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Videos are considered as most trustworthy means of communication in the present digital era. The advancement in multimedia technology has made video content sharing and manipulation very easy. Hence, the video authenticity is a challenging task for the research community. Video forensics refer to uncovering the forgery traces. The detection of spatiotemporal object‐removal forgery in surveillance videos is crucial for judicial forensics, as the presence of objects in the video has significant information as legal evidence. The author proposes a passive max–median averaging motion residual algorithm for revealing the forgery traces, successfully giving visible object‐removal traces followed by a deep learning approach, YOLO‐V8, for forged region localization. YOLO‐V8 is the latest deep learning model, which has a wide scope for real‐time application. The proposed method utilizes YOLO‐V8 for object‐removal forgery in surveillance videos. The network is trained on the SYSU‐OBJFORG dataset for object‐removal forged region localization in videos. The fine‐tuned YOLO‐V8 successfully classifies and localizes the object‐removal tampered region with an F1‐score of 0.99 and a precision of 0.99. The observed high confidence score of the bounding box around the forged region makes the model reliable. This fine‐tuned YOLO‐V8 would be a better choice in real‐time applications as it solves the complex object‐based forgery detection in videos. The performance of the proposed system is far better than the existing deep learning approach. [ABSTRACT FROM AUTHOR]

The goal of achieving autonomous navigation for agricultural robots poses significant challenges, mostly arising from the substantial natural variations in crop row images as a result of weather conditions and the growth stages of crops. The processing of the detection algorithm also must be significantly low for real-time applications. In order to address the aforementioned requirements, we propose a crop row detection algorithm that has the following features: Firstly, a projective transformation is applied to transform the camera view and a color-based segmentation is employed to distinguish crop and weed from the background. Secondly, a clustering algorithm is used to differentiate between the crop and weed pixels. Lastly, a robust line-fitting approach is implemented to detect crop rows. The proposed algorithm is evaluated throughout a diverse range of scenarios, and its efficacy is assessed in comparison to four distinct existing solutions. The algorithm achieves an overall intersection over union (IOU) of 0.73 and exhibits robustness in challenging scenarios with high weed growth. The experiments conducted on real-time video featuring challenging scenarios show that our proposed algorithm exhibits a detection accuracy of over 90% and is a viable option for real-time implementation. With the high accuracy and low inference time, the proposed methodology offers a viable solution for autonomous navigation of agricultural robots in a crop field without damaging the crop and thus can serve as a foundation for future research.

In recent years, the Internet of Things (IoT) has gradually developed applications such as collecting sensory data and building intelligent services, which has led to an explosion in mobile data traffic. Meanwhile, with the rapid development of artificial intelligence, semantic communication has attracted great attention as a new communication paradigm. However, for IoT devices, however, processing image information efficiently in real time is an essential task for the rapid transmission of semantic information. With the increase of model parameters in deep learning methods, the model inference time in sensor devices continues to increase. In contrast, the Pulse Coupled Neural Network (PCNN) has fewer parameters, making it more suitable for processing real-time scene tasks such as image segmentation, which lays the foundation for real-time, effective, and accurate image transmission. However, the parameters of PCNN are determined by trial and error, which limits its application. To overcome this limitation, an Improved Pulse Coupled Neural Networks (IPCNN) model is proposed in this work. The IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons, and all its parameters are set adaptively, which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of images. Experimental segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation Datasets. The IPCNN method achieves a better segmentation result without training, providing a new solution for the real-time transmission of image semantic information.

Abstract Crop residue-based biochar (CRB) has shown great potential for removing trace elements (TEs) from aquatic matrices. Despite the increasing interest in this area, no review has focused specifically on the efficacy of CRB for TEs removal in aquatic environments. This comprehensive review examines the global TEs water contamination status with an emphasis on their sources, compositional metrics for crop residue feedstock (proximate, ultimate, and lignocellulosic properties), and the potential use of CRB for TEs removal in aquatic media. It also evaluates the factors that affect the ability of CRB to remove TEs, such as feedstock type, production conditions, water pH, background electrolytes, water temperature, CRB/water ratio, and underlying pollutant sorption mechanisms. This review also discusses the practical applications of CRB in real water samples and engineering considerations for designing CRB with improved physicochemical properties, treatment efficiencies, and regeneration abilities. Additionally, the cost–benefit and economic assessment of CRB, challenges, and future research directions related to CRB are highlighted to promote research on this sustainable source of biochar. By elucidating the prospects of CRB as an adsorbent, this review emphasizes the need for continued research on its practical implications for environmentally relevant pollutant concentrations. Graphical Abstract

Crop residue-based biochar (CRB) has shown great potential for removing trace elements (TEs) from aquatic matrices. Despite the increasing interest in this area, no review has focused specifically on the efficacy of CRB for TEs removal in aquatic environments. This comprehensive review examines the global TEs water contamination status with an emphasis on their sources, compositional metrics for crop residue feedstock (proximate, ultimate, and lignocellulosic properties), and the potential use of CRB for TEs removal in aquatic media. It also evaluates the factors that affect the ability of CRB to remove TEs, such as feedstock type, production conditions, water pH, background electrolytes, water temperature, CRB/water ratio, and underlying pollutant sorption mechanisms. This review also discusses the practical applications of CRB in real water samples and engineering considerations for designing CRB with improved physicochemical properties, treatment efficiencies, and regeneration abilities. Additionally, the cost–benefit and economic assessment of CRB, challenges, and future research directions related to CRB are highlighted to promote research on this sustainable source of biochar. By elucidating the prospects of CRB as an adsorbent, this review emphasizes the need for continued research on its practical implications for environmentally relevant pollutant concentrations. Highlights: Crop residue-based biochar (CRB) emerged as a top-notch adsorbent for TEs in aquatic systems. The CRB adsorption potential depended on the experimental conditions. The application of CRB to real water samples highlights its potential on a larger scale. Engineer-designed CRB exhibits high treatment efficiencies and regeneration abilities. [ABSTRACT FROM AUTHOR]

