Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Arabic Computer-Based Screening Tool for Early Detection of Dementia

Accurate diagnostic tests rooted in neuropsychology are crucial for the detection of dementia in the elderly. Several studies have reported that detecting dementia at early stages is essential in enhancing the effectiveness of therapeutic intervention and delaying the disorder's progression. However, most neuropsychological studies in clinical practice require competent physicians' involvement. Advancements in technology have enabled the computerizing of screening tools, providing a faster and more convenient way to assess the common symptoms of dementia. However, currently, there is no Arabic computerized dementia screening software. Therefore, this study is carried out to present a computerized Arabic assessment tool that is versatile and easy to use. The proposed screening software was developed using MATLAB and consists of 28 questions with a total possible score of 47 points. A test was performed to evaluate four sensitive cognitive functions (memory, attention, visual and naming, and executive function). The computerized Arabic dementia screening tool was tested on five healthy subjects (4 males and 1 female, ages $61.80\pm 6.53$) to assess its practicality and ease of use. All participants were able to complete the tests on their own and reported no technical or linguistic difficulty. The proposed test has the potential in primary healthcare clinics, as it is easy to administer and can be supervised by healthcare providers with minimal training.
DanceU: motion-and-music-based automatic effect generation for dance videos

We propose a novel method, named DanceU, to automate dance video editing in an intelligent way. Our method fully leverages two folds of a dance video, the background music and the dancer’s motion, to determine where to add effects and what kind of effects to be added. We parameterize dance motion into two metrics: motion amplitude and motion intensity, to represent the motion characteristics of different dance types. Then, we design algorithms to align the dance motions with music beats and automatically decide dance effects for the dance video. To our best knowledge it is the first proposed method for automatic dance video effect generation. Results show that our method can generate fluent, performative, and music-matched effects, which achieves similar editing level to professionals. (A demo video: https://f.io/fgQUxehZ)
Speed Control of Variable Speed Wind Turbine Using Computational Intelligence Approaches

Wind power generation systems, which can be run at constant speed or variable speed, are gaining a lot of attention as clean and secure renewable energy sources. Due to an increase in the production of wind energy, variable speed generation systems are more appealing than fixed speed systems. A variable speed wind turbine should run as close as practical to its ideal power coefficient to maximise the harvest of wind energy. In this study, a fractional order PID controller with parameters set using computational intelligence techniques is used to regulate the speed of a two-mass mass wind turbine system. Particle Swarm Optimization and Genetic Algorithm are both used for the purpose of comparison. MATLAB simulation shows that, the proposed fractional order PID controller exhibits improved response in terms of shorter settling times, reduced overshoot as compared to conventional PID controllers.
Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regression

We present a mapping algorithm to compute large-scale magnetic field maps in indoor environments with approximate Gaussian process (GP) regression. Mapping the spatial variations in the ambient magnetic field can be used for 10-calization algorithms in indoor areas. To compute such a map, GP regression is a suitable tool because it provides predictions of the magnetic field at new locations along with uncertainty quantification. Because full GP regression has a complexity that grows cubically with the number of data points, approximations for GPs have been extensively studied. In this paper, we build on the structured kernel interpolation (SKI) framework, speeding up inference by exploiting efficient Krylov subspace methods. More specifically, we incorporate SKI with derivatives (D-SKI) into the scalar potential model for magnetic field modeling and compute both predictive mean and covariance with a complexity that is linear in the data points. In our simulations, we show that our method achieves better accuracy than current state-of-the-art methods on magnetic field maps with a growing mapping area. In our large-scale experiments, we construct magnetic field maps from up to 40000 three-dimensional magnetic field measurements in less than two minutes on a standard laptop.
Design of Digital Planner and 3D Vision System for Robot Bin Picking

Robot bin picking plays an important role in modern manufacturing process. In order to make these manufacturing systems more efficient and productive, it is essential to make a valid grasping plan from gripper design, industrial part recognition, pose estimation, to grasping evaluation. This paper proposes such a planning framework that enables the robot to learn to grasp an industrial part and improve the performance in two phases. First, prior knowledge of 3D model is utilized for gripper selection, database generation and grasping point evaluation in a design phase. Next, attempts for single part grasping are made in a test phase, and grasping failures will trigger the redesign in the previous phase. The grasping plan is then used for grasping randomly distributed parts. A risk assessment is made per part for selection of best candidate of parts, taking both grasping efficiency and potential collision into account. At last, pose adjustment is applied on the robot to improve grasping capability. Through simulation and field test, we demonstrate that the two-phase planning framework is a practical solution for robot bin picking applications.
IK-MLP-based Control for a Pneumatic Soft Manipulator

