Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention-enabled Deep Neural Network for Enhancing UAV-Captured Pavement Imagery in Poor Visibility

Integrating Unmanned Aerial Vehicle (UAV) technology with Artificial Intelligence AI and Computer Vision has revolutionized asset management, particularly pavement health monitoring. However, current AI-based methods often struggle in low-visibility scenarios, limiting their effectiveness. To address this, we present a novel end-to-end deep learning pipeline that detects image degradation using an efficient Attention mechanism and performs subsequent enhancement. This algorithm can be seamlessly integrated into drones or used for post-processing of pavement imagery. Its efficiency allows for scalability, making it a valuable tool for downstream road health monitoring tasks, such as cost estimation for road repairs. Our approach achieves mean accuracies of 93.34% with a mean inference time of 0.154 sec. , demonstrating its efficacy.
Questions Regarding the Applicability of a LiDAR-Based SLAM Method

This article provides an overview of different approaches to SLAM (Simultaneous Localization and Mapping) methods, with a focus on LiDAR-based SLAM. The article discusses the practical applicability of a simple and easy-to-use LiDAR SLAM method called Direct LiDAR Odometry, which was tested with different sensor mount configurations and with and without the additional input of an IMU. The results were evaluated based on Relative Pose Error based on Ground Truth information gained from GNSS receiver data. The tests were carried out using sensor data acquisitioned using a custom test vehicle.
Leaf Based Tomato Plant Disease Detection Using Generated Images from WGP-ESR GAN

Plant diseases impact agricultural products in terms of quantity and quality, ultimately affecting the income of farmers. Early detection of plant diseases is crucial for improving agricultural yields. In this paper, we are using deep learning techniques for detecting the diseases of tomato plants based on leaf images. Image gathering of diseased plant leaves from various plants leads to unbalanced disease classes. Such unbalanced disease classes are not suitable for the effective training of disease classifiers. So we are proposing a deep-learning model, WGP-ESR GAN(Wasserstein Generative Adversarial Network with Gradient Penalty and Enhanced Super Resolution GAN) to create a well-balanced dataset. The model consists of two stages. In the first stage, WGAN-GP generates 3x64x64 sized tomato plant leaf images, which are then inputted to ESRGAN in the second stage to produce realistic 3x256x256 sized images. Initially, the model was trained with only healthy tomato leaf images to get a pre-trained model, and then we continued the training process on a pre-trained model with unhealthy leaves. This process generates a well-balanced dataset of each class size is 5357. Disease classifiers (VGG16, ResNet50, and DenseNet121) are employed, and the Plant Village dataset is used for training and performance testing. The proposed model's generated images exhibit higher realism than state-of-the-art models, and the average classification accuracy of the balanced dataset generated by WGP-ESR (99.69 percent) is significantly improved than state-of-the-art models.
Gray-Box Modeling of the Vehicle Cabin and Comparison of Model Accuracy using Different Zonal Distributions

Thermal models are essential for the climate control system of vehicles, as they provide crucial information on the occupants’ thermal comfort and help optimize the heating, ventilation, and air conditioning (HVAC) system performance. A control-oriented cabin model should deliver accurate predictions while ensuring fast computational performance. With increasing expectations for thermal comfort, the predictability of zonal temperatures has become an essential aspect. This paper established various cabin models (mono-zone, 4-zone, and 8-zone model) using the gray-box modeling method. The parameters of these models were identified using measurements from real-world test drives based on available vehicle sensors and ten additional air temperature sensors. The goal is to develop a suitable cabin model and to determine a reasonable zonal distribution. Three model variants yielded promising results, with the average coefficient of determination (R²) calculated as 0.83 (mono-zone), 0.75 (4-zone), and 0.74 (8-zone).
A Hardware-Efficient Approximate Multiplier Combining Inexact Same-weight N:2 Compressors and Remapping Logic with Error Recovery

Approximate computing can trade accuracy for smaller power, delay, and area. So, approximate computing is hardware-efficient for fault-tolerant applications which do not require full precision, such as image processing and machine learning. This paper proposes same-weight N:2 compressors based on the probability analysis, and construct approximate multipliers combining proposed N:2 compressors, remapping logic and constant-truncation with its error recovery circuit. Evaluation results using commercial 65 nm process library show that proposed 8×8 multiplier has good hardware-accuracy trade-off. It reduces the power by 39%, the delay by 17%, the area by 34% and the power-delay-area-product (PDAP) by 67% with an MRED of 1.27% when compared to the exact multiplier. The proposed methods show the better PDAP compared with other approximate multipliers with almost the same MRED.
An Underwater Features Matching System Integrated with Image Enhancement

