Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure Transmission Scheme for Smart Grid CPS with Digital Twin Architecture Based on Multi-Agent DRL

The CPS technology based on the digital twin provides a dynamic and accurate digital model and real-time control of equipment for the construction of the smart grid. However, integrating physical and information sides makes the power grid CPS system vulnerable to attacks from the network and physical sensors, which brings new challenges to communication security. From the perspective of collaborative, secure communication, this paper proposes a secure transmission scheme for power grid CPS based on physical layer security and deep reinforcement learning. Grid transmission nodes select idle trusted devices as relay transmission nodes. Signals are transmitted between nodes through narrow directional beams, which alleviates blocking effects and prevents eavesdroppers from obtaining good eavesdropping capacity. We use the multi-agent dual-dueling deep Q-network algorithm (D3QN) to optimize the selection of cooperative nodes, maximizing the total secrecy ability as the optimization objective. The simulation experiment proves that the proposed scheme can effectively improve the secrecy ability of CPS nodes.
Development of an Interactive Dashboard to Analyse Physiological Signals in the Neonatal Intensive Care Unit

Premature babies and those born with a medical condition are cared for within the neonatal intensive care unit (NICU) in hospitals. Monitoring physiological signals and subsequent analysis and interpretation can reveal acute and chronic conditions for these neonates. Several advanced algorithms using physiological signals have been built into existing monitoring systems to allow clinicians to analyse signals in real time and anticipate patient deterioration. However, limited enhancements have been made to interactively visualise and adapt them to neonatal monitoring systems. To bridge this gap, we describe the development of a user-friendly and interactive dashboard for neonatal vital signs analysis written in the Python programming language where the analysis can be performed without prior computing knowledge. To ensure practicality, the dashboard was designed in consultation with a neonatologist to visualise electrocardiogram, heart rate, respiratory rate and oxygen saturation data in a time-series format. The resulting dashboard included interactive visualisations, advanced electrocardiogram analysis and statistical analysis which can be used to extract important information on patients’ conditions.Clinical Relevance— This will support the care of preterm infants by allowing clinicians to visualise and interpret physiological data in greater granularity, aiding in patient monitoring and detection of adverse conditions. The detection of adverse conditions could allow timely and potentially life-saving interventions for conditions such as sepsis and brain injury.
Dynamic Coded Caching in Cellular Networks with User Mobility: A Reinforcement Learning Method

Coded caching manages to release cellular network traffic by increasing transmission rate via satisfying multiple user requests simultaneously. Specific contents stored in the private cache memory are used as side information to decode individual requests from the coded broadcasting messages. Considering local content popularity could improve caching performance dramatically. However, in the mobility scenario, local content popularity varies with user movements. Even worse, contents in the cache memory might become outdated when the user location changes. In this paper, we propose a dynamic coded caching scheme that reduces the loss of coded caching gain due to the user movement and local content popularity dynamic changing. We quantify the relationship between user preference, local popularity, and user mobility. We formulate a metric to measure the performance of the proposed coded caching scheme and propose a reinforcement learning problem to obtain the cache replacement strategy in the mobility scenario. Numerical results verify that our obtained replacement policy significantly outperforms the popularity-based, least-frequently-used, and multilayer replacement policy in terms of traffic offloading.
Trajectory-Based SLAM for Indoor Mobile Robots with Limited Sensing Capabilities

In this paper we introduce a novel SLAM system for 2-D indoor environments that relies only on limited sensing. Our fully autonomous system uses only the trajectory of the robot around walls and objects in the environment as landmarks and is capable of robust and long-term exploration and mapping of a broad range of household floor plans. Rank-deficient and full-rank factors are created when the robot observes existing trajectory-based landmarks, and they are filtered and added in a pose graph, which is optimized periodically. The mission space is mapped by efficient adaptive local mapping algorithms. The proposed SLAM system has been extensively tested in various scenarios, and experimental results show its robustness and accuracy.
Dynamic Behavior of Gas Bubbles and Droplets in Magnetic Fluids in Microfluidic Chips of Various Configuration in Inhomogeneous Magnetic Field

Droplet formation in microchannels has a large number of applications such as microdosers and gas meters. The paper investigates the effect of the wettability of the walls of a microfluidic chip on the for generation of microdrops in the channel. It is shown that the flow speed and value of magnetic field strength have an effect on the size of droplets and bubbles formed in a ferrofluid in a microfluidic chip.
An Improved Variable Parameter Phase-Locked Loop for Single-Carrier Underwater Acoustic Communications

