Miracle-dz/UniFAR-FTUs huggingface dataset:
SSGL1/2: An Improved SVM with Smooth GroupL1/2 for Predicting AD

Alzheimer’s disease (AD) is currently one of the mainstream senile diseases recognized in the world. It is the key problem how to automatically identify the early AD based on structed Magnetic Resonance Imaging (sMRI). In order to achieve accurate recognition of AD and obtain highly relevant brain lesions, an improved SVM with group L1/2 sparse regularization and smoothing function (SGL1/2) is proposed. It can achieve sparseness within the group, and approximate the non-smooth absolute value function to a smooth function. The improved model adopts a calibrated hinge to replace the hinge loss function in traditional SVM which is abbreviated as SSGL1/2. In the experiment, the proposed model is applied to different sMRI datasets for training and testing. Compared to other regularization of the non-group level and the group level, the classification accuracy of the proposed method reaches up to 96.03%. At the same time, the algorithm can point out the important brain areas in the MRI group, which has important reference value for the doctor’s predictive work.
Prescribed Performance Sliding-mode Control for Piezoelectric Micro-Positioning Stage

In order to further improve the positioning accuracy of piezoelectric micro-positioning stages, a discrete-time sliding mode controller with a prescribed performance function is designed based on the Hammerstein model. First, this paper proposes a new prescribed performance function, which can set the time at which the system reaches the steady state arbitrarily. Then, the error with constraint range is transformed into an unconstrained form by using the error transformation method, and a discrete sliding mode controller is designed. Finally, the control simulation is carried out using sine wave and triangle wave, and compared with the conventional discrete sliding mode controller. The simulation results show that the error of the piezoelectric micro-positioning stages can converge in the prescribed region, and the error of the proposed method is less than that of the discrete sliding mode control.
Car-Following Model of ULVs using a Deep Learning Model

In recent years, various types of vehicles have been developed and made available for the road. This study clarifies the driving characteristics of Ultra Lightweight Vehicles (ULVs) that consume less energy. First, coordinate data in units of 0.1 seconds are created by observing traffic flows including ULVs using unmanned aerial vehicles (UAVs). In addition, a car-following model is developed using the long short-term memory (LSTM) model, which has attracted attention as a deep learning model. Consequently, a model with a higher degree of fitness than a non-linear model is developed.
Recent Trends in the Reconfigurable Intelligent Surfaces (RIS): Active RIS to Brain-controlled RIS

Reconfigurable Intelligent Surfaces (RIS) have risen as a promising technology for future wireless communications. RIS consists of a massive number of unit cells, each of which can be tuned individually to induce a manipulation of the wireless signal properties. These properties include the signal's phase, magnitude, frequency, and even polarization. In this manuscript, we briefly present the recent trends in RIS research. This manuscript covers the trends in the development of active RIS, various RIS control elements comparison (i.e., electronics, mechanics, and materials approaches), flexible and transparent RIS, RIS lens and RIS mirror, and the most-recent brain-controlled RIS paradigm. This review was derived from the recent high-quality meta-analyses, reviews, and original research papers related to the recent RIS works. We extensively searched online libraries such as IEEE, Nature, Scopus, and Google Scholar for those articles. There were no limitations on the year of the publications. In this manuscript, we consider any research study types written in English (excluding unpublished data, technical notes, and submitted manuscripts). As a result, we have gathered more than 50 references related to the recent RIS research. While RIS promised attractive capabilities for reconfiguring the wireless environment, several challenges are lying ahead and must be overcome. Among those, there are two major challenges: multiplicative fading effect and channel estimation problems.
Molecular Formula Image Segmentation with Shape Constraint Loss and Data Augmentation

