Miracle-dz/UniFAR-FTUs huggingface dataset:
Channel Capacity for Adversaries With Computationally Bounded Observations

We study reliable communication over point-to-point adversarial channels in which the adversary can observe the transmitted codeword via some function that takes the <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-bit codeword as input and computes an <inline-formula> <tex-math notation="LaTeX">$rn$ </tex-math></inline-formula>-bit output for some given <inline-formula> <tex-math notation="LaTeX">$r \in [{0,1}]$ </tex-math></inline-formula>. We consider the scenario where the <inline-formula> <tex-math notation="LaTeX">$rn$ </tex-math></inline-formula>-bit observation is computationally bounded – the adversary is free to choose an arbitrary observation function as long as the function can be computed using a polynomial amount of computational resources. This observation-based restriction differs from conventional channel-based computational limitations, where in the later case, the resource limitation applies to the computation of the (adversarial) channel error/corruption. For all <inline-formula> <tex-math notation="LaTeX">$r \in [0,1-H(p)]$ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$H(\cdot)$ </tex-math></inline-formula> is the binary entropy function and <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is the adversary’s error budget, we characterize the capacity of the above channel and find that the capacity is identical to the completely oblivious setting (<inline-formula> <tex-math notation="LaTeX">$r=0$ </tex-math></inline-formula>). This result can be viewed as a generalization of known results on myopic adversaries and on channels with active eavesdroppers for which the observation process depends on a fixed distribution and fixed-linear structure, respectively, that cannot be chosen arbitrarily by the adversary.
An Approach for Designing Patient-Specific Prosthetic Aortic Valves Based on CTA Images Using Fluid-Structure Interaction (FSI) Method

Background: Currently, the efficacy of prosthetic valves is highly dependent on the geometry of the aortic root. With the development of computational fluid dynamics, patient-specific modeling and simulation of prosthetic heart valves increasingly become the forefront of current research. Methods: In this study, we propose an approach for designing patient-specific prosthetic aortic valves using fluid-structure interaction (FSI) analysis. Firstly, the aortic root model was reconstructed from the computed tomography angiography (CTA) images. We then implemented a parametric valve design. The leaflets were determined by the attachment curve, the free edge, and the belly curve. We selected six key points, including three points located at the ends of the free edge, and three points located at the bottom of the attachment curve. This allowed for designing various leaflet geometries. Furthermore, each valve model was simulated using the FSI method. We evaluated deformation, effective orifice area (EOA), and stress to study its performance. Conclusion: The simulation results showed that patient-specific valves demonstrated a large EOA and proper stress, indicating satisfactory performance. Our study investigated an approach for designing patient-specific prosthetic aortic valves and evaluated hemodynamic parameters. This provides promising insights into the design of prosthetic aortic valves and potentially serves as a valuable tool to assist patient-specific aortic valve replacement.
Smart Grid vs. Intelligent Grid; Artificial Intelligence Takes the Power System to the Next Level

This paper discusses the concept of intelligent power systems, particularly the distinction between smart grids and intelligent grids. Smart grids represent a convergence of various energy technologies and communication systems, enabling multidomain interactions. However, in such a grid, humans are still the ultimate decision maker based on the gathered information. By adding artificial intelligence to the system, smart grids can be upgraded to intelligent grids. This article explores the cutting-edge methods and propositions of machine learning and artificial intelligence, and their influence on a plethora of smart grid applications, aiding their transition to truly intelligent grids. The piece highlights the surge in interest and rapid advancement in employing machine learning and AI techniques to effectively tackle technical obstacles across various facets of the smart grid. However, it notes unresolved issues meriting additional research, such as high-performance data processing for informed decision-making in large, intricate multi-energy systems, and lightweight solutions based on machine learning. Additionally, the article spotlights the potential of harnessing progressive computing and communication technologies such as edge computing, pervasive internet of things, and 5G wireless networks within the smart grid framework. Simulating smart grids requires overcoming challenges posed by the integration of diverse subsystems with varying time scales. Co-simulation is employed for these cases. This paper presents the state-of-the-art in simulating intelligent power systems and addresses challenges in applying co-simulation to test cases spanning various domains, including information and communication technology (ICT) and power systems. We anticipate that this study will serve as a launching pad for continued exploration and advancement in this burgeoning area of knowledge and practice.
Identifying Spatiotemporal Dispersion in Catheter Ablation of Persistent Atrial Fibrillation: A Comparative Study of Machine Learning Techniques Using Both Real and Realistic Synthetic Multipolar Electrograms