Pneumatic soft manipulators exhibit a mapping relationship between the output end-effector and the input pressure, which can be obtained by inverse kinematic modelling. The traditional inverse kinematic modelling method is complex for multiple degrees of freedom (DOF) soft manipulators, especially for a pneumatic soft robot. However, in traditional inverse modelling, a Jacobian matrix needs to be solved inversely, and a singular problem will be encountered. To address this issue, this paper proposes an Inverse Kinematics-Multilayer Perceptron (IK-MLP) method for soft manipulators. In this strategy, the trained intelligent controller can be applied to control pneumatic manipulators to grasp targets without establishing a traditional inverse kinematic model. Then, based on the ground experiment system of the soft manipulator, the control algorithm is experimentally tested by using the theoretical model and the physical system. And it is proved that the given intelligent controller adopting the IK-MLP method can effectively solve the inverse singularity. The results also show that the unidirectional turning manipulator has strong controllability of 83.2% grasping ratio.
Non-Contact methods for assessment of respiratory parameters

Respiratory parameters are very important signs that the human body shows about its well-being. These parameters have been a field of interest for different areas, ranging from healthcare to sports. The recent global pandemic situation has made the researchers to focus more on non-invasive or contactless monitoring of the respiratory parameters. This review paper tries to find out the technologies developed on the field of contactless respiratory monitoring systems. There were many reviews done on contact-based techniques for monitoring of respiratory parameters. But the reviews on contactless techniques are a few. Majority of the contactless techniques falls into four classifications: audio-based, camera-based, radio-based andlaser-based. Each classification is found effective in monitoring a certain set of parameters. The performance and suitability of these techniques are studied on different situations to find the better techniques for a set of conditions.
CAS-Net: Cascade Attention-Based Sampling Neural Network for Point Cloud Simplification

Point cloud sampling can reduce storage requirements and computation costs for various vision tasks. Traditional sampling methods, such as farthest point sampling, are not geared towards downstream tasks and may fail on such tasks. In this paper, we propose a cascade attention-based sampling network (CAS-Net), which is end-to-end trainable. Specifically, we propose an attention-based sampling module (ASM) to capture the semantic features and preserve the geometry of the original point cloud. Experimental results on the ModelNet40 dataset show that CAS-Net outperforms state-of-the-art methods in a sampling-based point cloud classification task, while preserving the geometric structure of the sampled point cloud.
Tactile-Based Object Pose Estimation Employing Extended Kalman Filter

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks.
Amplitude Mutual Information-Based Radar Waveform Design for Integrated Radar and Communication Systems

The integrated radar and communication systems can effectively solve the problem of lack of spectrum resources, which is a typical application of spectrum sharing technology. This paper considers the problem of amplitude mutual information-based radar waveform design for the integrated radar and communication systems in the same frequency band. The joint radar-communication mutual information (MI) is proposed to be utilized as the integrated performance metric for integrated systems. The joint radar-communication MI is the sum of radar amplitude MI and conditional communication MI with knowing radar channels. Based on this model, the MI and Neyman-Pearson (NP) criteria are considered to optimize the multi-carrier radar waveform under total transmit power and channel capacity constraints. We employ the waveform design method to investigate the performance of target detection. Numerical results demonstrate that the performance of target detection can be efficiently improved by exploiting the amplitude MI-based and NP-based criteria. It is also shown that the performance of MI-based method tends to be the same as MI-based method in high signal-to-noise ratio (SNR). The scattered part of the communication signal at the target can improve the target detection probability by 11.06% to 34.71%.
An Effective Parallel Acceleration Method for Transient Simulation of Large-Scale Power Ground Network

With the improvement of integrated circuit technique, chips have developed from large-scale to very large-scale integration (VLSI). The design of power/ground (P/G) network becomes a key factor limiting chip's rapid development, especially in the design of VLSI. Some classical algorithms, such as random walk, become difficult to perform transient simulation as the scale of P/G increases. In this paper, fast sparse matrix addition and distributed parallel acceleration method is proposed to accelerate transient analysis of P/G network without considering the specific topology of the network. Compared to the single-process solver, transient analysis of a P/G network example with around 5 million nodes can be accelerated 2.69X times by the proposed method, while keeping a high-precision solution.
Autonomous Emergency Landing on 3D Terrains: Approaches for Monocular Vision-based UAVs