The increasing demand for molecular formula image data leads to formidable pressure for researchers. Most existing image segmentation approaches can not be directly utilized for molecules, and how to improve the coverage fineness and generate a large amount of labeled training data is worthy of further exploration. To this end, we establish a deep learning based molecular formula image segmentation model (DL-MFS). Specifically, we design a shape constraint loss (SCL) function to refine the detection frame position and propose a rule-based molecular formula image data augmentation method for solving the bottleneck problem that the lack of training data. Experimental results demonstrate the effectiveness of the proposed segmentation model.
Computational learning of small RNA regulation in pancreatic cancer progression

Based on the fast-accumulating genomics data, gene regulation network modeling has been the primary computational means to infer interactions between genes and their regulators, such as such Transcription factors (TFs) and microRNAs, to study (post-)transcriptional regulation in biological systems. However, learning dynamic behaviors from data in complex diseases constitutes a challenge task due to the chaotic interplay between regulatory mechanisms and the fact that those interactions vary over time. It is therefore crucial to build scalable learning models that can consider different types of regulations by leveraging heterogeneous data that captures each behavior while meaningfully treating each specie in its uniqueness. This work explores integrative approaches to gene network learning in human cancer, with a particular focus on microRNA-mediated regulation. Specifically, we introduce a learning framework that integrates expression and interactome information of RNA and fuses distinct graphical models to transform the prediction of static interactions into the identification of semi-conditional ones. Through analyzing data on human pancreatic cancer, we have identified distinct gene regulatory networks associated with four progressive stages, from which a list of 15 microRNA-gene interactions are found conditional to stages. The subsequent functional analysis reveals significant microRNA-mediated dysregulations in major cancer hallmarks, particularly in adaptive immune response and lymphocyte proliferation, which shed light on the pathological processes and regulatory roles that microRNAs play in the process of pancreatic cancer progression. We believe this integrative model can be a robust and effective discovery tool to facilitate the discovery of key regulatory characteristics in other complex biological systems.
Heartbeats Localization in Forcecardiography Signals Via Template Matching

Forcecardiography (FCG) is a novel cardio-mechanical monitoring technique that records the weak forces induced on the chest wall by the cardio-respiratory activity. Indeed, FCG sensors proved capable of capturing respiration, infrasonic cardiac vibrations, and heart sounds, all simultaneously from a single site on the chest. The high-frequency component of infrasonic vibrations (HF-FCG) has been found to be very similar to the Seismocardiogram (SCG). Accurate heartbeats localization is crucial to extract reliable diagnostic information for patient monitoring. In SCG processing, this operation is usually carried out by locating specific markers, often with the support of Electrocardiographic (ECG) recordings, which however prevents the applicability of such methods in purely cardio-mechanical monitoring contexts. In addition, the signal morphology is generally affected by respiration, which further complicates markers localization. This study presents a preliminary investigation on the performances of a novel template matching approach for heartbeats localization in HF-FCG signals. To this aim, simultaneous recordings of FCG and ECG signals were acquired from 5 healthy subjects at rest, during quiet breathing. The template matching was applied to localize the heartbeats and obtain inter-beat intervals estimates, which were then compared with those obtained from ECG. Linear regression and Bland-Altman analyses assessed the excellent accuracy of the simple, yet effective method here proposed, which scored 100% sensitivity and positive predictive value, an R2 value in excess of 0.999 and limits of agreement as low as $\pm$ 4.2 ms. The very promising, yet preliminary results of this study must be confirmed on a larger cohort of subjects.
Ultrahigh current-carrying capability in clean MgB2 films

We have studied the current-carrying capability of high quality epitaxial MgB2 films synthesized using the hybrid physical-chemical vapor deposition technique by both transport measurement in nanobridge constrictions and magnetization measurement. An extremely high self-field critical current density Jc(0)>108A∕cm2, approaching the theoretical depairing current of MgB2, was observed on a 150nm bridge, indicating an excellent current-carrying capability in these films. The magnetization measurement also showed very high Jc.
Towards Out-of-core Neural Networks on Microcontrollers