A direct adaptive equalizer (DAE) coupled with a phase-locked loop (PLL) has been proved as an efficient receiver scheme for single-carrier coherent underwater acoustic (UWA) communications. Ever since its birth, numerous improvements have been proposed for the DAE. In contrast, the PLL has been somehow overlooked. A PLL with predetermined coefficients suffers performance degradation when channel conditions change, then a variable-parameter (VP) PLL designed via gradient descent (GD) method was proposed. The GD-VP-PLL is sensitive to parameter initialization though. In this paper, we propose a new VP-PLL scheme in light of the recursive Gauss-Newton (RGN) method. The resulting RGN-VP-PLL has been tested by at-sea experimental data, and showed better performance than the GD-VP-PLL and a PLL with fixed coefficients. Moreover, it is insensitive to initialization of parameters.
Surgical Continuum Manipulator Control Using Multiagent Team Deep Q Learning

Continuum manipulator has shown great potential in surgical applications. The flexibility of the continuum manipulator helps it achieve many complicated surgeries, such as neurosurgery, vascular surgery, abdominal surgery, etc. In this paper, we propose a Team Deep Q learning framework (TDQN) to control a 2-DoF surgical continuum manipulator with four cables, where two cables in a pair form one agent. During the learning process, each agent shares state and reward information with the other one, which namely is centralized learning. Using the shared information, TDQN shows better targeting accuracy than multiagent deep Q learning (MADQN) by verifying on a 2-DoF cable-driven surgical continuum manipulator. The root mean square error during tracking with and without disturbance are 0.82mm and 0.16mm respectively using TDQN, whereas 1.52mm and 0.98mm using MADQN respectively.Clinical Relevance—The proposed TDQN shows a promising future in improving control accuracy under disturbance and maneuverability in robotic-assisted endoscopic surgery.
An Iterative Post-Processing Method for Speech Source Separation in Realistic Scenarios

The purpose of this paper is to design a speaker-independent Blind Source Separation (BSS) system which aims to reduce the word error rate (WER) metric on Persian speech data. The main idea behind this method is that it tries to improve the quality of the output of any baseline separation system with the help of an iterative model by removing the remained parts of the interferer speaker from each source. For this purpose, we use embedded representations of the input speech signals. In addition, our system benefits from a convergence metric that aims to purify the output signals. To evaluate the proposed method, we have collected a dataset that contains about one hour of real phone calls from landline phones. Although most of the energy of some consonant phonemes appears in high-frequency bins which are filtered in telephony speeches, our method can handle this condition by properly removing the interference. Experimental results based on different metrics have proved the effectiveness of the proposed method.
Multi-Task Learning U-Net for Functional Shoulder Sub-Task Segmentation

The assessment of a frozen shoulder (FS) is critical for evaluating outcomes and medical treatment. Analysis of functional shoulder sub-tasks provides more crucial information, but current manual labeling methods are time-consuming and prone to errors. To address this challenge, we propose a deep multi-task learning (MTL) U-Net to provide an automatic and reliable functional shoulder sub-task segmentation (STS) tool for clinical evaluation in FS. The proposed approach contains the main task of STS and the auxiliary task of transition point detection (TPD). For the main STS task, a U-Net architecture including an encoder-decoder with skip connection is presented to perform shoulder sub-task classification for each time point. The auxiliary TPD task uses lightweight convolutional neural networks architecture to detect the boundary between shoulder sub-tasks. A shared structure is implemented between two tasks and their objective functions of them are optimized jointly. The fine-grained transition-related information from the auxiliary TPD task is expected to help the main STS task better detect boundaries between functional shoulder sub-tasks. We conduct the experiments using wearable inertial measurement units to record 815 shoulder task sequences collected from 20 healthy subjects and 43 patients with FS. The experimental results present that the deep MTL U-Net can achieve superior performance compared to using single-task models. It shows the effectiveness of the proposed method for functional shoulder STS. The code has been made publicly available at https://github.com/RobinChu9890/MTL-U-Net-for-Functional-Shoulder-STS.Clinical Relevance— This work provides an automatic and reliable functional shoulder sub-task segmentation tool for clinical evaluation in frozen shoulder.
High-accuracy NLOS identification based on random forest and high-precision positioning on 60 GHz millimeter wave

60 GHz millimeter wave (mmWave) system provides extremely high time resolution and multipath components (MPC) separation and has great potential to achieve high precision in the indoor positioning. However, the ranging data is often contaminated by non-line-of-sight (NLOS) transmission. First, six features of 60GHz mmWave signal under LOS and NLOS conditions are evaluated. Next, a classifier constructed by random forest (RF) algorithm is used to identify line-of-sight (LOS) or NLOS channel. The identification mechanism has excellent generalization performance and the classification accuracy is over 97%. Finally, based on the identification results, a residual weighted least squares positioning method is proposed. All ranging information including that under NLOS channels is fully utilized, positioning failure caused by insufficient LOS links can be avoided. Compared with the conventional least squares approach, the positioning error of the proposed algorithm is reduced by 49%.
Towards Simultaneous Noninvasive Arterial and Venous Oxygenation Monitoring with Wearable E-Tattoo*