With the increasing use of unmanned aerial vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs face numerous emergencies during missions, in these situations, the UAVs need to autonomously find a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. However, due to the limitations of computing power and sensors, it is challenging to achieve this goal. This research aims to explore the monocular emergency autonomous landing algorithm. Specifically, this work focuses on extracting depth and vision information. A topology information extractor is designed to convert image to graph and assess the connectivity of terrain. Additionally, a depth information extractor is designed to calculate the slope and roughness of the ground. A 3D topology optimizer is designed to optimize the graph by depth information and evaluate the landing suitability by a heuristic strategy. To make the action decision, a hybrid decision network (DGN) based on 3D topology information is proposed. Finally, this work build a simulated scenario based on real scene to verify the efficiency of DGN. The results of the experiment show that DGN outperforms its counterparts in terms of action prediction accuracy and landing success rate.
Accurate Timing Path Delay Learning Using Feature Enhancer with Effective Capacitance

Accurate timing path delay calculation is a critical technique during the process of chip design. For widely used path delay calculation methods, cell delay is captured from look-up table (LUT) and wire delay is calculated by classical Elmore model, which can be considered as first-order- moment matching approximation, efficient and easy to use but ignoring the resistive shielding. The wire delay calculated by commercial static timing analysis (STA) tools is still opaque and such lower-order moments delay have large errors compared with commercial STA tool. In this work, to address these limitations, we propose a learning-based timing path delay and wire delay prediction model with feature enhancer. Our model utilize the feature enhancer to calculate the wire delays with different classical wire delay metrics not only Elmore model, Delay with two Moments (D2M) model but also Effective Capacitance Metric (ECM) model and Modified D2M (MD2M) model, which captures resistive shielding by modeling the load capacitance with effective capacitance instead of total capacitance. Furthermore, an effective trick about label is setting the ratio of path delay from commercial tool to traditional method as label, which is critical for improving generalization and accelerating the convergence rate. Experimental results on multi-corner wire delay prediction demonstrate that our model using feature enhancer with effective capacitance achieves an average rRMSE values of 0.4% for trained designs and 3.3 % for unseen test designs.
The effect of cochlear nonlinearities on binaural masking level differences

Background The binaural masking level difference (BMLD) has been shown to be constant (10 − 15dB) for masker spectrum levels from 70dB/Hz down to 30 − 40dB/Hz and to gradually decrease with lower levels (McFadden, 1968; Hall and Harvey, 1984). The decrease at low levels was larger in an asymmetric condition where the masker was attenuated in only one ear. McFadden predicted the data by assuming that an external and an internal noise would interaurally decorrelate the internal representations of the stimuli. In the present study, the role of nonlinear cochlear processing and asymmetric masker level on the BMLD was investigated using an equalization − cancelation (EC) based binaural model framework.
Virtual Prototyping Process: Enabling Shipboard Sizing and Arrangement of a Power Electronics Power Distribution System

Naval shipboard power systems are complex and difficult to size and arrange due to their geographically distributed components. A modular Power Electronic Distribution System (PEPDS) built inside a Naval Integrated Power Electronic Corridor (NiPEC) has been proposed as a means of providing flexibility of power and energy delivery between sources and loads that significantly reduces electrical-thermal-structural overhead. In this study, a virtual prototyping process/that applies to PEPDS power trains is demonstrated. This approach enables the incorporation of digital twins of system building blocks, such as Power Electronic Building Block (PEBBs) and Integrated PEBB (iPEBBs) that have previously been reported on. The VPP ensures Pareto optimal integration of PEBB/iPEBB into PEPDS power trains and utilizes a unique dimensional compilation approach to account for the impacts of insulation coordination, repairability and thermal management on power density. This paper demonstrates Measure of Performance (MOP) outcomes of VPP and data management that can enable model based early-stage design exploration at the total ship level.
An Experiment on Dynamic Extraction of the Human Relationships on the Web Using Closeness Centrality

Opportunities to conduct advanced surveys and analysis using the Web have been increasing. In such works, an accurate understanding of the human relationships involved with the target of surveys/analyses contributes to their successful execution. However, the scope in which human relationships can be understood is limited due to the rapid expansion of the Web. We have aimed to develop methods for enabling observation of human relationships expressed in descriptions on the Web from various perspectives. In this paper, we describe methods for dynamically extracting human relationships on the Web using closeness centrality. We also describe an experiment to dynamically generate the human-relationship networks in accordance with people who the observer focuses on and discuss the effectiveness of our methods based on its results.
A $k-\text{Way}$ Partitioning Framework for Compression on Social Networks