Atrial fibrillation (AF) is a common heart condition affecting the elderly population and is a significant risk factor for strokes, making it a growing public health concern. Catheter ablation (CA) is the most effective long-term treatment for persistent AF. Recently, a novel CA approach based on spatiotemporal dispersion (STD) has been proposed. This technique targets STD patterns associated with active zones responsible for sustaining the arrhythmia. We present three datasets to be used to train and test different machine learning models in automatically identifying STD patterns from multipolar electrograms (EGM). Two different real dataset have been acquired from Nice Pasteur University Hospital and labelled by experts. To address the challenging scenario presented by the real data, a synthetic dataset has been created generating EGM records resembling real-world scenarios, using openCARP cardiac electrophysiology simulation software. We evaluate 13 machine learning techniques to demonstrate the challenging scenario of the real data, and we analyze their performance in the proposed datasets. Results show that the synthetic data are promising as training set for classifiers evaluated on real data, but a deeper statistical analysis is necessary to confirm these findings.
Outlier Detection in ECG

Automatic abnormal ECG detection algorithms are crucial for treating heart health problems and saving lives. This study's objective is to verify whether using unsupervised learning methods and more specifically probabilistic and deep learning models such as autoencoders, variational autoencoders (VAE), diffusion Models, partial shift variational autoencoder (our implementation of half VAE half prediction model), normalizing flows and Gaussian mixture models to detect outliers in ECG data is possible. An outlier for our case is an abnormal ECG signal, a one belonging mostly to a sick person while the normal case is that of a normal healthy person. The results have shown that the models distinguish between normal and abnormal data to a specific degree, with the VAE achieving an area under the curve (AUC) of the receiver operating characteristic curve score of 0.85 on the publicly available PTB-XL dataset and 0.83 on the publicly available CPSC dataset. Moreover, the VAE achieved anAUC of 0.89, 0.80 and 0.81 when distinguishing between normal and conduction disturbance, myocardial infarction, and STIT Change respectively. This indicates that a VAE when optimized itself and fed with more proper data may be able to be used in medical applications.
Design and Implementation of Beam Steering Using 8×8 Butler Matrix on X-Band Array Antenna for AESA Radar

The beam steering system which consists of rectangular array antenna and $8 \times 8$ Butler Matrix are presented. This system can work properly at 9.4 GHz for AESA radar of the fighter aircraft usage. The antenna has eight linear arrays with 40 single patches and was successfully implemented since the average value of return loss in each linear array is 32.18 dB. The Butler Matrix also can do beam steering. The main lobe position, when the input port (from first until eighth port) of Butler Matrix is supplied, leads to 50°, 250°, 80°, 300°, 40°, 310°, 60°, and 310°. Both antenna and Butler Matrix are implemented using FR-4 epoxy as the substrate. While in simulating the Butler Matrix, it uses two different substrates there are FR-4 epoxy and Duroid (RT 5880) to compare the performance.
Automated photonic resonator absorption microscope for point of care biomarker detection

We describe an automated biosensing microscope capable of measuring assays performed on photonic crystal (PC) biosensor surfaces using gold nanoparticle tags for detecting nucleic acid and protein biomarkers. The system incorporates a 3-axis computer-controlled motion stage, autofocus, large-area image tiling, and real-time monitoring of dynamic processes.
Hybrid Vision-Force Control for Human Robot Co-Carrying on Ball and Board Systems