In this paper, we address the challenge of ensuring stability in bipedal walking robots and exoskeletons. We explore the feasibility of real-time implementation for the Predicted Step Viability algorithm (PSV), a complex multi-step optimization criterion for planning future steps in bipedal gait. To overcome the high computational cost of the PSV algorithm, we performed an analysis using 11 classification algorithms and a stacking strategy to predict if a step will be stable or not. We generated three datasets of increasing complexity through PSV simulations to evaluate the classification performance. Among the classifiers, k Nearest Neighbors, Support Vector Machine with Radial Basis Function Kernel, Decision Tree, and Random Forest exhibited superior performance. Multi-Layer Perceptron also consistently performed well, while linear-based algorithms showed lower performance. Importantly, the use of stacking did not significantly improve performance. Our results suggest that the feature vector applied with this approach is applicable across various robotic models and datasets, provided that training data is balanced and sufficient points are used. Notably, by leveraging classifiers, we achieved rapid computation of results in less than 1 ms, with minimal computational cost. [ABSTRACT FROM AUTHOR]

Object detection has become a popular tool of deep learning in the era of digital manufacturing. In this study, the most powerful and efficient object detection algorithm, i.e., You Only Look Once (YOLO) algorithm, was used to detect anomalies in deposited beads of wire-arc additive manufacturing (WAAM) using melt pool images. This study used the latest version of YOLO algorithm to train and validate the custom image dataset of the melt pool obtained by conducting experiments using a robotic-controlled WAAM. The mean average precision (mAP) for the "Regular bead" class and the "Irregular bead" class reached 99% at an Intersection over Union (IoU) threshold of 0.5, for both training and validation. When the model was tested for new or unseen datasets by conducting four new experimental trials, the mAP value for the "Regular bead" class reached 98.47% and for the "Irregular bead" class reached 96.68% at an average processing time of 0.014 s/frame. The object detection algorithm YOLO has shown an excellent processing time of 15 ms per frame, which shows its potential for real-time application in the manufacturing industry. [ABSTRACT FROM AUTHOR]

The given text discusses a road segmentation method called MSAN that uses multi-modal scale-aware attentions to segment roads in real-time for autonomous driving. The method combines RGB and depth information to improve prediction results and outperforms single modality models. The MSAN architecture consists of two attention modules, one for feature fusion across modalities and scales and another for scale-aware fusion. The experiments show that MSAN achieves high performance and efficiency on multiple datasets. However, the method relies heavily on labeled data, which limits its adaptability to different scenarios. Future research aims to reduce data dependency through few-shot learning strategies. [Extracted from the article]

Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model's enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system. [ABSTRACT FROM AUTHOR]

Introduction: Date palm species classification is important for various agricultural and economic purposes, but it is challenging to perform based on images of date palms alone. Existing methods rely on fruit characteristics, which may not be always visible or present. In this study, we introduce a new dataset and a new model for image-based date palm species classification. Methods: Our dataset consists of 2358 images of four common and valuable date palm species (Barhi, Sukkari, Ikhlas, and Saqi), which we collected ourselves. We also applied data augmentation techniques to increase the size and diversity of our dataset. Our model, called DPXception (Date Palm Xception), is a lightweight and efficient CNN architecture that we trained and fine-tuned on our dataset. Unlike the original Xception model, our DPXception model utilizes only the first 100 layers of the Xception model for feature extraction (Adapted Xception), making it more lightweight and efficient. We also applied normalization prior to adapted Xception and reduced the model dimensionality by adding an extra global average pooling layer after feature extraction by adapted Xception. Results and discussion: We compared the performance of our model with seven well-known models: Xception, ResNet50, ResNet50V2, InceptionV3, DenseNet201, EfficientNetB4, and EfficientNetV2-S. Our model achieved the highest accuracy (92.9%) and F1-score (93%) among the models, as well as the lowest inference time (0.0513 seconds). We also developed an Android smartphone application that uses our model to classify date palm species from images captured by the smartphone's camera in real time. To the best of our knowledge, this is the first work to provide a public dataset of date palm images and to demonstrate a robust and practical image-based date palm species classification method. This work will open new research directions for more advanced date palm analysis tasks such as gender classification and age estimation. [ABSTRACT FROM AUTHOR]

Stress is the mood of pressure and tension that a person feels. Usually, when the pressure on an individual decrease, the body begins to stabilize the state and calm down. Hence, stress detection in real-time is a critical duty in medical systems. However, acquiring physiological data requires additional equipment and is difficult for users to carry with them at all times. Depending on this problem, it is possible to detect stress through behavioral data. Smartphones are devices that provide various behavioral data that people use constantly throughout the day. In this study, a real-time stress detection system based on soft keyboard typing behaviors was developed with the data obtained from linear acceleration, gravity, gyroscope sensors, and a touchscreen panel of the smartphone. 172 attributes were extracted from the raw sensor data. However, such a high number of dimensions could negatively affect the performance of machine learning algorithms. To address this problem, the number of features was reduced by various techniques such as filter-based methods and standard binary-code chromosome Genetic Algorithm as a contribution to this study. Then, writing behaviors were classified with the commonly used machine learning methods namely, C4.5, kNN, and Bayesian Networks. As a result of the experiments, the best classification was obtained from the kNN method using the features selected by the Genetic Algorithm with a classification accuracy of 89.61% and F-Measure of 0.9052. Another contribution of this study is that a mobile service and a relaxation application were developed for stress detection and to reduce stress levels using the selected feature vector. [ABSTRACT FROM AUTHOR]