Social networks have been studied in the context of matrices or graph theory for efficient representation, and manipulation. Knowledge gleaned from these graphs are useful to better coordinate events, advertise, and in recommendation of friends or games. Many social networks represented as graphs or matrices are large containing 100's of millions or even billions of entries. Hence it has become important to store them efficiently using data compression. The authors have used different kinds of compressed data structures such as quad-tree, array of binary trees (ABT), and differential, compressed binary trees (DCBT) in previous research for representing massive social networks and performing various queries [15]–[17]. In this paper, we provide a $k-\mathbf{way}$ partitioning framework that generalizes the array of binary trees structure by providing a combinatorial basis that is shown to lead to substantial improvements in compressing social network graphs.
Wearable 3D Shape Display for Dynamic Interfaces Rendering

Recreating the feeling of touch is crucial for seamless interactions with objects in the virtual world. Many haptic solutions exist in the forms of graspable, wearable, and touchable systems for recreating kinesthetic and tactile feedback. Yet, to the best of our knowledge, no wearable system to date can directly render dynamic shapes in user’s hand with drastic shape rendering capabilities. We present a wearable 3D haptic display with drastic shape change and dynamic signal rendering. We explored direct physical and dynamic rendering of shapes in users’ hands using a 3D lattice of pneumatic actuators-a direct-embodiment approach. We also conducted user studies to determine the efficacy of the shape and frequency rendering and found the results to be generally convincing.
Deep Multi-feature Fusion CNNs with Gemstone Image Classification Algorithm

Accurate gemstone classification is critical to the gemstone and jewelry industry, and the good performance of convolutional neural networks in image processing has received wide attention in recent years. In order to better extract image content information and improve image classification accuracy, a CNNs gemstone image classification algorithm based on deep multi-feature fusion is proposed. The algorithm effectively deeply integrates a variety of features of the image, namely the main color features extracted by the k-means++ clustering algorithm and the spatial position features extracted by the denoising convolutional neural network. Experimental results show that the proposed method provides competitive results in gemstone image classification, and the classification accuracy is nearly 9% higher than that of CNN. By deeply integrating multiple features of the image, the algorithm can provide more comprehensive and significant useful information for subsequent image processing.
Power Prediction of RTL-Level Circuits by Using Machine Learning

This paper proposes a power prediction framework by using machine learning (ML), which extracts the Hamming distance of the signals from the VCD file generated by RTL simulation as the power features, and uses the power values after placement and routing as the labels. On the 100G network processor (NP) chip adopting 28nm process, the circuit components are divided and the key signals are selected. The power model based on RTL design is constructed, and the power consumption of the system is predicted based on the method of component superposition. The existing experimental results show that the prediction error of component level and system level is less than 15% as a whole, and less than 10% in most cases.
A Stereovision EEG Channel Selection Method Based on Cross Increment Entropy Maximization

Electroencephalography (EEG) has been widely used in the research on stereo vision because it is a convenient neuroimaging technology. However, the multi-channel EEG signals make laboratory operations complicated, and the data analysis is also easily affected by redundant channels. Therefore, we proposed a method called Cross Increment Entropy Maximization (CIEM) for the selection of EEG channels. Results showed that the channels related to stereoscopic vision could be effectively preserved by CIEM. This method is significant for the removal of the interference of redundant channels and the facilitation of experimental process.
Plant Disease Detection using Vision Transformers on Multispectral Natural Environment Images

Enhancing agricultural practices has become essential in mitigating global hunger. Over the years, significant technological advancements have been introduced to improve the quality and quantity of harvests by effectively managing weeds, pests, and diseases. Many studies have focused on identifying plant diseases, as this information aids in making informed decisions about applying fungicides and fertilizers. Advanced systems often employ a combination of image processing and deep learning techniques to identify diseases based on visible symptoms. However, these systems typically rely on pre-existing datasets or images captured in controlled environments. This study showcases the efficacy of utilizing multispectral images captured in visible and Near Infrared (NIR) ranges for identifying plant diseases in real-world environmental conditions. The collected datasets were classified using popular Vision Transformer (ViT) models, including ViT- S16, ViT-BI6, ViT-LI6 and ViT-B32. The results showed impressive training and test accuracies for all the data collected using diverse Kolari vision lenses with 93.71 % and 90.02 %, respectively. This work highlights the potential of utilizing advanced imaging techniques for accurate and reliable plant disease identification in practical field conditions.