina and in the visual cortex. Prior electrophysiological studies in the retina have shown that introducing an interphase gap make stimulation more efficient. Previous in vitro studies have also demonstrated the response properties of retinal ganglion cells are frequency dependent. However, the effect of these two stimulus parameters are not well explored at the cortical level where higher visual processing signals are processed. In this study, we examined the response properties of visual cortical neurons under stimulation of retinal ganglion cells in rat using a single-channel electrode of diameter 75 µm. We compared the response strength curves as a function of stimulus current amplitudes under different stimulus pulse duration, interphase gap and stimulus rate. Localized response to single channel epiretinal stimulation was robustly observed in V1 neurons. We found that V1 neurons were more sensitive to longer pulse and stimulus with an interphase gap, similar to previously reported results in the retina. We were also able to examine the effect of stimulus frequency on threshold in the visual cortex. Our results indicate that electrical activation of V1 neurons are more efficient at low frequency.
Loss Comparison for Different Technologies of Semiconductors for Electrical Drive Motor Application

In this paper, a loss comparison is performed between different semiconductor switching devices for a three-phase voltage source inverter (VSI). A two-level VSI is used for a high-speed electrical motor drive application. The topology is examined using Silicon (Si) metal-oxide-semiconductor field-effect transistors (MOSFET), Si insulated-gate bipolar transistors (IGBT) with a Silicon Carbide (SiC) anti-parallel Schottky diode and SiC MOSFETs. The comparison is done in regards to the total (conduction and switching) loss and efficiency.
Design and Testing of a New Compliant Parallel-Kinematic Constant-Force End-Effector

This paper presents the design, analysis and testing of a novel three-degree-of-freedom (3-DOF) parallel-kinematic constant-force end-effector. The constant-force end-effector enables a safe interaction between the end-effector and contacted object. To obtain the constant-force property without extra fixing support, the symmetrical bistable flexure hinges are adopted. Then, to reduce the constant-force value, a preloading constant-force mechanism is introduced. The parallel-kinematic mechanism based on flexure hinges is constructed to decouple the 3-DOF motion. FEA simulation study is conducted to investigate the constant-force property and the decoupling ratio of the designed mechanism. Moreover, a prototype is fabricated and experimental testing is conducted to verify the performance of the constant-force mechanism.
Improving Accessibility for People with Disabilities: A Case Study on Inclusive Beach Tourism*

Disability is an important area in biomedical engineering. But research on disability should not only focus on the healthcare aspects, but also on the integration of people with disabilities in the cultural and social contexts, such as the existence of architectural elements that prevent the use of common public services. The present research aims to improve accessibility and enjoyment of people with physical and motor disabilities to the tourist resources of the area of interest. We present a case study focused on the beaches of the Region of Murcia, Spain. Both the architectural and technological aspects of accessibility are analyzed. From the architectural point of view, the method includes the definition of the parameters of accessibility, and the features required for the equipment according to the local and national regulations. From the technological point of view, the tools and requirements necessary to develop the system are presented.
An EEMD-Based Electromagnetic Induction Method for Nondestructive Testing of Buried Metal Conductors

Nondestructive testing of substation grounding grids is an issue that has increasing importance. The traditional EMI method transforms the condition of the undergrounding conductors to the surficial induced electric signal in the sensing coil. However, The EMI signals excited by multiple coexisted faults combining with other unknown noises surrounding the substation often cause the failure of detection. Therefore, the observed EMI signals rather complex and cannot be used directly. To address this problem, the separation of individual signatures from the mixture is posed as an SCBSS problem. To extract the induced signal, an EEMD-based EMI method is proposed. The desired signal is then reconstructed to visualize the structure of the grounding grids by a virtual instrument that consists of DAQ and digital signal processing modules. The numerical simulation and practical experiments are employed. The results show the proposed method can be used to effectively detect the topological structure of grounding grid in real substations’ electromagnetic environment.
Toward a cooperation index based on EEG-workload causality: preliminary findings on aerospace-like tasks

According to Human-System Integration analyses, cooperation between humans is one of the most relevant factors in many of today's human activities: do not take it into account in models of working environments is highly farfetched. Although the Human Factor aspects have obtained much benefit from the use of neurophysiological signals to estimate human-machine interaction, very few are the indications about neurophysiological analysis of human cooperation deviating from typical laboratory tasks. Among these, some evidence showed that there is a relationship between the mental workload experienced by the subjects cooperating and some characteristics of the brain network obtained through multi-subjects connectivity analysis. Accordingly, this work aimed to identify common dynamics in time series that describe the EEG-based mental workload of cooperating subjects and to exploit this information to create an index of cooperation. In order to answer the question whether a causality between the workload values of the two subjects can be in some way discerned and related to the cooperation required by the task, Granger's causality test has been performed. This method was applied to two different tasks simulating features of the aerospace domain. The results showed that the causality test was statistically significant for the most collaborating couple. In addition, causality values are modulated by the presence of real couples compared to fake couples. The extension of the experimental sample could open up the possibility for the development of an objective and neurophysiological signals-based cooperation index.
Different roles for theta- and alpha-band brain rhythms during sequential memory