To run neural networks (NNs) on microcontroller units (MCUs), memory size is the major constraint. While algorithm-level techniques exist to reduce NN memory footprints, the resultant losses in NN accuracy and generality disqualify MCUs for many important use cases. To address the constraint, we investigate out-of-core execution of NNs on MCUs: dynam-ically swapping NN data tiles between an MCU's small SRAM and its large, low-cost external flash. Accordingly, we present a scheduler design that automatically schedules compute tasks and swapping IO tasks in order to minimize the IO overhead in swapping. Out-of-core NNs on MCUs raise multiple concerns: execution slowdown, storage wear out, energy consumption, and data security. Our empirical study shows that none of these concerns is a showstopper; the key benefit - MCUs being able to run large NNs with full accuracy/generality - trumps the overheads. Our findings suggest that MCUs can play a much greater role in edge intelligence.
Collaborative motion synchronization for affordable open-source cobots

Collaborative robots are increasingly used in several industries because of their safety and easy deployment for increased productivity. This paper presents the motion control synchronization methods for controlling dual six-axis serial manipulators using network socket and microcontroller interface. The sequence of steps for network-based cooperative robot control with dual six-axis serial manipulators is also presented in this work. The proposed affordable collaborative robotic motion control may be useful to control multiple serial manipulators, which are deployed for operations in the industries. Even though microcontrollers can perform high-performance edge computing for robot motion control, it is always accompanied by a large number of wires. However, network synchronization using the server-client interface has some latency and security issues. But it has its capability for large-scale integration and possible adoption of new technologies such as remote monitoring, cloud computing, etc.
Research on Part Edge Detection Algorithm Based on Deep Learning

When the traditional edge detection algorithm is used for edge detection of parts, it will be affected by its surface reflective properties, machining and grinding tool marks, and uneven light intensity, resulting in noise points, missing edge points and reduced edge detection accuracy. This paper proposes an improved deeplabv3+ network and a part edge detection algorithm that combines the most peripheral constraint algorithm. First, the improved deeplabv3+ network is used to obtain segmented images with obvious boundaries, and then the most peripheral constraint algorithm is used for edge detection. The experimental results show that the algorithm proposed in this paper can accurately extract the complete and single-pixel edge contour of the part under the interference of factors such as uneven illumination intensity, and there are no noise points and burrs. the RMSE reaches 13.36, the PSNR reaches 25.60, and the SSIM reaches 0.9852, which meets the requirements of part edge detection accuracy.
Neural Network Model-based Self-interference Cancellers for True Full-duplex Systems

True full-duplex communications have attracted increasing attention for potentially doubling the spectral efficiency, but the analog impairments in the transmitter and receiver hardware, which nonlinearly distort the signal, make it difficult to model and remove the self-interference. In this work, three different types of neural network model-based cancellers have been evaluated. Our results indicate that the NARX (Nonlinear auto-regressive exogenous) network canceller can achieve a peak cancellation performance of 9dB, similar to the conventional polynomial model but with 16.7% computations that the polynomial model requires.
MPGNN-DSA: A Meta-path-based Graph Neural Network for drug-side effect association prediction

Drug side effect is an important entity in the biomedical field, and identifying the association of the drug-side effects is a very important issue in pharmacological studies and drug risk-benefit. Traditional side effect discovery methods are mainly based on pharmacological experiments. These methods can detect the side effects of some drugs, but the identification process is time-consuming, expensive, and fails to identify some rare side effects. In recent years, with the expansion of massive biomedical data, computational-based methods are widely developed and applied for the task of drug-side effect association(DSA) prediction. However, existing methods cannot fully utilize public biomedical databases, and the complex semantic associations between drugs and side effects are not effectively captured, which leads to suboptimal model prediction performance. In this study, we develop a novel meta-path-based graph neural network model for drug-side effect association prediction. In the proposed model, we first construct a heterogeneous information network(HIN) by fusing multiple biological datasets. And then, a novel meta-path-based feature learning module is designed to learn high-quality representations of drugs and side effects. Finally, with the learned features, the prediction module utilizes a fully connected neural network to make prediction. In addition, comprehensive experiments is conducted, the results demonstrate the effectiveness of our model, indicating that the method will be a viable approach for DSA prediction tasks.
Real-time Control of UGV Robot in Gazebo Simulator using P300-based Brain-Computer Interface