With the increasing demand for ocean exploration and underwater observation, video-based 3D reconstruction tasks and simultaneous localization and mapping (SLAM) tasks in underwater scenarios attract great attention. Accurate point correspondence extraction from consecutive underwater video frames is crucial for estimating camera poses and motion trajectories in video-based 3D vision tasks. However, the poor quality of underwater images, caused by light absorption and scattering in water, makes it challenging to obtain accurate matches solely relying on a robust feature-matching network. This paper proposes a matching system designed explicitly for achieving more precise matching between underwater video frames. The system integrates an image enhancement model, a feature extraction model, and a feature-matching model. To address the lack of ground truth depth and camera poses for underwater images, we introduce a unique homographic transformation to simulate camera motion-induced viewpoint changes. The transformed images, paired with the original ones, are utilized to train the feature matching network, which is a graph neural network with self- and cross-attention. And we replace dot-product attention with linear attention to reduce computational complexity. The experimental results validate the effectiveness of underwater feature-matching of the proposed method.
SereneMind: Design and Evaluation of a Persuasive Mobile App for Managing Stress Among Adults

Stress is a global public health problem that leads to severe physical and psychological health risks if not managed. Various stress management interventions have been delivered using mobile health (mHealth) apps. Although there are different existing mobile apps promoting stress-coping strategies, more work is still needed to design and evaluate such apps from a persuasion standpoint. Persuasive mobile apps incorporate persuasive strategies (PS) that work as backbone mechanics to motivate desired behaviour change. Therefore, we design and evaluate a mobile app called SereneMind following a five-step process. First, we deconstruct the PS operationalized in 150 stress management apps from both Google Play and App Store to identify the most implemented PS using the Persuasive Systems Design (PSD) framework. Second, we design a low-fidelity prototype (LFP) depicting various features of the app based on the top ten PS. Third, we conduct a user study involving our target audience to evaluate the perceived persuasiveness/effectiveness of each feature and uncover further improvement based on users' feedback. Fourth, based on our findings from the LFP evaluation, we design a high-fidelity prototype (HFP) using only the features that were perceived as effective by our target audience and applying user suggestions to refine the prototypes. Finally, we run a usability study to evaluate the HFP. Our results show that participants perceived SereneMind as usable and useful for managing stress. Therefore, it is more likely to be adopted and used. Lastly, we offer design recommendations for stress management apps in line with our findings.
Design of Non-Uniform Bending Dual-Band Monopole Antenna

In this paper, a non-uniform snake-shaped dualband monopole antenna is studied and designed using the equivalent circuit method. Based on the traditional monopole antenna, the non-uniform bending technique is applied to effectively improve the miniaturization and multifunctionalization of the antenna. The simulation results show that the antenna can work in DCS1800 band and UMTS2100 band, and return loss less than -10dB at 1747MHz-1878MHz and 2078MHz-2335MHz, showing good operation performance.
GWO-PI Controlled Re-Lift LUO Converter for Standalone Load System

When renewable energy resources started, it opened a new horizon to transmission and distribution of electrical energy. Several distributed energy resources are linked to the utility network or microgrid with the aid of a power electronic interface. However, the power quality is deteriorated by different factors such as varying load demands, changing weather condition and nonlinear loads. Hence, the proposed system aims to achieve improved power quality under balanced and unbalanced conditions with high control capability. To raise the quality of power at Distribution side of utility grid, three-phase four-leg VSI is used. In this proposed system, Photovoltaic system is used to supply power to load. Therefore, a DC-DC Relift Luo converter is used, which efficiently boosts the voltage gain while reducing the effects of parasitic elements over a large duty ratio range. Output of DC-DC converter is stabilized using a Grey Wolf Optimization (GWO) tuned PI controller and the effective grid voltage synchronization is achieved by the technique of feedback line controller. To ascertain the working of the proposed design in terms of its efficiency and dynamic compensation capability, a MATLAB based simulation model along with a laboratory prototype are developed and analyzed.
Coupled photochemical-biological system useful in the complete mineralization of a biorecalcitrant wastewater pollutant

Note: Serie Ponencias, Improving Human Potential Programme: Proceedings of the 1st Users Workshop, March 15, 2001, Almeria, Spain Reference LBE-POSTER-2001-016 Record created on 2005-03-02, modified on 2016-08-08
Improving Detection of Diabetic Retinopathy in Low-Resolution Images via Latent Diffusion