Numerous studies have demonstrated that brain rhythms are modulated according to memory performance or memory processing. In sequential memory tasks, memory performance can be reduced by shortening the intervals between memory item presentations. To clarify the neurophysiological mechanism underlying this, we recorded magnetoencephalograms in 33 healthy volunteers performing two sequential memory tasks with either short or long intervals between memory items (hereafter, fast and slow conditions, respectively). Memory accuracy, and theta- and alpha-band activities originating from occipital and frontal brain areas were analyzed. Memory performance was significantly lower for the fast condition than the slow condition. Meanwhile, occipital and frontal theta activities were significantly lower for the fast condition than the slow condition. Increased occipital-alpha, a sign of active inhibition of task-irrelevant visual input, occurred regardless of condition. However, memory processing related to occipital- and frontal-theta activities had some temporal limitations. Namely, the shorter intervals of the fast condition attenuated theta activity, likely disrupting working memory processing, thereby leading to the observed decline in memory performance.
A data-aware write-assist 10T SRAM cell with bit-interleaving capability

Abstract: Cell stability is becoming an important design concern as process technology continues to scale down. In this paper, we present a single-ended 10T SRAM cell that improves simultaneously both read static noise margin (RSNM) and write static noise margin (WSNM) by employing separate read buffer and power gating transistors, respectively. The cross-point write structure of the proposed cell facilitates bit-interleaved architecture to enhance soft-error immunity. Simulation is done on 65 -nm CMOS technology on Cadence. Simulation results show that the RSNM of the proposed SRAM cell is 2.78 times and 1.47 times higher than those of the conventional 6T and Schmitt trigger-based 10T (ST-2) cells, respectively, at 0.4 V. The WSNM of the proposed design is 5.14 times larger than that of the two-port disturbfree 9T (TPDF9T) cell (without write assist) at 0.4 V. Write delay of the proposed cell is 77.56% less than that of the TPDF9T cell at 0.4 V. Leakage power dissipation of the proposed SRAM cell is 0.89 times that of the ST-2 cell at 0.4 V. The proposed cell occupies 1.34 times more area than the conventional 6T cell.
Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning

Glaucoma is the second leading cause of blindness worldwide. This paper proposes an automated glaucoma screening method using retinal fundus images via the ensemble technique to fuse the results of different classification networks and the result of each classification network was fed as an input to a simple artificial neural network (ANN) to obtain the final result. Three public datasets, i.e., ORIGA-650, RIM-ONE R3, and DRISHTI-GS were used for training and evaluating the performance of the proposed network. The experimental results showed that the proposed network outperformed other state-of-art glaucoma screening algorithms with AUC of 0.94. Our proposed algorithms showed promising potential as a medical support system for glaucoma screening especially in low resource countries.
A Parkinson’s Disease Classification Method: An Approach Using Gait Dynamics and Detrended Fluctuation Analysis

Parkinson’s Disease (PD) is a neurodegenerative disorder that affects, among other things, the gait rhythm. This paper presents an automatic method to identify PD subjects from healthy subjects using information derived from a time series of stride intervals, swing intervals, stance intervals and double support intervals of stride-to-stride measures of footfall contact times using force-sensitive resistors. In our approach, we propose the use of machine learning based classifiers along with features based on metrics of fluctuation magnitude and fluctuation dynamics, obtained from a detrended fluctuation analysis. We evaluate and compare performance of five state-of-the-art classification methods according to their accuracies: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA) and Decision Tree (DT). Our experiments were carried out on a publicly available data base of gait dynamics in neurodegenerative diseases. The results show an average accuracy of 96.8%, representing an improvement compared to other results in the literature. Therefore, the proposed approach presents a path towards an automated, non-invasive and low-cost diagnosis of Parkinson’s Disease.
A Framework for Evaluating Physical-Layer Network Coding Gains in Multi-Hop Wireless Networks

Physical-Layer Network Coding (PLNC) was first introduced as a solution to increase the throughput of a two-way relay channel communication. Unlike most wireless communication techniques which try to avoid collisions, PLNC allows two simultaneous transmissions to a common receiver. In basic topologies, this technique has been shown to significantly enhance the throughput performance compared to classical interference-free communications. However, quantifying the impact of PLNC in large multi-hop networks remains an open question. In this work, we introduce the first theoretical framework that, given a particular network topology and traffic matrix, can evaluate the optimal network throughput subject to a fairness constraint relative to the initial demand of each flow, when PLNC is adopted. Based on linear programming, our solution ensures to respect the particularities imposed by PLNC. We use this framework to evaluate three state-of-the-art PLNC schemes across a variety of topologies and traffic matrices. Our numerical analysis reveals that while in simple toy topologies PLNC can significantly increase the network throughput, in large topologies the verdict is mixed. For certain topologies and traffic patterns adopting PLNC can double the throughput while in others, depending on the scheme used for implementing PLNC, the gain can be as high as 60 percent or as low as 0 percent when compared to traditional interference-free transmissions.
Using Convolutional Neural Networks for Classification of Bifurcation Regions in IVOCT Images

Optical Coherence Tomography (OCT) technology enabled the experts to analyze coronary lesions from high-resolution intravascular images. Studies have shown the relationship between bifurcation regions and a higher occurrence of wall thickening and lesions in these areas. Some level of automation could benefit experts, since examining pullback frames is a laborious and time-consuming task. Although Convolutional Neural Networks (CNN) have shown promising results in classification tasks of medical images, we did not identify the use of CNN’s in IVOCT images to classify bifurcation regions in the literature. In this work, we evaluated a CNN architecture in the bifurcation classification task trained with IVOCT images from 9 pullbacks from 9 different patients. We used data augmentation to balance the dataset, due to the low amount of bifurcation-labeled frames. Our classification results are comparable to other works in the literature, presenting better result in AUC (99.70%).