Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges and Future Trends of EEG as a Frontier of Clinical Applications

The non-invasive techniques for diagnosis are rapidly increasing due to technological advancement in medicine. The analysis of physiological signals reveals information about the state of human health. Among these, electroencephalography (EEG) is commonly used in neuroscience for a wide range of operations that acquire electrical activities of brain. Human behavior during different psycho-physiological states can be studied using EEG. In fact, EEG has been found to be useful in a number of clinical applications. This review mainly presents various clinical prospects of EEG. The genesis of EEG is discussed along with its spectral behavior. In addition, various preprocessing approaches for artifacts removal are briefly discussed. The common features such as time, frequency, and non-linear parameters are also stated that reveal underlying information in EEG which is useful for both supervised and unsupervised classification problems. The processed EEG can be useful for the following clinical applications: seizure detection, psychological assessment, cognitive development, anesthesia monitoring, polysomnography, drowsiness detection, and brain computer interface. Although the non-invasive approach is highly beneficial in medicine, accuracy and reliability of such system is always an issue. To overcome these challenges, sophisticated and highly intelligent instrumentation techniques with convenient experimental setup needs to be developed which can attract consumers with broad spectrum usage of EEG.
Early Identification of Diabetic Retinopathy Using Deep Learning Model: A Survey

Diabetes mellitus and its symptoms are causing severe and even life-threatening difficulties in the lives of people all over the world. A side effect of diabetes mellitus called diabetic retinopathy causes an eye condition that can impair vision. The important aspect of treatment is that if it is detected early, then disease progression can be slowed by controlling modifiable risk factors (such as blood glucose and blood pressure), and laser therapy or treatment with vascular endothelial growth factor inhibitors can be used to reduce the risk of visual loss. Early identification of diabetic retinopathy is aided by retinal screening. Recently, deep learning methods are becoming massively widely used to facilitate automatic retinal screening in the retina's fundus. Deep learning methods are more effective in the early detection of deep retinal retinopathy. To evaluate the effectiveness of deep learning approaches for detecting diabetic retinopathy, we conducted a systematic review, including a meta-analysis of distinct deep learning methods and several types of classifiers from some recent pertinent research work. This survey will be useful for technical people and academics who wish to strive to improve the diagnostics of a system that will be more powerful in practice.
MSL3D: Pointcloud-based muck pile Segmentation and Localization in Unknown SubT Environments

This article presents MSL3D, a novel framework for pointcloud-based muck pile Segmentation and Localization in unknown Sub-Terranean (Sub-T) environments. The proposed framework is capable of progressively segmenting the muck piles and extracting their location in a global constructed point cloud map, using the autonomy sensor payload of mining or robotic platforms. MSL3D is structured in a two layer novel architecture that relies on the geometric properties of muck piles in underground tunnels, where the first layer extracts a local Volume Of Interest (VOI) proposal area out of the registered point cloud and the second layer is refining the muck pile extraction of each VOI proposal in the global optimized point cloud map. The first layer of MSL3D is extracting local VOIs bounded in the look-ahead surroundings of the platform. More specifically, the ceiling, left and right walls as well as the ground are continuously segmented using progessive RANSAC, searching for inclination in the segmented ground area to keep as the next-best local VOI. Once a local VOI is extracted, it is transmitted to the second layer, where it is converted to the world frame coordinates. In the sequel, a morphological filter is applied, in order to segment ground and nonground points, followed by RANSAC once again to extract the remaining points corresponding to the right and left walls. In this approach, Euclidean clustering is utilized to keep the cluster with the majority of points, which is assumed to belong to the muck pile. The efficacy of the proposed novel scheme was successfully and experimentally validated in real and large scale SubT environments by utilizing a custom-made UAV.
Onset of centrifugal instability at a rotating cylinder in a stratified fluid