While noninvasive arterial blood oxygenation is easily estimated using peripheral pulse oximeters, noninvasive venous blood oxygenation monitoring is still a critical unmet need. Critical conditions that lead to inefficient extraction of oxygen from the blood, such as sepsis or shock, can only be detected by analyzing the oxygen content of the venous blood. In this work, we introduce a soft wearable e-tattoo sensor that simultaneously measures the arterial and venous pulses from the wrist. First, we prove that the origin of the signal is venous pulsatility. We hypothesize that a significant obstacle for simultaneous SaO2 and SvO2 extraction is the close proximity of the artery and vein, thus leading to crosstalk. We characterize this crosstalk with simulation, in vitro, and in vivo experiments. Finally, we offer a potential solution for minimizing the crosstalk through spatial filtering.Clinical Relevance— This lays foundational work for a novel method of noninvasively and simultaneously measuring arterial and venous blood oxygenation to improve clinical diagnoses of sepsis, shock, and metabolic abnormalities.
Di-5-ANEQ(F)PTEA Offers Better Performance than Di-4-ANEQ(F)PTEA for In-Situ Cardiac Optical Mapping*

Cardiac optical mapping has traditionally been performed in ex-vivo, motion-arrested hearts. Recently, in-situ cardiac optical mapping has been made possible by both motion correction techniques and long-wavelength voltage sensitive dyes (VSDs). However, VSDs have been observed to wash out quickly from blood-perfused in-situ hearts. In this study, we evaluate the performance of a newly developed VSD, di-5-ANEQ(F)PTEA, relative to an earlier VSD, di-4-ANEQ(F)PTEA. We find that di-5-ANEQ(F)PTEA persists over 3 times longer, produces improved signal-to-noise ratio, and does not prolong loading unacceptably.Clinical Relevance—Optical mapping has provided many insights into cardiac arrhythmias, but has traditionally been limited to ex-vivo preparations. The present findings extend the utility of optical mapping in the more realistic in-vivo setting and may eventually enable its use in patients.
Symphony of Sensors: The Harmonious Art of Massive Data Generation by Devices

In today's interconnected world, devices of all shapes and sizes orchestrate a grand symphony of data generation, producing an unparalleled cacophony of information that prevail through every aspect of our lives. This paper takes center stage to explore the intricate choreography of this symphony, shedding light on the diverse cast of devices, the rhythms of data generation, and the significant implications for society. Through a harmonious blend of analysis and imagination, we unravel the melodies of innovation, challenges, and opportunities that arise from the massive data generation by devices.
Detection of spreading depression features from the scalp of epileptic patients

Spreading depression (SD), a pathological cortical negative DC potential, is caused by an elevation of potassium ions in the extracellular space. This leads to a transient relocation of ions within neurons and a slow spread through brain tissue. Our previous research established a correlation between scalp SD and seizures in patients with intractable epilepsy using our novel electroencephalography (EEG). In this study, we enhanced our EEG system by incorporating a Near-infrared spectroscopy (NIRS) module for multi-modal EEG-NIRS measurements. The aim is to provide an investigation into the defining characteristics and methods for detecting SD.Clinical Relevance—: The detection of SD serves as a novel biomarker for epilepsy, capable of forewarning seizures within a time range from 10 secs to 30 min. This detection plays a crucial role in predicting and preventing seizures and providing diagnostic information for drug-resistant epilepsy patients.
Deadline Sensitive And Function Placement In Multi-tier Serverless Platform

Serverless architecture has emerged as a distributed computing paradigm that raises the question of how to efficiently place functions in a distributed environment to meet deadlines and optimize resource utilization. This paradigm, also known as Function-as-a-Service (FaaS), provides businesses with the advantage of relinquishing resource and infrastructure management, allowing them to focus on their core business logic. Infrastructure and resource provisioning becomes the responsibility of cloud service providers. This proposal aims to address the challenge of function placement in a hierarchical or distributed edge-cloud environment, specifically for time-sensitive tasks where meeting deadlines is critical, considering the heterogeneity of compute nodes in terms of CPU, RAM, and memory. Our approach involves exploring the optimal strategy for function placement by utilizing offline data collection techniques and employing a non linear machine learning model for the placement strategy. To investigate this problem, we focus on deadline-sensitive Function within the context of a video surveillance camera application. We examine their placement across heterogeneous edge-cloud nodes, taking into account factors such as network latency and computation latency. The scope of our study is primarily centered around the deadline of tasks, the hierarchical nature of the edge-cloud infrastructure with geographically distributed nodes, the placement of functions, and the heterogeneity of serverless nodes. To simulate the execution environment, we employ Docker containers. By exploring an effective function placement strategy in a distributed edge-cloud environment, this research contributes to the optimization of resource utilization and timely execution of time-sensitive tasks within a serverless architecture.
Soft, Modular, Shape-Changing Displays with Hyperelastic Bubble Arrays