We propose a hybrid vision-force control that combines vision servo control with force control to perform a robot collaborative by human carrying task on a ball-and-board systems. The proposed control allows the robot to effectively cooperate with human by successfully moving the board to the desired target position, while actively avoiding the ball from falling off the board. A RBFNN-based force control is designed to ensure the robot is able of complying with human motion under uncertain robot dynamics. Visual servo control based on image recognition and location is used to generate reference height and angle to adjust the position and posture of the board to keep the ball on the board. The experiment illustrates that the proposed technique is practical and effective to perform human-robot co-carrying on a ball-and-board systems.
Detecting Preload Changes Using Seismocardiography

Seismocardiography (SCG) is a non-invasive tool that utilizes the chest wall vibrations from the beating heart to measure cardiac movements through a highly sensitive accelerometer. SCG offers continuous measurement of cardiac function that could be utilized for monitoring, diagnostic assessments, and prognostic health checks. The aim of this study was to investigate if the SCG could detect an increase in preload by intravenous saline infusion. Twenty-six subjects were included in this study, sixteen with cardiac disease and ten healthy subjects without known cardiac disease. Post infusion the diastolic SCG amplitudes D<inf>d</inf> increased 27% (p=.016), E<inf>d</inf> increased 48% (p = <.000) and the systolic L<inf>s</inf> increased 19% (p = <.015). Diastolic time intervals B<inf>d</inf> to F<inf>d</inf> decreased with 10% (p =. 010)% (p =. 040). The same was observed for early systolic time intervals E<inf>s</inf> to G<inf>s</inf> that decreased 4% and E<inf>s</inf> to L<inf>s</inf> that decreased 14% (p = <.000) However, the mid systolic interval G<inf>s</inf> to B<inf>d</inf> increased 5%, L<inf>s</inf> to B<inf>d</inf> increased 16% and B<inf>d</inf> to E<inf>d</inf> increased with 1%. The results found in this study demonstrates a significant difference in the SCG measures after preload augmentation, thus indicating that the SCG could potentially be utilized to detect preload changes, for example signs of decompensated heart failure.
Power Delay Profile Measurement Applied MUSIC for Local 5G System

Power delay profile for Local 5G system at sub-6 band is measured. Modulation method of transmitted signal is not changeable in this case generally. We are not able to use more flexible modulated transmission signals which are suitable for measuring a power delay profile of Local 5G systems. We have already proposed the more flexible measurement method using two antennas for reference signal near transmission point and received signal at observation point. The IFFT function is used in conventional method. The subjects of low time-resolution and high level sidelobe caused by zero-padding are remained in conventional case. We apply MUSIC algorithm with time-oversampling and with increasing data and snapshots for higher time-resolution and low sidelobe characteristics. Measurement results of power delay profile for Local 5G at sub-6 band by using proposed method are shown in this report.
Fairness-Aware Scheduling Optimization for NB-IoT in LEO Satellite Networks Using a 3D Spherical Coordinate System

This article focuses on optimization problems in military scenarios of the narrow-band Internet of Things (NB-IoT)-low Earth orbit (LEO) satellite networks. A 3D model of the LEO satellite is presented using a spherical coordinate system, enabling the determination of a practical elevation angle for the ground-to-satellite channel model. This study aims to maximize the minimum average throughput by formulating an optimization problem that incorporates the association between IoT devices and LEO satellites. To solve this problem, an iterative algorithm based on successive convex approximation algorithms is proposed. Simulation results confirm the effectiveness of the proposed optimal approach and show up to 59.51% of effective throughput improvement compared to conventional approaches while ensuring fairness between IoT devices. In addition, the article highlights important findings through top-view 2D and 3D snapshots, demonstrating optimal decisions using the proposed method.
Acquiring Accurate Quality Digital Entities For Digital Twins within Virtual Reality

Creating digital entities (3D models) of digital twins for smart installations requires a number of considerations, especially when developing these to run on standalone virtual reality (VR) platforms. This paper provides an overview of the methods used for creating these digital entities for use with the Virtual Immersive, Remote Sensing, and Actuation (VIRSA) system. It explores conventional and cutting-edge techniques such as 3D scanning, laser-based surveys, photogrammetry, and procedural modeling. Additionally, the paper includes a thorough cost comparison between outsourcing and in-house model creation, considering factors such as initial investment, ongoing expenses, quality control, and scalability. Future possibilities in digital entity creation are discussed, envisioning advancements in this area. By equipping stakeholders with this knowledge, the research aims to optimize digital twin development, fostering innovation and enhancing the effectiveness of smart installations in various industries and applications.
A High-PSRR and Curvature-Compensated Subthreshold Voltage Reference Without Special Devices or Resistors