In this experimental note, we consider the centrifugal instability of a laminar shear layer, generated by the impulsive start of the rotation of a circular solid cylinder about its vertical axis immersed in a linearly stratified fluid. The flow is determined by the Reynolds number, Re, based on the cylinder rotation rate and the cylinder radius, and the Froude number, Fr, represented by the ratio of the rotation frequency Ω over the buoyancy frequency N. The onset of the instability starts when the boundary layer reaches a certain thickness. We show for this boundary layer that there is a transition from the centrifugally unstable regime to a wave-like regime at Fr ≈ 1 and a stable flow below a critical Reynolds number. We focus on the centrifugally unstable regime Fr⪆1, for which the onset time and wavelength are predicted by scaling laws that depend on the Reynolds number. Agreement with the theoretical prediction of Kim and Choi [“The onset of instability in the flow induced by an impulsively started rotating cylinder,” Chem. Eng. Sci. 60, 599–608 (2005)] in a homogeneous fluid confirms that the instability of this boundary layer is not modified by the presence of stratification. These results therefore show that the centrifugal instability of the spin-up boundary is dominated by inertial motions, suggesting that close lateral boundaries, as in thin-gap stratified Taylor-Couette flow, increase the effects of buoyancy on the instability and wavelength.In this experimental note, we consider the centrifugal instability of a laminar shear layer, generated by the impulsive start of the rotation of a circular solid cylinder about its vertical axis immersed in a linearly stratified fluid. The flow is determined by the Reynolds number, Re, based on the cylinder rotation rate and the cylinder radius, and the Froude number, Fr, represented by the ratio of the rotation frequency Ω over the buoyancy frequency N. The onset of the instability starts when the boundary layer reaches a certain thickness. We show for this boundary layer that there is a transition from the centrifugally unstable regime to a wave-like regime at Fr ≈ 1 and a stable flow below a critical Reynolds number. We focus on the centrifugally unstable regime Fr⪆1, for which the onset time and wavelength are predicted by scaling laws that depend on the Reynolds number. Agreement with the theoretical prediction of Kim and Choi [“The onset of instability in the flow induced by an impulsively started r.
Bee colony optimization algorithm for routing and wavelength assignment based on directional guidance in satellite optical networks

With the development of satellite communication, in order to solve the problems of shortage of on-board resources and refinement of delay requirements to improve the communication performance of satellite optical networks, this paper proposes a bee colony optimization algorithm for routing and wavelength assignment based on directional guidance (D-BCO-RWA) in satellite optical networks. In D-BCO-RWA, directional guidance based on relative position and link load is defined, and then the link cost function in the path search stage is established based on the directional guidance factor. Finally, feasible solutions are expanded in the global optimization stage. The wavelength utilization, communication success probability, blocking rate, communication hops and convergence characteristic are simulated. The results show that the performance of the proposed algorithm is improved compared with existing algorithms.
Security Implications of a Satellite Communication Device on Wireless Networks Using Pentesting

The notion that ships, marine vessels and off-shore structures are digitally isolated is quickly disappearing. Affordable and accessible wireless communication technologies (e.g., short-range radio, long-range satellite) are quickly removing any air-gaps these entities have. Commercial, defence, and personal ships have a wide range of communication systems to choose from, yet some can weaken the overall ship security. One of the most significant information technologies (IT) being used today is satellite-based communications. While the backbone of this technology is often secure, third-party devices may introduce vulnerabilities. Within maritime industries, the market for satellite communication devices has also grown significantly, with a wide range of products available. With these devices and services, marine cyber-physical systems are now more interconnected than ever. However, some of these off-the-shelf products can be more insecure than others and, as shown here, can decrease the security of the overall maritime network and other connected devices. This paper examines the vulnerability of an existing, off-the-shelf product, how a novel attack-chain can compromise the device, how that introduces vulnerabilities to the wider network, and then proposes solutions to the found vulnerabilities.
Frequency-Sensitive Soil Moisture Profiling Using WiFi Sensing

In recent times, WiFi signals have been widely used in wireless sensing applications for detecting large-scale environmental or physical characteristics, like human count and ambient temperature. Soil moisture detection using WiFi sensing is gaining interest, but the key underlying challenge is to realise contact-free sensing technology that can characterise the impact of water spreading into the soil on the Channel State Information (CSI). Therefore, we develop a framework for sensing soil water levels using CSI- based sensing. We investigate the WiFi CSI signatures pertinent to the soil water infiltration, enabling applications including soil health monitoring. In our experimental study, we use the 5GHz WiFi spectrum to implement our novel frequency-selective CSI sensing framework for soil moisture profiling using commodity Raspberry Pi devices. The experimental results verified that specific WiFi Orthogonal Frequency Division Multiplexing (OFDM) subcarriers are more sensitive to the changes in soil moisture, leading to a frequency-sensitive behaviour on CSI. Our framework exploits the changes in the pressure levels due to the water movement or varying humidity levels in the soil channel between the WiFi transmitter and receiver that leave impressions on the underlying CSI. Furthermore, this paper demonstrates a novel approach by inspecting the CSI amplitude pattern of water entering the soil at a finer level. Lastly, in contrast to the existing works, our low-cost and contact-free method for detecting soil moisture detection has been empirically shown to efficiently utilise the newly-identified frequency-selective CSI signatures for the water infiltration and humidity levels in soil channel for accurate soil moisture prediction.
Network Common Mode EMI Attenuation Using Bandwidth Indexing