Incorporating compliance into shape-changing displays can improve their wearability and actuation modalities. While recent advances in soft actuators highlight promising paths for soft shape-changing displays, these displays currently face some practical challenges of device failure and limited actuator displacement. A monolithic fabrication processes means the device is challenging to repair, for a single point of failure often renders the whole device ineffective. We have leveraged a modular hyperelastic bubble array layer to create a soft shape-changing skin. The modularity of this device allows for rapid repair of individual bubbles and fast prototyping, and the spherical, hyperelastic actuators enable an increase in degrees of freedom due to bubble-to-bubble interactions. Furthermore, we present a forward kinematic description of our device, incorporating these bubble-to-bubble interactions and the nonlinear instabilities unique to hyperelastic actuator inflation. We demonstrate the utility of this soft shape-changing skin as a haptic display that can be worn comfortably or applied to passive interactive objects such as a computer mouse.
MVI-Wise GAN: Synthetic MRI to Improve Microvascular Invasion Prediction in Hepatocellular Carcinoma

MRI is crucial for the diagnosis of HCC patients, especially when combined with CT images for MVI prediction, richer complementary information can be learned. Many studies have shown that whether hepatocellular carcinoma is accompanied by vascular invasion can be evidenced by imaging examinations such as CT or MR, so they can be used as a multimodal joint prediction to improve the prediction accuracy of MVI. However, it is high-risk, time-consuming and expensive in current clinical diagnosis due to the use of gadolinium-based contrast agent (CA) injection. If MRI could be synthesized without CA injection, there is no doubt that it would greatly optimize the diagnosis. Based on this, this paper proposes a high-quality image synthesis network, MVI-Wise GAN, that can be used to improve the prediction of microvascular invasion in HCC. It starts from the underlying imaging perspective, introduces K-space and feature-level constraints, and combines three related networks (an attention-aware generator, a convolutional neural network-based discriminator and a region-based convolutional neural network detector) Together, precise tumor region detection by synthetic tumor-specific MRI. Accurate MRI synthesis is achieved through backpropagation, the feature representation and context learning of HCC MVI are enhanced, and the performance of loss convergence is improved through residual learning. The model was tested on a dataset of 256 subjects from Run Run Shaw Hospital of Zhejiang University. Experimental results and quantitative evaluation show that MVI-Wise GAN achieves high-quality MRI synthesis with a tumor detection accuracy of 92.3%, which is helpful for the clinical diagnosis of liver tumor MVI.
FPGAs-based Edge Computing with Distributed Leader Election on Internet of Things

This paper presents a flexible architecture for the Internet of Things (IoT) with edge computing based on Field Programmable Gate Arrays (FPGAs) and distributed leader election (DLE). An enhanced DLE algorithm is introduced which implicitly monitors the communication nodes on the wireless sensor networks (WSNs) and chooses the appropriate leader to improve the reliability of the system. At the edge computing layer, devices rely on hardware description language to leverage the versatility and parallel processing capabilities of FPGAs. We also perform simulations and hardware implementation to evaluate the performance of specific tasks. Numerical results evidence the rise of the leader and how the data from network nodes are utilized to distinguish signals from noise successfully.
Interaction between helophytes and endophytic bacteria: their synergistic effects in bioremediation of contaminants

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An Approach to Design a Biomechanically-Inspired Reward Function to Solve a Patience Cube Under Reinforcement Learning Framework

This paper presents an approach to design a reward function by adopting both control theoretic and biomechanical perspectives. In reinforcement learning (RL), a reward function plays a crucial role for an RL agent training; especially, a task learning time and a task performance. Accordingly, designing a reward function becomes a key issue to train an RL agent generating human-like policy/strategy to perform dexterous manipulation. Since human beings are good at producing heuristic approaches to complete a given task, determining a set of basis functions as well as corresponding weights used not to be so straightforward. In this study, we consider solving a patience cube as an example of a dexterous manipulation task. In our approach, we first employed a quadratic regulator form as a backbone of a desired reward function. Next, the kinematic data of a controlled object and the sEMG data of a human expert were measured while performing a demonstration to solve a patience cube. Then, from the measured data, the weights of the basis functions were determined by utilizing muscle synergy extraction and inverse optimal control as two key tools. Finally, an RL agent was trained by the designed reward function and comparative analysis versus the other RL agents trained by prototypical weight settings was followed. The result showed that the RL agent trained by our approach yielded human-like learning curve as well as policy successfully and outperformed the others in terms of a task success rate and a task completion time. These findings substantiated the feasibility of extending our approach to an assistive robotic manipulator or prosthesis design to perform the activities of daily living.