Brain computer interface (BCI)-based virtual environment control has found broad applications in solving and pursuing factual healthcare issues concerning efficiency, safety, and costs. In this technical paper, an unmanned ground vehicle (UGV) robot with a simulator-equipped BCI system was utilized. The Gazebo simulator was employed to develop a simulated setting. The software CitySim World allowed rendering the simulated milieu more down-to-earth. A non-invasive electroencephalogram (EEG)-based BCI was used to follow the brain signals and extract the P300 component, a kind of simultaneous BCI controlling procedure for safe, fast, and inexpensive implementation. This UGV control system using human brain activity can be beneficial for the real UGV platform control. It enables the discovery of the probable errors in the physical implementation. All the steps implementing our BCI system were appropriately provided (data acquisition system, user interface design, BCI data architecture, ROS/ robot Jackal, and implementation and tests). Furthermore, the project implementation and some solutions to possible issues were posed. The project outcomes were assessed by employing BCI in a simulation; before implementing real-time, to determine errors and resolve the validity of the project scope.
Digital Noise Generator Based on Bernoulli Chaotic Map

This paper describes the noise emulation in materials through the digital implementation of a chaotic noise generator based on the Bernoulli map. The bifurcation diagram, Lyapunov exponent and the Ergodic Theorem were used in order to determine the operation conditions under which the Bernoulli map is able to generate noise with different statistical distributions. A 32-bits digital circuit was designed and implemented in a FPGA, and it can be used to emulate and analyze the noise in different materials. The obtained results are consistent with the generalized Bernoulli map behavior.
PR ] 2 5 M ar 2 01 0 Sojourn time in Z + for the Bernoulli random walk on

Let (Sk)k>1 be the classical Bernoulli random walk on the integer line with jump parameters p ∈ (0, 1) and q = 1 − p. The probability distribution of the sojourn time of the walk in the set of non-negative integers up to a fixed time is well-known, but its expression is not simple. By modifying slightly this sojourn time–through a particular counting process of the zeros of the walk as done by Chung & Feller [“On fluctuations in coin-tossings”, Proc. Nat. Acad. Sci. U.S.A. 35 (1949), 605– 608]–, simpler representations may be obtained for its probability distribution. In the aforementioned article, only the symmetric case (p = q = 1/2) is considered. This is the discrete counterpart to the famous Paul Lévy’s arcsine law for Brownian motion. In the present paper, we write out a representation for this probability distribution in the general case together with others related to the random walk subject to a possible conditioning. The main tool is the use of generating functions.
A Deep-Learning Method to Predicting Traffic Accidents Due to Drowse *

Many accidents occur as a result of driving sleepiness. It is becoming one of the leading causes of traffic accidents. According to recent data, many accidents were caused by driver tiredness. Thousands of people are killed in car accidents caused by drowsy driving. Drowsiness is responsible for more than 30 percent of all accidents. Driver sleepiness is one of the leading causes of accidents worldwide. Detecting the driver's sleepiness is one of the most reliable methods of determining driver weariness. To avoid this, a device that detects sleepiness and informs the driver is necessary, perhaps saving a life. In this study, we describe a system for detecting driver sleepiness. In this case, the driver is constantly observed through the camera. This model employs image processing algorithms that primarily concentrate on the car's motorist's face and eyes. The software analyzes the motorist's face and forecasts the motorist's eye blinking based on the eye area. To calculate Per close, we employ an algorithm that tracks and analyses drivers' features and eyes. If the blinking rate is too fast, the system notifies the driver with a sound.
Structure from Motion and Visual-Inertial Simultaneous Localization and Mapping for Three-Dimensional Reconstruction