Power converters are networked together in a tightly packed area to enhance power handling capacity and power density. While each power converter is designed to adhere to electromagnetic interference (EMI) requirements when standalone, the EMI aggregate when multiple converters operate together could exceed current electromagnetic compatibility (EMC) standards. EMI solutions including filters and chokes are designed specifically for standalone converters. There is a need for a solution that can reduce the EMI aggregate in a network environment without attaching additional heavy, bulky, and costly filters. Hence, this paper proposes a novel method for suppressing Common Mode (CM) EMI for multiple power converters in a large-scale network by utilizing sinusoidal frequency modulated carrier wave (SFMCW) bandwidth indexing. Using this method, different switching frequency bands are assigned to each converter in the network in order to minimize switching frequency overlap, while simultaneously spreading energy across the discrete narrow bands of each converter to reduce aggregate EMI. Results of experimental testing validated CM EMI attenuation between [2–10dBuV] for two parallel half-bridge inverters using Gallium Nitride (GaN) switching devices. The proposed method reduces aggregate CM EMI without requiring redesign of individual converters or adding filter devices to increase weight, volume, and form factor.
A comparison of human skeleton extractors for real-time human-robot interaction

Modern industrial manufacturing procedures gradually integrate physical Human-Robot interaction (pHRI) scenarios. This requires robots to understand human intentions for effective and safe cooperation. Vision is the most commonly used sensor modality for robots to perceive human behavior. In this paper, we compare various vision-based human skeleton extraction frameworks, to provide guidance for the design of human-robot interaction applications. We run various skeleton extractors on a video of a person working with the help of a dual-arm collaborative robot, in a scenario simulating a typical human-robot workspace. By comparing the outcomes of the various skeleton extractors, we justify our choices according to pHRI constraints.
AI-Assisted Resource Allocation for Improved Business Efficiency and Profitability

The emergence of smart devices has raised demand for numerous computational paradigms, including cloud computing, fog computing, and the Internet of Things. But even in these paradigms, efficient resource allocation is still difficult. This article provides a thorough assessment of the literature on the use of machine learning (ML) and deep learning (DL) techniques in computational paradigms for resource allocation optimization. As far as we are aware, there are no studies of resource allocation methods based on AI in various computing paradigms. The analyzed machine learning-based methods are divided into two categories: multi - layer perception and evolutionary computation. DL-based techniques are also surveyed, as well as how they work in conjunction with RL. An examination of unexplored study areas and a conclusion round out the review.
Reduced Operational Inhomogeneities in a Reconfigurable Parallelly-Connected Battery Pack Using DQN Reinforcement Learning Technique

Battery cells that are placed in parallel in order to increase capacity are commonly considered single-series cells. In reality, there exist unavoidable variations between cells due to manufacturing processes as well as operational conditions that create current and State of Charge (SOC) inhomogeneities. If these inhomogeneities are not taken into consideration, accelerated degradation may occur causing early decommissioning of battery packs. Literature review reveals that reconfigurable battery packs are capable of dealing with these inhomogeneities, however, that a lack of demonstrated intelligent control methods exists. Thus in this work, a novel reconfigurable battery pack topology for reducing SOC and current inhomogeneities in a parallelly connected battery pack using a Reinforcement Learning (RL) Deep Q-Network (DQN) is presented. Results show that the RL-DQN based switch controller can reduce both current and SOC imbalances over time between parallel battery cells, especially in lower degradation variation battery packs and under lower operational current rates.
Cybersecurity Oriented Architecture to Ensure the Autonomous Vehicles Communication

The topic of in-vehicle and V2X communication in autonomous vehicles consists of a variety of different communication protocols, mechanisms, and devices. The implementation and cooperation between these entities and protocols in such a complex system is a rigorous and complicated process that should not only be efficient, robust, flexible, and scalable, but also secure. The security of critical systems such as autonomous vehicles requires a deep understanding of all the individual and distinct components that compose the system. This paper presents a cybersecurity architecture having as purpose to shield the communication security in the autonomous vehicles. For this reason, several well-established cybersecurity tools (e.g. Keycloak, Cloudflare) and communication mechanisms (e.g. MQTT, Kafka) have been combined in this architecture along with a novel statistical-based Intrusion Detection System. All the aforementioned cybersecurity defense mechanisms were selected to protect the entire system pipeline and meet the requirements for Confidentiality, Integrity, and Availability regarding vehicle communication. To test the performance of the proposed architecture abnormal data have been injected to the system and the results from the experiments conducted highlighted that the proposed solution can achieve its purpose of increased cybersecurity.
Visual Imitation Learning for robotic fresh mushroom harvesting

