Miracle-dz/UniFAR-FTUs huggingface dataset:
Navigating the Nexus: Exploring the Fusion of AI and Nanotechnology for Cutting-Edge Advances

The convergence of artificial intelligence (AI) and nanotechnology has initiated a transformative journey, introducing innovative possibilities across various fields. This review article explores the dynamic interaction between these two disciplines, emphasizing their collaborative potential, notable accomplishments, and prevailing challenges. The integration of AI algorithms with nanotechnology has expedited material discovery, nanoscale characterization, personalized medical applications, and nano- electronics. AI's predictive capabilities have revolutionized the discovery of novel nanomaterials with customized properties, the enhancement of drug delivery systems, and the design of intricate nano-electronic components. However, this synergy is accompanied by challenges such as data quality, multidisciplinary cooperation, ethical considerations, and computational complexity. The fusion of AI and nanotechnology is poised to transform industries, unlock quantum advancements, and influence sustainable materials. As AI's capacities evolve, scholars are prepared to address challenges and harness the transformative potential of AI- nanotechnology integration.
Exploring Gender Differences in Motor Imagery EEG for Brain-Computer Interface Applications*

Brain-computer interface (BCI) technology demonstrated immense potential across diverse fields. However, current research on electroencephalogram (EEG) commonly assumed single model can be universally applied across all gender identities. While a few studies have identified gender differences through supervised classification of resting-state EEG, the majority have largely overlooked the potential impact of gender-specific differences in BCI applications. This study explored the gender-specific differences in motor imagery (MI) within BCIs and the feasibility of gender recognition in unsupervised settings. We utilized public datasets of male and female EEG signals, applied widely used machine learning algorithms for task and gender identification. The results showed that the average MI classification accuracy for female was 0.57% higher than male, despite the dataset containing more male subjects. In addition, gender recognition accuracy from EEG MI data exceeded 97%. These findings have highlighted the importance of considering gender-specific differences in BCI research and application. The results of this study could inform the development of more personalized effective BCIs in healthcare and other fields, ultimately leading to improved outcomes and experiences for users of all genders.
Every dark cloud has a colored lining

This dissertation focused on two topics: (1) the relation between positive and negative affect, and (2) the relation between neuroticism, extraversion and depressive symptoms on the one hand, and affective reactivity to positive and negative events on the other hand. These topics were investigated using the TRacking Adolescents’ Individual Lives Survey (TRAILS), in which more than 2000 adolescents were followed from age 11 to age 23. In addition, we used two studies in which early adolescents and students were asked about experienced events and affect 5 to 9 times a day in their daily life. We found that depressed mood and anhedonia (lack of pleasure) predicted each other across time. High scores on neuroticism or depressive symptoms and low scores on extraversion were not associated with decreased positive affect reactivity to positive events at the same moment, nor with decreased reactivity at a subsequent time point. We found tentative evidence that the effects of positive events on positive affect were slightly larger for individuals higher in depressive symptoms. These results concern minor events in daily life. Major positive events have the potential to exert a beneficial effect on mood, yet only when the total amount of events is limited.
Comparing the Effect of Different Electrode Subsets on P300 Speller Performance

The P300 speller is a widely used application in brain-computer interface research. It has been demonstrated that the P300 speller can serve as a neurofeedback training tool for attention enhancement by gradually increasing the difficulty of the spelling task. This adaptive approach makes it harder for users to spell words correctly, encouraging them to improve their attention to counteract the increasing difficulty. Therefore, the adaptive P300 speller has the potential to serve as a treatment option for children with ADHD, elderly patients with dementia, and as a cognitive enhancement tool for healthy adults. However, the training length, including setup time, needs to be quick to ensure user acceptability. This study investigates the effect of different electrode subsets on P300 speller performance, with and without the use of the xDAWN spatial filter. Results indicate that the xDAWN spatial filter can improve performance with many electrodes but can decrease results with fewer than eight electrodes. For scenarios where near-perfect performance is crucial and many electrodes are available, a set of 16 electrodes with the xDAWN spatial filter is recommended. For situations where cost and setup time are a concern and lower performances are acceptable, using six electrodes without the spatial filter were found to be sufficient.
Mixed Integer Quadratic Programming and Change-Point Detection based Framework for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) can help in assessing the energy consumption behaviour of households in a cost-efficient way. This paper proposes a mixed integer quadratic programming (MIQP) based NILM framework in which appliances are modelled through their operating states, and the NILM problem is solved for segments in the aggregated power consumption profile. The appliance operating states and the segments are obtained with the iterative K-means clustering (IKMC) algorithm and the change point detection (CPD) framework, respectively. The optimization formulation is regularised with the $l_{1}$-norm based penalty term, which promotes sparsity in the appliance operating states switching vector. Case studies are performed on the AMPds, REDD and the UK-DALE datasets and the performance of the proposed NILM framework is compared with the state-of-the-art NILM models. The comparison studies show that the proposed framework is more accurate and computationally efficient than the other models, even with the low sampling rate datasets.
Model Predictive Control of a Modified Nested Neutral-Point Clamped Inverter