IntroductionDate palm species classification is important for various agricultural and economic purposes, but it is challenging to perform based on images of date palms alone. Existing methods rely on fruit characteristics, which may not be always visible or present. In this study, we introduce a new dataset and a new model for image-based date palm species classification.MethodsOur dataset consists of 2358 images of four common and valuable date palm species (Barhi, Sukkari, Ikhlas, and Saqi), which we collected ourselves. We also applied data augmentation techniques to increase the size and diversity of our dataset. Our model, called DPXception (Date Palm Xception), is a lightweight and efficient CNN architecture that we trained and fine-tuned on our dataset. Unlike the original Xception model, our DPXception model utilizes only the first 100 layers of the Xception model for feature extraction (Adapted Xception), making it more lightweight and efficient. We also applied normalization prior to adapted Xception and reduced the model dimensionality by adding an extra global average pooling layer after feature extraction by adapted Xception.Results and discussionWe compared the performance of our model with seven well-known models: Xception, ResNet50, ResNet50V2, InceptionV3, DenseNet201, EfficientNetB4, and EfficientNetV2-S. Our model achieved the highest accuracy (92.9%) and F1-score (93%) among the models, as well as the lowest inference time (0.0513 seconds). We also developed an Android smartphone application that uses our model to classify date palm species from images captured by the smartphone’s camera in real time. To the best of our knowledge, this is the first work to provide a public dataset of date palm images and to demonstrate a robust and practical image-based date palm species classification method. This work will open new research directions for more advanced date palm analysis tasks such as gender classification and age estimation.

The heavy use of machine learning algorithms in safety-critical systems poses serious questions related to safety, security, and predictability issues, requiring novel architectural approaches to guarantee such properties. This paper presents an architecture solution that leverages heterogeneous platforms and virtualization technologies to support AI-powered applications consisting of modules with mixed criticalities and safety requirements. The hypervisor exploits the security features of the Xilinx ZCU104 MPSoCs to create two isolated execution environments: a high performance domain running deep learning algorithms under the Linux operating system and a safety-critical domain running control and monitoring functions under the freeRTOS real-time operating system. The proposed approach is validated by a use case consisting of an unmanned aerial vehicle capable of tracking moving targets using a deep neural network accelerated on the FGPA available on the platform. [ABSTRACT FROM AUTHOR]

In this paper, we address the challenge of ensuring stability in bipedal walking robots and exoskeletons. We explore the feasibility of real-time implementation for the Predicted Step Viability algorithm (PSV), a complex multi-step optimization criterion for planning future steps in bipedal gait. To overcome the high computational cost of the PSV algorithm, we performed an analysis using 11 classification algorithms and a stacking strategy to predict if a step will be stable or not. We generated three datasets of increasing complexity through PSV simulations to evaluate the classification performance. Among the classifiers, k Nearest Neighbors, Support Vector Machine with Radial Basis Function Kernel, Decision Tree, and Random Forest exhibited superior performance. Multi-Layer Perceptron also consistently performed well, while linear-based algorithms showed lower performance. Importantly, the use of stacking did not significantly improve performance. Our results suggest that the feature vector applied with this approach is applicable across various robotic models and datasets, provided that training data is balanced and sufficient points are used. Notably, by leveraging classifiers, we achieved rapid computation of results in less than 1 ms, with minimal computational cost.

The Internet of Things (IoT) facilitates the deployment of sensing devices for data acquisition and transmission across networks and systems, thus enabling innovative real-time applications. Securing these platforms remains paramount due to the high value placed on data privacy. This study proposes a novel real-time authentication method, fundamentally grounded in a One-Time Pad (OTP) concept. Data are encrypted using dynamic encryption, the parameters of which are determined by data from randomly distributed sensors. Encryption keys are dynamically transmitted, allowing for on-the-fly key generation and exchange. This enhances the security and privacy of user data. A lightweight technique for key creation, exchange, and authentication is presented for data collection from sensors used in real-time applications. This protocol ensures data privacy and security throughout the data collection process. The proposed protocol's efficacy is demonstrated through a discussion of how it meets these criteria, and an example illustrating its operation at each tier in the event of desynchronization or an incident. [ABSTRACT FROM AUTHOR]

Currently, remote sensing equipments are evolving toward intelligence and integration, incorporating edge computing techniques to enable real-time responses. One of the key challenges in enhancing downstream decision-making capabilities is the preprocessing step of image dehazing. Existing dehazing methods usually suffer from steep computational costs with densely connected residual modules, as well as difficulties in maintaining visual quality. To tackle these problems, we designed a lightweight atmosphere scattering model based network structure to extract, fuse, and weight multiscale features. Our proposed LFD-Net demonstrates strong interpretability by exploiting the gated fusion module and attention mechanism to realize feature interactions between multilevel representations. The experimental results of LFD-Net on SOTS dataset reach an average frequency per second of 54.41, approximately eight times faster than seven most popular methods with equivalent metrics. After image dehazing by LFD-Net, the performance of object detection is significantly improved. The mean average precision when IoU = 0.5 (mAP@0.5) based on YOLOv5 is improved by 4.73% on DAIR-V2X dataset, which verified the practicability and adaptability of LFD-Net for real-time vision tasks.

This work assesses the effectiveness of heterogeneous computing based on a CUDA implementation for real-time ego-lane detection using a typical low-cost embedded computer. We propose and evaluate a CUDA-optimized algorithm using a heterogeneous approach based on the extraction of features from an aerial perspective image. The method incorporates well-known algorithms optimized to achieve a very efficient solution with high detection rates and combines techniques to enhance markings and remove noise. The CUDA-based solution is compared to an OpenCV library and to a serial CPU implementation. Practical experiments using TuSimple's image datasets were conducted in an NVIDIA's Jetson Nano embedded computer. The algorithm detects up to 97.9% of the ego lanes with an accuracy of 99.0% in the best-evaluated scenario. Furthermore, the CUDA-optimized method performs at rates greater than 300 fps in the Jetson Nano embedded system, speeding up 25 and 140 times the OpenCV and CPU implementations at the same platform, respectively. These results show that more complex algorithms and solutions can be employed for better detection rates while maintaining real-time requirements in a typical low-power embedded computer using a CUDA implementation.

Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system.

The processing of underwater images can vastly ease the difficulty of underwater robots' tasks and promote ocean exploration development. This paper proposes a fast and efficient underwater image enhancement model based on conditional GAN with good generalization ability using aggregation strategies and concatenate operations to take full advantage of the limited hierarchical features. A sequential network can avoid frequently visiting additional nodes, which is beneficial for speeding up inference and reducing memory consumption. Through the structural re-parameterization approach, we design a dual residual block (DRB) and accordingly construct a hierarchical attention encoder (HAE), which can extract sufficient feature and texture information from different levels of an image, and with 11.52% promotion in GFLOPs. Extensive experiments were carried out on real and artificially synthesized benchmark underwater image datasets, and qualitative and quantitative comparisons with state-of-the-art methods were implemented. The results show that our model produces better images, and has good generalization ability and real-time performance, which is more conducive to the practical application of underwater robot tasks. [ABSTRACT FROM AUTHOR]

This article presents a computer vision-based approach for monitoring water flow at outlet points of a tile drain system. The approach relies only on video capture of events at outlet points, thus a camera can be installed remotely and without contact with water. The algorithm detects, identifies, and tracks flows by motion, shape, and color features and measures flow rate based on a proposed model and two provided dimensions. The software was tested in a laboratory environment with three different target flow rate conditions: 0.312, 0.946, and 1.58 L/s (5, 15, and 25 gal/min). Flow rates reported by the computer vision approach are within 12% of the ground-truth flow rate baseline. The results of this work show that computer vision can be used as a reliable method for monitoring outlet flows from free-standing outlet structures under laboratory conditions. This work opens the possibility of applying computer vision techniques in tile drain monitoring from outlet points with mobile video recording devices in the field. [ABSTRACT FROM AUTHOR]

With the development of mobile payment, the Internet of Things (IoT) and artificial intelligence (AI), smart vending machines, as a kind of unmanned retail, are moving towards a new future. However, the scarcity of data in vending machine scenarios is not conducive to the development of its unmanned services. This paper focuses on using machine learning on small data to detect the placement of the spiral rack indicated by the end of the spiral rack, which is the most crucial factor in causing a product potentially to get stuck in vending machines during the dispensation. To this end, we propose a k-means clustering-based method for splitting small data that is unevenly distributed both in number and in features due to real-world constraints and design a remarkably lightweight convolutional neural network (CNN) as a classifier model for the benefit of real-time application. Our proposal of data splitting along with the CNN is visually interpreted to be effective in that the trained model is robust enough to be unaffected by changes in products and reaches an accuracy of 100 % . We also design a single-board computer-based handheld device and implement the trained model to demonstrate the feasibility of a real-time application. [ABSTRACT FROM AUTHOR]

A traffic sign recognition system is crucial for safely operating an autonomous driving car and efficiently managing road facilities. Recent studies on traffic sign recognition tasks show significant advances in terms of accuracy on several benchmarks. However, they lack performance evaluation in driving cars in diverse road environments. In this study, we develop a traffic sign recognition framework for a vehicle to evaluate and compare deep learning-based object detection and tracking models for practical validation. We collect a large-scale highway image set using a camera-installed vehicle for training models, and evaluate the model inference during a test drive in terms of accuracy and processing time. In addition, we propose a novel categorization method for urban road scenes with possible scenarios. The experimental results show that the YOLOv5 detector and strongSORT tracking model result in better performance than other models in terms of accuracy and processing time. Furthermore, we provide an extensive discussion on possible obstacles in traffic sign recognition tasks to facilitate future research through numerous experiments for each road condition. [ABSTRACT FROM AUTHOR]

During the maneuvering of most unmanned aerial vehicles (UAVs), the GPS is one of the sensors used for navigation. However, this kind of sensor cannot handle indoor navigation applications well. Using a camera might be the answer to performing indoor navigation using its coordinate system. In this study, we considered indoor navigation applications using the ZED2 stereo camera for the quadcopter. To use the ZED 2 camera as a navigation sensor, we first transformed its coordinates into the North, East, down (NED) system to enable the drone to understand its position and maintain stability in a particular position. The experiment was performed using a real-time application to confirm the feasibility of this approach for indoor localization. In the real-time application, we commanded the quadcopter to follow triangular and rectangular paths. The results indicated that the quadcopter was able to follow the paths and maintain its stability in specific coordinate positions. [ABSTRACT FROM AUTHOR]

Emotion recognition is a very challenging research field due to its complexity, as individual differences in cognitive–emotional cues involve a wide variety of ways, including language, expressions, and speech. If we use video as the input, we can acquire a plethora of data for analyzing human emotions. In this research, we use features derived from separately pretrained self-supervised learning models to combine text, audio (speech), and visual data modalities. The fusion of features and representation is the biggest challenge in multimodal emotion classification research. Because of the large dimensionality of self-supervised learning characteristics, we present a unique transformer and attention-based fusion method for incorporating multimodal self-supervised learning features that achieved an accuracy of 86.40% for multimodal emotion classification. [ABSTRACT FROM AUTHOR]

It is important for the autonomous system to understand environmental information. For the autonomous system, it is desirable to have a strong generalization ability to deal with different complex environmental information, as well as have high accuracy and quick inference speed. Network ensemble architecture is a good choice to improve network performance. However, it is unsuitable for real-time applications on the autonomous system. To tackle this problem, a new neural network ensemble named partial-shared ensemble network (PSENet) is presented. PSENet changes network ensemble architecture from parallel architecture to scatter architecture and merges multiple component networks together to accelerate the inference speed. To make component networks independent of each other, a training method is designed to train the network ensemble architecture. Experiments on Camvid and CIFAR-10 reveal that PSENet achieves quick inference speed while maintaining the ability of ensemble learning. In the real world, PSENet is deployed on the unmanned system and deals with vision tasks such as semantic segmentation and environmental prediction in different fields. [ABSTRACT FROM AUTHOR]