Diabetic retinopathy (DR) is the leading cause of blindness in working-age adults worldwide. Early detection and intervention are crucial in preventing blindness, but there is no national program for DR screening in the Philippines, and the current DR screening pathway is neither practical nor economical. The paper proposes a novel approach called Affinitive Diffusion-Augmented Vision for Interpretable Transformers (AffiDAVIT), which utilizes 5-fold cross-validation, diffusionbased data augmentation, and vision transformers to improve the detection of DR in low-resolution images. The approach leverages explainable ViT using Grad-CAM to highlight the regions of interest in fundus images for DR detection. Fuzzy logic is then applied whenever a no DR (negative) result is predicted by the model to lessen false negatives. AffiDAVIT outperforms state-ofthe-art models on the same dataset. Overall, the paper presents a potential solution for improving the detection of DR in lowresolution images, and the findings contribute significantly to the field of DR detection. The approach presents a promising alternative for DR screening in areas with limited access to ophthalmologists and comprehensive diabetes care.
Optimizing the IoT Performance: A Case Study on Pruning a Distributed CNN

Implementing Convolutional Neural Networks (CNN) based computer vision algorithms in Internet of Things (IoT) sensor nodes can be difficult due to strict computational, memory, and latency constraints. To address these challenges, researchers have utilized techniques such as quantization, pruning, and model partitioning. Partitioning the CNN reduces the computational burden on an individual node, but the overall system computational load remains constant. Additionally, communication energy is also incurred. To understand the effect of partitioning and pruning on energy and latency, we conducted a case study using a feet detection application realized with Tiny Yolo-v3 on a 12th Gen Intel CPU with NVIDIA GeForce RTX 3090 GPU. After partitioning the CNN between the sequential layers, we apply quantization, pruning, and compression and study the effects on energy and latency. We analyze the extent to which computational tasks, data, and latency can be reduced while maintaining a high level of accuracy. After achieving this reduction, we offloaded the remaining partitioned model to the edge node. We found that over 90% computation reduction and over 99% data transmission reduction are possible while maintaining mean average precision above 95%. This results in up to 17x energy savings and up to 5.2x performance speed-up.
Revolutionizing Weed Detection in Agriculture through the Integration of IoT, Big Data, and Deep Learning with Robotic Technology

The objective of this study is to develop a weed detection and classification technique that can be utilized by robotic weed control systems. The approach leverages deep learning-based image processing algorithms to classify plants as either crops or weeds based on their features. A categorization strategy is applied to identify weeds, which can grow both inside and outside of crop rows. Data collected from crop image wavelet analysis is initially used to differentiate between crops and weeds, and the farmer receives a phone alert. Afterwards, the farmer provides the robot with instructions to spray the necessary areas. The entire model is executed by a cutting-edge real-time object detection system, YOLOv4 (You Only Look Once). During testing, this model has shown superiority over classifier-based systems in several aspects, such as achieving higher accuracy rates and being able to evaluate the full context of the image for all predictions. For instance, the deep learning-based image processing algorithms used in this technique can differentiate between crops and weeds with higher accuracy compared to traditional classifier-based systems. Additionally, the YOLOv4 object detection system used in this study is capable of analyzing the entire image to make predictions, taking into account the context of each object. This represents a significant improvement over traditional classifier-based systems that often rely on local features to make predictions, which can lead to misclassifications
Research on Optimized Planning Deployment and Capacity Configuration Model for Multi-factor Fusion in Distribution Network

As various new factors, including distributed power supplies, energy storage, data centers, and 5G base stations, are being extensively incorporated into distribution networks, fully integrating and leveraging these new factors become crucial for fostering the high-quality development of distribution networks. Taking into full account the complementary adaptation among these new factors within the distribution network and the source-grid-load-storage characteristics, as well as their alignment with market demands, this paper presents a planning, deployment, and capacity configuration model for multi-factor fusion. The aim is to minimize system costs while considering constraints associated with distribution network operations and communication resources. By applying this model, the overall utilization level of distribution facilities is significantly enhanced.
Influence of Different Activation Functions on Neural Networks Computational RCS

The purpose of this paper is to find out the most suitable activation function for neural networks regressive calculation of radar cross section (RCS). Different activation functions can give neural networks different feature of data processing. This paper chooses four different activation functions, and use them in same multilayers full connected neural networks to compare which one is more suitable for regressive computing RCS.
Sliding Mode Control Strategy for Buck Converters based on Linear Extended State Observer

In order to improve the robustness of the sliding mode control algorithm of the four-phase interleaved parallel Buck converter, an improved adaptive non-singular terminal sliding mode PI (ANTSMC-PI) dual closed-loop control strategy based on linearly expanding state observer (LESO) is proposed in this paper. First, two LESO observers are designed to estimate the matched and mismatched disturbance in the system dynamic model, respectively. Second, the estimates of the observers are Integrationed into the outer-loop controller, and an improved sliding-mode controller is proposed to achieve error-free tracking of the output voltage to the reference voltage; while the PI controller is used for the current inner loop to ensure the equal current flow in each phase of the system. Finally, the stability of the designed controller is demonstrated by using the lyapunov stability principle. The simulation in Matlab/Simulink platform shows that the proposed scheme can effectively reduce the output ripple and improve the system dynamic performance compared with the traditional non-singular terminal sliding-mode PI control strategy.
Domain-Driven Design as Model Contract in Full-Stack Development