Imitation Learning holds significant promise in enabling the automation of complex robotic manipulations tasks which are impossible to explicitly program. Mushroom harvesting is a task of high difficulty requiring weeks of intense training even for humans to master. In this work we present an end-to-end Imitation Learning pipeline that learns to apply the series of motions, namely reaching, grasping, twisting, and pulling the mushroom directly from pixel-level information. Mushroom harvesting experiments are carried out within a simulated environment that models the mushroom dynamics based on von Mises yielding theory with parameters obtained through expert picker demonstration wearing gloves with force sensors. We test the robustness of our technique by performing randomization on the camera extrinsic and intrinsic parameters as well as on the mushroom sizes. We also evaluate on different kinds of visual input namely grayscale and depth maps. Overall, our technique shows significant promise in automating mushroom harvesting directly from visual input while being remarkably lean in terms of computation intensity. Our models can be trained on a standard Laptop GPU in under one hour while inference of an action takes less than 1.5ms on a Laptop CPU. A brief overview of our experiments in video format is available at: https://bit.ly/41kCH7T
Towards Explainable Table Interpretation Using Multi-view Explanations

Table interpretation (TI), which aims to predict the column types and relations of tables, plays an essential role in necessary decision-making actions for data management systems. Typically, TI is followed by a manual verification, where experts manually verify the correctness of TI’s predictions. Manual verification is able to ensure the quality of decision-making actions but labor-intensive. To reduce the labour costs, providing explanations for TI’s predictions is necessary as these explanations can help them do faster and more accurate verification. However, existing TI approaches overlook the manual verification process and lack explainability1. To fill this gap, this paper explores the challenging explainable table interpretation problem, which aims to provide faithful explanations and meanwhile achieve high prediction performance. Furthermore, we propose ExplainTI framework. ExplainTI consists of two phases: (i) tables are converted to sequences and lightweight column graphs; and (ii) a pre-trained transformer encoder is fine-tuned to provide multi-view explanations and aggregate contextual information. Extensive experiments on both real Web tables and database tables confirm that ExplainTI outperforms competitive baselines. Moreover, systematical analysis of explainability demonstrates that our framework can provide faithful explanations to facilitate the manual verification process.
Automated Cross Channel Temperature Predictions for the PFR Lime Kiln Operating Support

The Parallel Flow Regenerative (PFR) lime kiln process is challenging with respect to the energy efficiency, product quality and production stops, due to the inability of the human operators to accurately predict the evolution of the process. Monitoring and controlling of such processes encounter several issues, related to the high mass and heat inertia of the process, data quality, production stops, operator’s experience, as well as unknown exogenous factors (e.g., quality of the fuel, and raw material properties). Hence, an automated control/optimization mechanism for properly configuring the process is not straightforward. In this paper, we present a selection of mechanisms for data preprocessing together with domain specific feature analysis that allow for capturing the short-term changes of the critical parameters of the process. Through these mechanisms, automated predictive modeling can be performed that can be used by the kiln operator or a predictive-based controller to modify fuel feed strategies to meet energy efficiency and product quality requirements. We validate the proposed data-based preprocessing and modeling approaches through experiments in real-world data sources.
A PCA-based Method to Map Aesthetic Movements from Dancer to Robotic Arm

The research field of motion is shared by both robotics and dance for both their respective pragmatic and aesthetic purposes. Dance is an art form which uses the graceful motion of the performer's body to create an expression, while robotics focuses on the motion control of articulated bodies. We propose an approach to combine the graceful motion of a dancer and the robotic motion of an articulated arm to create a unique choreography. Our mapping approach is based on the results of a principal component analysis of dancer motion, projected onto the degrees of freedom of the robotic arm. The challenge here is to find a kinematic relationship between two distinct kinematic structures, a dancer's body and a robotic arm, to create an enhanced aesthetic experience. Various joint assignment policies will be examined and compared to identify the characteristics that optimize the audience's opinion of the robot's movement aesthetics and the appeal of the human-robot choreography.
CLDG: Contrastive Learning on Dynamic Graphs

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://github.com/yimingxu24/CLDG.