The process systems domain is undergoing the fourth industrial revolution, which is helping industries digitize and optimize their production techniques. Concurrently, the field of data-based modeling has been expanding, leading to the proposal of many fault detection models. However, the rapid expansion has created gaps in the field. For instance, Smart Manufacturing (SM) methodologies have yet to be incorporated into undergraduate chemical engineering education. Additionally, only a few developed fault detection models have been deployed for real-time usage and practical applications. This study takes a crucial step toward bridging the two mentioned gaps by enabling undergraduate students to learn SM techniques and developing a safe and controlled academic environment for deploying fault detection models. The demonstration is implemented on a shell and tube heat exchanger, taught in a senior year laboratory course, using the Smart Manufacturing Innovation Platform (SMIP). The implementation provides an easily customizable pipeline for SM applications involving human-in-the-loop decision-making on a real-life hardware system. Actual data from heat exchanger equipment is used to train and compare the performances of several state-of-the-art fault detection models, including fully connected, convolutional, and recurrent neural networks. Current work also presents tutorials on deploying models for practical real-time applications using the SMIP. The overall architecture is a plug-and-play package that will motivate students to learn about SM and catalyze their interest in developing and deploying fault detection models using real-world data. • Architecture to motivate undergraduate students to learn aspects of Smart Manufacturing. • The demonstration is implemented on a shell and tube heat exchanger using the Smart Manufacturing Innovation Platform (SMIP). • Fault detection models are deployed to test real-time applications, including fully connected, convolutional, and recurrent neural networks. • Students get first-hand experience of Human-in-the-Loop decision making similar to a control room. [ABSTRACT FROM AUTHOR]

The efficient utilization of energy is a crucial consideration for plug-in hybrid electric buses (PHEB). However, achieving the optimal energy management strategy (EMS) for PHEB necessitates the harmonious optimization of both driving conditions and battery status. Consequently, an innovative EMS that integrates the data-driven prediction of the driving cycles and battery health status. Explicitly, the bi-directional long short-term memory algorithm is employed to reconstruct and train the health status data of power batteries, enabling the accurate estimation of their State of Health (SOH) values. Moreover, a database of historical driving behavior is meticulously collected through a real-world PHEB platform, and the driving cycles for a certain while are predicted with the clustering algorithm. Then, this invaluable information on the battery status and driving cycles is seamlessly introduced into an adaptive-network-based fuzzy inference system (ANFIS) to regulate of the energy conversion factor in the following equivalent consumption minimization strategy (ECMS). Besides, the offline global optimization of equivalent fuel consumption with the Pontryagin's minimum principle (PMP) is preliminarily conducted to generate the pre-established energy conversion factors, facilitating convenient access to the engine and motor's reasonable power distribution range. Finally, simulations and experiments have validated the effectiveness of the proposed EMS, demonstrating a 23.57 % reduction in battery SOC depletion and a 10.53 % decrease in fuel consumption compared to traditional ECMS methods. More importantly, the proposed method has been validated for its executability in embedded devices through real-world bench experiments. This research holds significant reference value for energy utilization and practical implementation in the PHEB, thereby contributing to the advancement of knowledge in this field. • A real-time energy management strategy for a PHEV is proposed by incorporating the offline global PMP optimization and online ECMS execution. • The equivalent energy conversion factor for the EMS fully considers the battery health status and driving cycle recognition. • The driving cycles are well recognized with the Bi-LSTM algorithm which has enhanced the accurate future driving information prediction. • The real-world experimental test is implemented for the practical application. [ABSTRACT FROM AUTHOR]

Conductive hydrogel represents an important development direction in modern/future electronic industry. However, successfully achieving high performance hydrogels which are capable of performing a real-world task still remain extremely challenging because most conventional approaches of constructing hydrogels are difficult to make hydrogels achieve the integration of multiple performances like robust mechanical properties, excellent conductivity and stable electrochemical performance. Here, we develop a simple and universal two-layer hydrogen bond networks (TLHBNs) strategy by which seven types of PVA based hydrogels with simultaneously robust mechanical properties (stress, strain, toughness, elasticity, fatigue resistance, notch insensitivity, anti-damage), excellent conductivity (7.2− 12.2 S m−1, 11.7− 19.8 S m−1, 10.5− 21.6 S m−1), reversible/stable network structure and constantly sensitive signal output capability are achieved via sophisticatedly layer-by-layer introducing TLHBNs to PVA hydrogel. Benefiting from the extraordinary performances, one hydrogel is so strong that it can load as heavy as 80 kg of a boy volunteer. Meanwhile, the hydrogels, assembled into flexible sensors, satisfactorily fulfills practical tasks (human joint motions and bionic skin) upon handling real-world application. This strategy, enabling extraordinary performances while using a generic and facile designing concept, opens up a new insight into the design of high-performance PVA hydrogel and beyond. • Engineer PVA/polyethyleneimine/sodium carboxylate hydrogel sensors. • Robust mechanical properties, excellent conductivity and practical application. • Stable signal output, high sensitivity and insensitivity against breakage. • Two-layer hydrogen bond networks responsible to high performances. • Human motion monitoring, bionic skin applications and loading 80 kg a boy volunteer. [ABSTRACT FROM AUTHOR]