In this work, a domain-driven design process is proposed, allowing to define a contract regarding the structure of the used data within a multi-language full-stack application using a model-to-text transformation approach. This process is based on a metamodel, which allows for the implementation of language-specific transformers to generate representations of the data as domain models for arbitrary programming languages. It is evaluated in the context of a modern full-stack microservice architecture including a C# and Python based backend with access to a database using object-relational-mapping principles and a TypeScript based frontend, connected by a GraphQL interface. The proposed methodology is intended for an agile modelling process including automatic adaptions, but also the preservation of user-defined extensions within the generated italic-code artifacts.
Dissemination of the « Planck-Kilogram »

Metrologists all over the world are looking at the activities of the Metre Convention concerning the New International System of Units on the basis of fundamental constants. Especially the redefinition of the kilogram will have consequences in nearly all fields of physics, engineering and trade. A new definition of a base unit imperatively implies thoughts about the future dissemination of the unit. After a short glance at the potential of the Kibble(Watt)-Balance the presentation will describe the strategy of the Physikalisch-Technische Bundesanstalt to use silicon spheres of different qualities for the dissemination of the quantum based kilogram to the macroscopic world. Beside metrological aims the availability of realisations is of crucial importance within a future dissemination chain. Aspects like the connection to the established system as an important aspect for acceptance, the applicability of the developed tools and procedures for using such spheres as mass standards or the current state of activities to proof the expected excellent long term characteristics of silicon spheres in use will be presented.
Automating Safety Critical Ultrasonic Data Analysis with a Variational Auto-Encoder

Non-destructive evaluation based on the analysis of normal beam single probe pulse echo ultrasonic data is often a core component of a fitness for service argument supporting the continued use of manufactured components in safety critical contexts. Typical applications include testing of pipe walls, weldments, and concrete structure material condition. We model the analysis process with probabilistic statements and implement them by leveraging the empirically observed capacity of variational auto-encoders to learn and characterize complex distributions. The variational auto-encoder based characterization of ultrasonic datasets allows a partition that effectively removes outlier bias from consideration when applying deterministic methods for parameter estimation. This affords a bias free characterization of nominal response, the direct identification of deviations from it, and obtains real value estimates for the time of flight of features in the dataset. Our results are explainable, do not depend on out of distribution generalization, do not require data-preprocessing, and do not require massive labelled class balanced datasets. As such our method provides an ideal approach to support the automation of normal beam single probe ultrasonic data analysis in safety critical applications.
A High-Performance SIW Slot Antenna Array for mmWave Radar

In this paper, a high-performance millimeter-wave radar antenna is designed. This antenna is Substrate Integrated Waveguide (SIW) planar slotted array with narrow beam width, high gain and low sidelobe level at 77 GHz. Firstly, the excitation amplitude distribution of the array element corresponding to −20 dB sidelobe level is obtained by Taylor synthesis method. Thus, the design parameters of the array elements can be obtained. The feed network of the antenna is composed of an air-filled rectangular waveguide (RWG) to Substrate Integrated Waveguide (SIW) transition and a power division feed network. Finally, the array element and the feed network are combined to form an $8\times 16$ slot array. The overall simulation design is carried out in HFSS. In the simulation, RWG is used to replace SIW equivalently to accelerate the simulation speed and save design resources. The simulation results show that the gain at 77 GHz is 21.4 dB with 75 % radiation efficiency, and the front-to-back ratio is 45.5 dB. Within the relative bandwidth of 2 %, the VSWR of the feed port is less than 1.78. The design can satisfy the antenna requirements in millimeter-wave radar systems.
Design of 20GHz Wide Tuning Range LC-VCO in 55-nm CMOS

In this paper, a 20GHz wideband, high linearity and low phase noise voltage-controlled oscillator (VCO) is achieved. A different bias voltages variable capacitor array improved frequency modulation linearity,effectively. Adopting the tail current source noise filtering technique, the level of phase noise is significantly reduced. The designed VCO has been implemented in 55nm RF CMOS process, operates at a supply voltage of 1V, and exhibits an average power consumption of 24.8mW excluding output driving buffers. Post-simulation results demonstrate that the VCO obtains a tuning range from 18.84 to 21.58GHz, which achieves a low phase noise of -110.58dBc/Hz at 1MHz offset from a 20GHz carrier. The core chip occupies an area of $640\mu \mathrm{m}\times 420\mu \mathrm{m}$.