In this paper, a high power supply rejection ratio (PSRR) and curvature-compensated subthreshold voltage reference free of special devices and resistors is presented. The proposed voltage reference (VR) exhibits four excellent properties: high PSRR, low temperature coefficient (TC), reduced power consumption and compact layout area. By utilizing cascode current mirror, a decorrelating transistor, a low pass filter and a three-branch structure, both low and high frequencies present high PSRR. In order to reduce the TC of the VR, a proportional to absolute temperature (PTAT) current obtained from an inverse diode is added to an active load, thus achieving curvature compensation. Furthermore, the VR consists solely of MOSFETs, most of which operate in subthreshold region, so the VR occupies a very small area and consumes fairly low power. The proposed VR is designed in a standard TSMC 180 nm process and occupies 0.032 mm$^{2}$ active area. The simulation results demonstrate that the proposed VR operates desirably over a supply range of 1 V to 3.5 V with a line sensitivity of 0.047 %/V. The TC is 18.4 ppm$/^{\circ}\mathrm{C}$ with a temperature range of -$30 \sim 120$ only consuming a quiescent current around 46 nA, and the PSRR approximates -80 dB at 100 Hz, and -45 dB at 1 GHz owing to special circuit structures.
Lumination-property characterization for InP/ZnSe/ZnSeS/ZnS quantum dots with variable temperature

Red-emissive InP/ZnSe/ZnSeS/ZnS quantum dots (QDs) with multiple shells were fabricated and evaluated with a lumination-property-characterization system with variable temperature. The proposed system is capable of monitoring luminous efficiency, light intensity, optical spectra, and temperature variation of the QD solution with heating equipment. The luminous efficiency of the 611-nm QD sample decreases to 0.35 under 140°C and reverts to 0.69 under the ambient temperature (RT). The light intensity and fluorescent lifetime of the 611-nm sample show a smaller variation after heating in comparison with that of the 649-nm sample, which possesses a larger core during the synthesizing process. The proposed illumination property characterization system can effectively evaluate QDs with different excitation and temperature conditions. Our research results on InP/ZnSe/ZnSeS/ZnS QDs under various temperatures can provide guidelines for the application of InP QDs in display, illumination, and communication.
Identifying Noisy ECG Signals in Large Datasets Using a Temporal Convolutional Neural Network Trained to Estimate Pseudo-SNR

Background: Electrocardiogram (ECG) signals are often contaminated by noise. Manual review of large ECG databases to identify noisy signals is time-consuming. Traditional signal quality assessment algorithms often do not generalize well or are computationally expensive. This study developed a Temporal Convolutional Neural Network (TCNN) to estimate the signal-to-noise ratio (SNR) of ECG signals. Method: We trained a TCNN on a proprietary database of 134,019 12-lead ECGs without any machine or human-added noise labels. Assuming that this data had high SNR, we randomly selected a single lead from each ECG and added random Gaussian noise. We then scaled the signals and added noise to give a negatively skewed normal distribution of true SNR values. We trained a TCNN to regress low- and high-frequency pseudo-SNR values from the raw noisy input signals. Results: On the testing dataset, the TCNN achieved a mean error of $0.31\pm 1.80\ dB$ and a Pearson correlation coefficient of 0.96 for low-frequency pseudo-SNR. Similarly, for high-frequency pseudo-SNR, the mean error was $0.29\pm 1.63\ dB$ and the Pearson correlation coefficient was 0.97. Conclusion: A Temporal Convolutional Neural Network can accurately estimate the SNR of unseen ECGs.
Design of 40Gb/s PAM4 Signal SerDes Low Power CMOS 64:4 MUX