Physics-based lithium-ion battery model is rarely used in real-time applications due to its complexity, restricting the development of next-generation refined battery management systems. Fidelity and computation efficiency are still challenging to coordinate for existing Model Order Reduction (MOR) methods, especially in solving either electrochemical reaction rate distribution or coupled multi-physics fields. This paper proposes a novel reduced-order electrochemical model called the Triple Region Transmission-Line Model (TRTLM), aiming at a win-win situation for both fidelity and real-time applicability. The TRTLM stands on a simple triple region transmission-line circuit topology, concisely depicting the inhomogeneous distribution of the electric field. Current and potential distribution are obtained considering the effect of the reaction distribution and concentration field. Two different linearized MIMO state-space models are then established to solve liquid and solid phase concentration fields continuously based on Padé Approximation techniques, achieving less distortion with limited discretization of the electric field. Convenient parameterized governing equations and extreme computation efficiency are both realized. Coupling of electric and concentration field is implemented by solving them alternatively and iteratively. A comparison study of the TRTLM with a Full-Order Model (FOM) and other representative MOR is conducted. The result shows less than 10 mV maximum distortion is reached by the TRTLM, even in extreme conditions, mainly through increasing the fidelity of reaction overpotential and liquid phase polarization. Moreover, hardware validation of the TRTLM is executed under different computation periods (down to 0.01s) based on the dSPACE platform, intuitively proving the real-time applicability of the model. [Display omitted] • A reduced order electrochemical model is proposed with high fidelity and efficiency. • A new topology is constructed to characterize electrical inhomogeneity concisely. • Compared with other reduced order methods in fidelity and computation speed. • A hardware validation is executed under multiple computation periods. [ABSTRACT FROM AUTHOR]

Software defined network (SDN) is a new network architecture proposed by the clean slate research group of Stanford University.The significant feature of this architecture is to decouple the functions of control plane and forwarding plane,and to flexibly forward the network traffic.Based on this,Internet service providers have deployed SDN technology in their backbone network to maximize the utilization of network resources.However,due to the limitation of economic cost and technical conditions,the backbone network of internet service providers must be in the hybrid SDN network for a long time.The studies have shown that single network node failure is inevitable and occurs frequently.Therefore,it is a key scientific problem to study the routing protection me-thod for single network component failure in hybrid SDN networks.In this paper,the route protection method for single network component failure in hybrid SDN network is described,and then two heuristic methods are used to solve the problem.Finally,the proposed heuristic algorithms are tested in real and simulated topologies.The experimental results show that in the traditional backbone network,only a part of the traditional devices need to be upgraded to SDN devices,and the algorithms proposed in this paper can deal with all possible single network node failure cases in the network.

The control of robotic manipulators has become increasingly difficult over recent years due to their high accuracy, performance, speed, and reliability in a variety of applications, such as industry, medicine, research, etc. These serial manipulator systems are extremely complex because their dynamic models include perturbations, parametric variations, coupled nonlinear dynamics, and non-modular dynamics, all of which require robust control for trajectory tracking. This paper compares two control techniques: computational torque control (CTC) and sliding mode control (SMC). In this study, the latter was used for a physical robotic arm with six degrees of freedom (DOF) and online experiments were conducted, which have received little attention in the literature. As a result, the contribution of this work was based on the real-time application of this controller via a self-developing interface. The great resilience of sliding mode controllers to disturbances was also demonstrated in this study. [ABSTRACT FROM AUTHOR]

Facial expression detection is a method to predict human facial emotions. This work is a trending research topic that can be implemented for human-robot interaction. More recently, deep convolutional neural network provides a robust extractor features but tends to be slow in real-time implementations and often requires a large memory and graphics processing units for fast execution. In this article, an efficient CPU-based facial expression detector is proposed using a sequential attention network to improve the baseline performance. The proposed attention network consists of three modules, global representation to capture the global features, channel representation, and dimension representation, which are focused on the channel and using spatial attention to discriminate local features. The efficient partial transfer module is also presented as a light backbone to extract facial features from an image. The entire module is trained and tested on several benchmarks to classify seven facial expressions. As a result, the proposed model reaches an accuracy of 98.18%, 98.75%, 95.63%, and 74.17% on CK+, JAFFE, KDEF, and FER-2013, respectively. It achieves competitive performance when compared to state-of-the-art methods. Lastly, it is integrated with a face detector and runs in real-time without a constraint at 69 frames per second on a CPU. [ABSTRACT FROM AUTHOR]

The goal of achieving autonomous navigation for agricultural robots poses significant challenges, mostly arising from the substantial natural variations in crop row images as a result of weather conditions and the growth stages of crops. The processing of the detection algorithm also must be significantly low for real-time applications. In order to address the aforementioned requirements, we propose a crop row detection algorithm that has the following features: Firstly, a projective transformation is applied to transform the camera view and a color-based segmentation is employed to distinguish crop and weed from the background. Secondly, a clustering algorithm is used to differentiate between the crop and weed pixels. Lastly, a robust line-fitting approach is implemented to detect crop rows. The proposed algorithm is evaluated throughout a diverse range of scenarios, and its efficacy is assessed in comparison to four distinct existing solutions. The algorithm achieves an overall intersection over union (IOU) of 0.73 and exhibits robustness in challenging scenarios with high weed growth. The experiments conducted on real-time video featuring challenging scenarios show that our proposed algorithm exhibits a detection accuracy of over 90% and is a viable option for real-time implementation. With the high accuracy and low inference time, the proposed methodology offers a viable solution for autonomous navigation of agricultural robots in a crop field without damaging the crop and thus can serve as a foundation for future research., Competing Interests: MK was employed at 84.51°. VR was employed at Equipment Technologies. This study received funding from ET Sprayers, Inc.. The funder had the following involvement in the study: Setting the overall study goals and project guidance. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Khan, Rahi, Rajendran, Al Hasan and Anwar.)