The development of Structure from Motion (SfM) and visual-inertial Simultaneous Localization and Mapping (viSLAM) is gaining attraction. As a result, researchers figure out many methods and multiple versions of methods to solve the problem under various circumstances or within different cameras. SfM and SLAM are on two tracks but have a connection to the essence. The paper started by introducing the basic principles and methods of SfM and viSLAM, covering basic techniques and improvements to promote efficiency and stability when the input information varies from sequential to with depth information. This paper serves as a description and suggestion for whom wants to use SfM or viSLAM as three-dimensional reconstruction tools. This paper summarizes the focus of diverse methods and lists their outcomes to show the alteration or improvement. Then the pros and cons of methods are aggregated to give a specific comparison between reconstruction methods.
Tumor segmentation using CNN for automatic diagnosis of bone tumor in X-ray image

Bone tumors are often firstly diagnosed at the nearby hospital, because they cause bone pain and swelling. The number of patients are quite small (0.3” of population), and the identification of malignancy requires a great effort even for specialists. Therefore, there is a need to develop a system that can automatically extract bone tumor regions and estimate benign and malignant conditions. In this study, we propose a method for automatically extracting bone tumor regions using convolutional neural networks. We compared the segmentation performance of U-Net, DeepLab v3, and Mask R-CNN, and inspected the effect of tumor area cropping before semantic segmentation. Experiments using 118 knee X-ray images of 18 patients revealed that U-Net achieved the highest dice coefficient (0.67) in original x-ray image. In the experiment using cropped x-ray images, DeepLab v3 achieved highest 0.92 dice coefficient.
A Multifactorial Correction Method for Tumor Mutation Burden

The emergence of next generation sequencing (NGS) techniques has provided invaluable clinical information about the characteristics of cancerous tissue which is used to predict patient response to different therapies. Among these biomarkers is tumor mutation burden (TMB), which measures the frequency of mutation in tumor tissue and is established to predict patient response to immunotherapy. However, due to technical limitations, we observe a significant undercount of mutations in TMB. This non-detection rate is variable and corresponds to several factors unrelated to mutation frequency. We propose a novel method for correcting TMB to standardized baseline parameters using a multifactorial adjustment.
A Ku-Band Circular-Polarized Horn Antenna Based on a 3D Printed Polarizer

In this paper, a compact circular-polarized horn antenna is presented, based on a proposed 3D printed polarizer. The antenna consists of a transition between rectangular and circular waveguides, a septum polarizer for liner-to-circular polarization conversion, and a conical horn for radiation. By employing the 3D printing technique, the configuration printed with resin material will behave as the supporting structure, where the outer walls could be copper plated as boundaries; more importantly, the electric property of cured resin is also utilized to form a septum for phase delaying, finally realizing circular polarization (CP). At the feeding port of the designed horn, the simulation shows a superior reflection of -20 dB, covering the entire Ku band from 12 to 18 GHz. Accordingly, the axial ratio (AR) of CP is within 1.4 dB crossing the operational bandwidth, with realized gains between 9.8 and 12.9 dBi. The proposed work has the advantages of an integrated and compact configuration, light weight, and high fabrication accuracy but with low fabrication cost.
Event triggered control based on interval observer for discrete-time linear systems

This paper studies an Event Triggered Control (ETC) based on an interval observer for linear discrete time system subject to unknown bounded disturbance and measurement noise vectors. First, the analysis of the interval observer is presented. Then an Event triggered Control is designed using the two bounds of the interval observer to maintain system stabilization. The observer and ETC gains are calculated based on Lyapunov theory and convex conditions are formulated in Linear Matrix Inequality (LMI) form. Finally, the approach is illustrated by a numerical example to prove its effectiveness.