In this article, a modified nested neutral-point clamped (MNNPC) inverter with a five-level (5L) operation is proposed. The 5L-MNNPC inverter has a smaller number of flying capacitors (FCs) and has redundancy switching states, which allows better controllability of the FCs voltage compared with the existing 5L-NNPC topologies. In the proposed MNNPC inverter, each FC voltage should be maintained at one-fourth of the net dc-bus voltage to produce a 5L output voltage waveform. The proposed topology’s structure and its operation are discussed and analyzed thoroughly. Also, a model predictive control (MPC) approach is introduced to control the proposed 5L-MNNPC inverter’s ac currents and FC voltages. The sampled-data models are developed to implement the MPC method for the proposed inverter. On the basis of the multi-objective cost function, the optimal switching state will be determined from the assessment of all available switching states. The MATLAB simulation studies are presented to demonstrate the proposed 5L-MNNPC inverter’s functionality and its performance.
Formation Method of Multiple Unmanned Surface Vehicles Based on Ring Topology Structure

In this paper, the problem of multiple USV(unmanned surface vehicle)s forming a regular polygon formation is studied. Based on the leader-follower approach, this article proposes a method for designing the communication topology in mobile ad hoc network to form a formation for USVs. A two-dimensional particle vehicle motion model with containment control method is established. Based on the motion model, the USV cluster is formed by the ring topology. Specifically, this paper proves that in the multi-USV containment control, due to the rotation symmetry of the ring topology, the shape of the followers’ convergence in the convex hull is consistent with the convex hull, and explains that under the formation of the USV cluster guided by the ring topology, the multi-USV cluster can be formed into a regular multilateral formation. Finally, the effectiveness of the method is verified by simulation experiments.
Predicting local distortions introduced by AV1 using Deep Features

Semantics extracted by filters in deep learning networks correlate well with how human eyes perceive distortions. These methods (e.g., LPIPS, PieAPP, etc.) rely on the relative difference in activation between feature maps in pairs of references and distorted patches. However, Deep Feature extraction can be expensive to compute as a difference of latent code between reference and distorted frames. Therefore, it is challenging to integrate them into the decision process of modern video codecs like AV1, making thousands of encoding trials during exhaustive Rate-Distortion Optimization (RDO) searches. In this study, we present a method using deep features to predict the distortion perceived locally by human eyes in AV1-encoded videos. The prediction relies on Deep Features extracted from the reference frame only to weigh the Mean Squared Error (MSE) introduced during encoding. This approach will make integration into video codecs easier as a pre-processing step before starting encoding. We show the superiority of the proposed metric against other Reference-Only metrics on a dataset of local distortions in videos. We achieve comparable performance as state-of-the-art Full-Reference video quality metrics.
Augmented Matrix Construction Based DOA Estimation of Coherent Underwater Acoustic Signals

Direction of arrival (DOA) estimation is critical in various fields, including wireless communications, radar, sonar, and electronic reconnaissance. Traditional DOA algorithms like minimum variance distortion-less response (MVDR) and multiple signal classification (MUSIC) are effective for incoherent sources. However, the presence of coherent signals due to underwater multipath propagation and human interference poses a challenge for traditional incoherent signal estimation algorithms. In this paper, we propose an augmented matrix construction based on DOA estimation of coherent signals, which firstly completes the conjugate rearrangement of the original received data, then obtains the autocorrelation matrix of the rearranged data, and obtains the cross-correlation matrix with the original received data. Finally, the augmentation matrix is constructed based on the covariance matrix of the original received signal, the conjugate rearrangement signal's autocorrelation matrix, and the two signals' intercorrelation matrix. Hence, this approach makes full use of the information of the echo signal, so it has higher resolution and estimation accuracy. Simulation analysis is conducted to verify the effectiveness and accuracy of our proposed algorithm. The results demonstrate that the algorithm outperforms traditional DOA estimation algorithms and provides robust performance in the presence of coherent signals.
Improving 6D Object Pose Estimation Based on Semantic Segmentation