Hypochlorite is one of the most potent among the reactive oxygen species owing to its importance in living organisms besides as a bleaching agent in industry, as well as potable water disinfectant. The harmful effects of its under and over expressions to the living systems has necessitated the development of reliable and sensitive sensing probes for real sample monitoring to keep check on its level in industrial/environmental/biological samples. Though a number of reaction based fluorescent probes have been reported, there is a shortage of reversible probes. To fill this void, the present study aims to develop reversible sensing probe. A simple Imidazole based donor-π-acceptor molecular probe has been synthesised. It discloses its pH sensitivity by forming cationic and anionic species at low and high pH values respectively, which consequently affects the magnitude of intramolecular charge transfer (ICT) process. The probe not only exhibits pH sensitivity, but also selectively detects hypochlorite ions employing deprotonation-protonation strategy. The probe which otherwise is weakly emissive in mixed solvent system "lights-up" on interaction with hypochlorite ions which is attributed to the more ordered assembling of the anionic state of the probe as compared to the free probe for which X-ray structure has revealed disordered assembling in solution. Interestingly, the sensing of hypochlorite is reversible with a little precedence in literature over a large number of reports on irreversible reaction based sensing of hypochlorite. The reversible sensing probes contribute significantly to the cost-effective molecular recognition processes. The real-sample application of the probe and the construction of ready to use paper strips for the on-site detection without using expensive equipment has also been explored. [Display omitted] • Imidazole donor-π-acceptor conjugate based molecular probe, ISO is synthesised. • The probe exibits pH sensitivity via the formation of cationic and anionic species. • In solution, reversibly detects ClO− employing deprotonation-protonation strategy. • ClO− induced deprotonation generates the fluorescent assemblies in the solution. • Applied for monitoring ClO− in water samples, on the solid support and live cells. [ABSTRACT FROM AUTHOR]

Over the past decade, perovskite-based nanomaterials have gained notoriety within the scientific community and have been used for a variety of viable applications. The unique structural properties of these materials, namely good direct bandgap, low density of defects, large absorption coefficient, high sensitivity, long charge carrier lifetime, good selectivity, acceptable stability at room temperature, and good diffusion length have prompted researchers to explore their potential applications in photovoltaics, light-emitting devices, transistors, sensors, and other areas. Perovskite-based devices have shown very excellent sensing performances to numerous chemical and biological compounds in both solid and liquid mediums. When used in sensing devices, Perovskite nanomaterials are for the most part able to detect O2, NO2, CO2, H2O, and other smaller molecules. This review article looks at the use of lead-free halide perovskite materials for humidity sensing. A complete description of the underlying mechanisms and charge transport characteristics that are necessary for a thorough comprehension of the sensing performance will be provided. An overview of considerations and potential recommendations for the creation of new lead-free perovskite nanostructure-based sensors is presented. [ABSTRACT FROM AUTHOR]

In this work, a lane departure warning system (LDWS) algorithm for embedded platforms which has restricted resources is proposed. An LDWS consists of two main sub-functions which are lane detection and lane tracking. Although sophisticated methods have been developed for both sub-functions, they usually require high processing power and even GPU processing power. Therefore, they are not applicable for hardware with limited resources. In this work, Hough Transform (HT)-based lane detection algorithm is applied. The vulnerability of HT-based methods against misleading images is eliminated by the proposed filtering algorithm. Main differences of the proposed filtering algorithm from the classical methods in the literature are that it is applied in the parameter space rather than the image, and it is specialized only for determining lanes. In the lane tracking stage, the K-means clustering algorithm has been modified to operate online. In this way, the parameters of the detected lane can be followed adaptively during lane changing or overtaking. Real-time test results on embedded hardware demonstrated that the processing time does not exceed 41.67 ms with an accuracy of over 91.5%. [ABSTRACT FROM AUTHOR]

With the development of mobile payment, the Internet of Things (IoT) and artificial intelligence (AI), smart vending machines, as a kind of unmanned retail, are moving towards a new future. However, the scarcity of data in vending machine scenarios is not conducive to the development of its unmanned services. This paper focuses on using machine learning on small data to detect the placement of the spiral rack indicated by the end of the spiral rack, which is the most crucial factor in causing a product potentially to get stuck in vending machines during the dispensation. To this end, we propose a k-means clustering-based method for splitting small data that is unevenly distributed both in number and in features due to real-world constraints and design a remarkably lightweight convolutional neural network (CNN) as a classifier model for the benefit of real-time application. Our proposal of data splitting along with the CNN is visually interpreted to be effective in that the trained model is robust enough to be unaffected by changes in products and reaches an accuracy of 100%. We also design a single-board computer-based handheld device and implement the trained model to demonstrate the feasibility of a real-time application.

A traffic sign recognition system is crucial for safely operating an autonomous driving car and efficiently managing road facilities. Recent studies on traffic sign recognition tasks show significant advances in terms of accuracy on several benchmarks. However, they lack performance evaluation in driving cars in diverse road environments. In this study, we develop a traffic sign recognition framework for a vehicle to evaluate and compare deep learning-based object detection and tracking models for practical validation. We collect a large-scale highway image set using a camera-installed vehicle for training models, and evaluate the model inference during a test drive in terms of accuracy and processing time. In addition, we propose a novel categorization method for urban road scenes with possible scenarios. The experimental results show that the YOLOv5 detector and strongSORT tracking model result in better performance than other models in terms of accuracy and processing time. Furthermore, we provide an extensive discussion on possible obstacles in traffic sign recognition tasks to facilitate future research through numerous experiments for each road condition.