This paper designs a low-power CMOS 64:4 MUX circuit for 40Gb/s PAM4 signal SerDes system. The circuit uses UMC28nm process with a 1.05V voltage. The circuit structure is composed of 4-stage cascaded 2:1 MUX and configurable clock module. Clock control sampling of 2:1 MUX per stage is used in a configurable clock scheme to guarantee maximum and provide clock drive. To reduce power consumption, 5Latch and 3Latch 2:1 MUX schemes are used. For different transmission rates, each stage 2:1 MUX using different structures to implement. Improving the robustness of the circuit, CMOS 64:4 MUX enables the conversion of 64-bit 625Mb/s parallel data into 4-bit 10Gb/s serial data under various PVT conditions. Abstract: This article designs a low-power CMOS 64:4 MUX circuit for the 40Gb/s PAM4 Signal SerDes system. The circuit adopts UMC28nm technology and a 1.05V power supply voltage. The circuit structure is composed of a 4-stage cascaded 2:1 MUX circuit and a configurable clock module. Using a configurable clock scheme, clock control sampling is performed on each stage of 2:1 MUX to ensure maximum timing margin and provide clock drive. In order to reduce power consumption, the 5Latch and 3Latch 2:1MUX scheme is adopted. For different transmission rates, 2:1MUX at each stage is implemented with different structures, which improves the robustness of the circuit. Under various PVT conditions, CMOS 64:4 MUX achieves the conversion of 64 bit 625Mb/s parallel data into 4 bit 10Gb/s serial data.
Age of Critical Information: Optimizing Data Freshness Based on Content Criticality

Age of Information (AoI) is an important metric to measure information freshness in IoT applications. However, traditional AoI only measures information freshness from its generation time and does not take into consideration of the relative significance in the information content. To address this limitation, we consider the scenario where information can be classified based on its criticality so that we can focus on freshness of critical information. We introduce a new metric called Age of Critical Information (AoCI) to quantify the elapsed time between the present and the generation time of the head-of-line (HOL) queued (untransmitted) critical information sample at its source. We study an AoCI scheduling problem at the base station (BS), with an objective of minimizing the maximum (minmax) proportion of time when AoCI exceeds a target threshold among all sources. For this problem, we first derive an important property regarding the rates of violating AoCI thresholds and the sum of all rates under an optimal scheduler. Guided by this property, we propose Janus—a low-complexity scheduling algorithm to our problem. Through extensive performance evaluation, we show Janus meets our design objectives and significantly outperforms a well-known scheduler (EDF).
Infant Vomit Detection Using ShuffleNetV2

Infants often experience phenomena such as vomiting during the neonatal period. Typically, vomiting is when the food or milk in the baby's stomach comes out of the mouth after feeding, usually due to poor digestion or overeating. Therefore, this paper proposes a monitoring system to assist parents in being aware of whether their infant is vomiting even when they briefly divert their attention. The system consists of two stages: first, the utilization of YOLO5FACE for detecting the infant's face and capturing the baby's mouth region. Image processing techniques enhance color saturation in the captured mouth images. And then, ShuffleNetV2 extracts image features, and finally, frame differencing is utilized to determine if vomiting has occurred. Parents can be immediately notified of abnormal conditions by promptly detecting such incidents. The proposed method successfully and rapidly detects vomiting in videos with an accuracy of 98.30%.
Privacy Preserving Techniques and Deployment Types in Unmanned Aerial Systems

Unmanned Aerial Vehicles (UAVs) have garnered significant attention within the research community in recent years, with applications spanning from civilian to military use cases. The data generated by UAVs is susceptible and vulnerable to privacy breaches. Consequently, preserving privacy is crucial to safeguard this data against unauthorized access, and service providers must address this concern. This article establishes classification categories based on the required level of privacy preservation and provides an overview of the privacy-preserving techniques employed in Unmanned Aerial Systems (UAS). Additionally, the article offers insights into implementing privacy-preserving strategies across different levels, namely Type1, Type2, and Type3 in UAS. These deployment types can be combined to achieve a heightened level of privacy per the specific requirements. Lastly, the article compiles a table listing the attacks that privacy-preserving techniques can address and highlights the future challenges in this field.