The performance of 6D pose estimation, which is important for scene understanding, can be improved by more accurate object segmentation. RGB-D data including depth maps can provide more accurate position information than RGB data for semantic segmentation. In this work, we propose a novel two-stage RGBD-based pose estimation network, which can provide more precise semantic segmentation and effective point cloud features. Firstly, we use a lightweight semantic segmentation head to process the RBG-D data to get the pixel-level clustered mask, and then use a multi-scale and attention-based backbone to extract the point cloud features for pose estimation. We analyze the performance of our network on the YCB-Video dataset and the results show that our method is comparable to current state-of-the-art methods after optimization.
NextGen Dynamic Video Generator using AI

In an era marked by remarkable technological advancements, the way we create and share information has undergone a profound transformation. This paradigm shift is epitomized by the NextGen Dynamic Video Generator using AI, a cutting-edge tool that seamlessly integrates artificial intelligence with content creation. What sets this tool apart is the customizability, which allows the users to fine-tune every aspect of the tutorials. Users could tune creative elements like humor, the depth of explanation, character appearance, and voice to tailor tutorials to precise specifications. The core functionality of the tool revolves around script generation, the Cohere's language, laying the groundwork for the tutorial's content. Furthermore, seamless integration with Edge TTS ensures that the generated scripts are delivered with utmost clarity and engagement, enhancing the overall learning experience. Character animation is powered by SadTalker, adding a dynamic and captivating dimension to these tutorials. This animated character serves as a relatable guide, facilitating a deeper connection between the content and the audience. The tool also seamlessly integrates relevant and eye-catching images from Google, which are incorporated into the presentation slides. The workflow is a well-orchestrated process involving script generation, audio dialogue creation, image retrieval, video generation, and the seamless fusion of character animations and slides. The resulting video tutorials are not only comprehensive but also engaging and ready to be shared as valuable educational resources.
An Anti-Interference Method for Substation Boundary Noise Monitoring

For the problem that substation noise measurement is easily disturbed by external interference, a substation station boundary noise anti-interference monitoring scheme is proposed. Using the beam formation of the ring array in the modal domain for far-field directional pickup of substation noise to reduce the impact of steady-state interference on the measurement. Then the array output is converted into a single-channel signal, and the presence probability of substation noise at each time-frequency bin is estimated in real-time using an improved minimum-controlled recursive averaging algorithm, thus reducing the error caused by transient interference. The simulation results prove that the proposed method can effectively improve the accuracy of noise measurement.
Energy Market Predictions with Hybrid Neural Network 1D-CNN-BiGRU

Electricity price forecasting is important for managing supply and demand, planning investments in energy projects, ensuring energy security and efficient use of resources. This paper presents a hybrid neural network of two types of neural networks: the convolutional neural network (CNN) and the recurrent neural network (RNN) for energy market data analysis to forecast electricity prices. CNN is used to extract features from the raw data by applying a convolution operation on the temporal axis with different filters. At the same time, bidirectional gated recurrent units (BiGRU) of RNN are used for subsequent analysis of the temporal dependence of the extracted features, allowing the historical data to be considered. BiGRU is particularly useful for applications where the current input depends on past and future contexts. Thus, the ID-CNN-BiGRU method allows effective time series analysis and prediction of future values and can be widely used in the tasks of forecasting electricity prices, stocks, traffic, and other time series. The results indicate that the presented model is promising for use in a highly dynamic energy market.
Monitoring vegetation dynamics in Zhongwei, an arid city of Northwest China

This case study used Zhongwei City in northwest China to quantify the urbanization and revegetation processes (1990–2011) through a unified sub-pixel measure of vegetation cover. Research strategies included: (1) Conduct sub-pixel vegetation mapping (1990, 1996, 2004, and 2011) with Random Forest (RF) algorithm by integrating high (OrbView-3) and medium spatial resolution (Landsat TM) data; (2) Examine simple Dark Object Subtraction (DOS) atmospheric correction method to support temporal generalization of sub-pixel mapping algorithm; (3) And characterize patterns of vegetation cover dynamics based on change detection analysis. We found the RF algorithm, combined with simple DOS, showed good generalization capability for sub-pixel vegetation mapping. Predicted sub-pixel vegetation proportions were consistent for “Pseudo-invariant” pixels. Vegetation change analysis suggested persistent urban development within the city boundary, accompanied by a continuous expansion of revegetated area at the city fringe. Urban development occurred at both the suburban and urban core areas, and was mainly shaped by transportation networks. A transition in revegetation practices was documented: the large-scale governmental revegetation programs were replaced by the commercial afforestation conducted by industries. This study showed a slight increase in vegetation cover over the time period, balanced by losses to urban expansion, and a likely severe degradation of vegetation cover due to conversion of arable land to desert vegetation. The loss of arable land and the growth of artificial desert vegetation have yielded a dynamic equilibrium in terms of overall vegetation cover during 1990 to 2011, but in the long run vegetation quality is certainly reduced.
Aerial Robotics in Urban Environments: Optimized Path Planning and SITL Assessments

This paper focuses on guiding aerial robots through city environments. The goal is to create an optimal and safe flight path, avoiding obstacles in urban landscapes with tall buildings. We combine the A* algorithm and the Medial Axis algorithm to develop a well-balanced trajectory that ensures both safety and efficiency. To better understand narrow passages in cities, we analyze and preprocess two-dimensional maps. The A* algorithm forms the basis, while the Medial Axis algorithm identifies secure routes through tight spaces. Additionally, we explore new methods to seamlessly integrate multiple path planning algorithms, offering a comprehensive solution for navigating urban airspace. Our proposed method is rigorously evaluated using simulation tools, mimicking real-world scenarios. The results consistently demonstrate superior safety and efficiency compared to alternative algorithms. Even when subjected to atmospheric disturbance tests, our approach maintains adherence to critical operational constraints, ensuring enhanced safety and shorter flight paths. This study significantly contributes to advancing aerial robotics in urban areas, providing a promising way to enhance safety and efficiency in complex environments.
Defect detection of laptop appearance based on improved multi-scale normalizing flows

In the laptop production process, timely detection of appearance defects is essential to ensure product quality. At present, there are many shortcomings in the manual visual detection based method on the laptops production line. In addition, due to the wide variety of laptop appearance defects and extreme differences in defect scales, existing defect detection algorithms perform poorly in the field of laptop appearance detection. In response to the above problems, this paper proposes a defect detection algorithm based on improved multi-scale normalizing flows. First, the multi-level features extracted from the backbone network are fused by using the pyramid feature fusion module to obtain multi-scale features with rich semantic and spatial information. Then, the effective density estimation of the multi-scale features is achieved by fusing the normalizing flows of attention mechanisms. Finally, the defects are detected and localized based on the output likelihood values. The experimental results demonstrate the effectiveness of the proposed method in detecting and locating appearance defects.
Adoption of Industry 4.0 Technologies for Performance Management

The fourth industrial revolution (Industry 4.0) continues to evolve, revolutionize, and impact various aspects of contemporary organizations, and one such paradigm shift is pertinent to performance management. Innovative advancements in emerging and advanced Industry 4.0 technologies, such as artificial intelligence (AI), internet-of-things (IoT), cloud computing, machine learning (ML), digital twin, big data analytics, automation, and several others are reshaping the performance management domain in myriad ways. This study delves into the adoption of Industry 4.0 technologies that influence how organizations measure, evaluate, and augment employee performance. Digital tools assist in monitoring remote performance, and AI-driven algorithms offer predictive analytics. Comprehensively, these foster agility, adaptability, and alignment, thus providing personalized insights, continuous feedback, and performance trends. While it enhances objectivity and efficiency, there are also critical challenges relating to data privacy and ethical considerations. Study findings emphasize the transformative potential that Industry 4.0 has in transforming performance management parameters toward workforce empowerment and heightened levels of engagement and productivity.
IQScan: Automatic Insect Loading and 3D Scanning

We aim to tackle the challenges of digitising natural collections, especially insect collections, to extract rich information for bio-discovery and biosecurity. Although significant progress has been made in image-based 3D modelling of the specimens, particularly recent developments in the neural radiance field, capturing high-quality images at scale remains a major bottleneck for very large insect collections which can hold over tens of millions of specimens. Recent development in 3D insect scanning suggests that 3D printing allows custom 3D scanners to be built with lower cost and better functionalities. Current designs still require significant time-consuming manual work to operate the scanners. Furthermore, some specimens bend or slide with the change of gravity direction during scanning, causing errors in 3D modelling. This paper presents a new 3D design for insect specimen scanning along with a low-cost 3Dprinted prototype called IQScan, that allows a batch of multiple specimens to be loaded and scanned, to automate and speed up the image-capturing process. Major improvements include an autoloading mechanism to load multiple insects at a time. In addition, during scanning, tilting motion is applied to the camera, instead of the specimen, to keep the specimen undisturbed, which is especially important for flexible specimens. Early experiments show that IQScan can work with a batch of six insect specimens at a time and capture high-quality images for high-quality 3D insect models. IQSCan achieves a significant speedup of six times as compared to the existing scAnt 3D insect scanner. Further extension is possible for IQScan to work continuously with an unlimited number of insects. Our initial experiments show that the scanner works as expected producing usable images for 3D reconstructions.