Real-time clinical applications such as robotic lung surgery, tumor localization, atelectasis diagnosis, tumor motion prediction for radiation therapy of lung cancer, or surgery training are in need of biomechanical models of lungs, not necessarily highly accurate, but with good computational properties. These properties can include one or several of the following: low computation time, low memory resource requirement, a low number of parameters, and ease of parameter identification in real-time. Among the numerous existing models of lung parenchyma, some may be well suited for real-time applications; however, they should be extensively assessed against both accuracy and computational efficiency criteria to make an informed choice depending on the requirements of the application. After demonstrating how to derive a real-time compliant force-indentation model from a unixial stress-strain model with rational expression, the core purpose of this paper is to propose such an evaluation of selected models in fitting human lung parenchyma experimental and synthetic data of uniaxial tension. Furthermore, new uniaxial stress-strain models are developed based on an empirical observation of the volumetric behavior of the lungs along with an emphasis on computational performance. These new proposed models are competitive with existing one in terms of computational efficiency and compliance with experimental and synthetic data. One of them reduces the prediction error by 2 compared to other investigated models while maintaining an excellent adjusted coefficient of determination between 0.999 and 1 across various datasets. It exhibits excellent real-time capabilities with an explicit rational expression, only 3 parameters and linear numerator and denominator in the parameters. It is computed with only 20 floating point operations (flops) while another proposed model even requires as few as 2 flops., Competing Interests: Declaration of competing interest The authors of the article entitled « Design and comparison of lung parenchyma stress–strain real-time uniaxial computationally efficient models » , namely Mr Fabien Courreges, Mr Boris Melloni and Mr Joseph Absi declare having carried out their research independently from any third party influence and have no conflict of interest in regards of the exposed methodology and results., (Copyright © 2024. Published by Elsevier Ltd.)

Undoubtedly, one of the most important areas of information technology implementation is closely related to the provision of everyday human needs and security. Special attention is drawn to developments that are designed to help people in an emergency. An example is a software product that allows users to call an ambulance at the touch of a button and track its location on the road in the event of deteriorating human health. An important function for the patient-user is to ensure continuous tracking of the ambulance throughout the movement of the ambulance. The implementation of such a solution requires reliable operation, integrity of internal components, easy accessibility and ease of use. The aim of this work is to create a software product that helps ambulance crew drivers to plan the optimal route to the place of call, as well as allows patients to monitor the movement of the carriage and determine the time of arrival of medics. The system has developed a reliable, dedicated server that processes data such as: user information, calls, crews, carriages. The possibility of receiving real-time information about the location of the carriage on the way to the patient has been implemented. The position of the object is calculated by using a GPS receiver placed on it, which receives and processes signals from satellites of the GPS space segment - the global positioning system. The C # programming language, the ASP.NET Core framework, the PostgreSQL database management system, the JetBrains Rider development environment, and the Heroku server test hosting platform were used for implementation. The result of the development is a mobile application that will allow the user to call an ambulance and track their carriage. For drivers of carriages convenient work at performance of the duties is provided. Interaction between mobile applications is carried out by means of the server which works with all users of mobile applications, providing fast and timely data transfer. [ABSTRACT FROM AUTHOR]

For plug-in hybrid electric vehicles, the predictive energy management can be implemented in real-time with a short-term prediction of future driving condition. However, the completely precise prediction of future driving conditions and the determination of a reasonable predictive horizon remain quite difficult. To solve the above problems, we propose a variable horizon-based predictive energy management control method with adaptive SOC constraint, and more importantly, a reasonable predictive horizon is determined to balance global energy optimality and the cost of data sampling for the connected vehicles. Firstly, an integrated velocity predictor is proposed by combining Markov model and speed limits. Considering that the effect of information amount on prediction accuracy is opposite to that of predictive horizon, a new evaluation metric, namely predictive information entropy, is proposed to objectively evaluate the velocity prediction accuracy under different amounts of available information. Then, a variable horizon-based predictive energy management strategy is developed with the dynamically updated reference SOC. By comprehensively considering the effects of predictive horizon on prediction accuracy, real-time application and global optimality, a suitable predictive horizon should be set to 20s–30s to achieve a better fuel economy with a relatively low data sampling cost. • Propose a variable horizon-based predictive energy management. • Determine a suitable predictive horizon. • Propose predictive information entropy to evaluate prediction accuracy. [ABSTRACT FROM AUTHOR]

With increasing use of antibiotics, the development of antibiotic-resistant pathogens poses a serious threat to human health and the environment. Photocatalytic inactivation of these harmful pathogens is one of the novel and non-antibiotic treatments. The study fabricated Ag NPs decorated CdZnS QDs via a facile and biological co-precipitation method using L. camara plant extract as a green alternative to treat the toxic chemicals. The fabricated Ag/CdZnS QDs (NCs) were prepared for the efficient treatment of antibiotic-resistant pathogens as they raise a major global concern. The fabricated NCs were characterized with various characterization techniques to verify its physicochemical properties. The fabricated NCs have shown excellent photo-sterilization performance of 97 % against S. aureus. The excellent activity was attributed to the decoration of Ag NPs on CdZnS QDs as it helped in shortening band gap, improved visible light absorption ability, increased active sites, and boosted photogenerated electron/hole pairs stability. Radical trapping experiment and ESR analysis indicated the involvement of •OH and h+ in the photoinactivation of bacteria. The photo sterilization reaction of NCs was carried out under different environmental conditions, including light and dark conditions and different pH conditions. The experiment was carried out in sewage-treated water in order to test the real-time application, and the fabricated NCs achieved excellent 95.9 % photo-inactivation of S. aureus cells in sewage treated water and the Chemical Oxygen Demand (COD) of the system was increased after photo inactivation treatment. The fabricated NCs have also shown excellent reusable efficiency of 95% after six runs and the photostability and anti-corrosive nature of NCs were confirmed. The study provides an insight for the employment of photocatalysis for the sterilization of pathogens in real time aquatic environment across the globe. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper proposes a participating-domain segmentation based server selection scheme in a delay-sensitive distributed communication approach to reducing the computational time for solving the server selection problem. The proposed scheme divides the users' participation domain into a number of regions. The delay between a region and a server is a function of locations of the region and the server. The length between the region and the server is considered based on conservative approximation. The location of the region is determined regardless of the number of users and their participation location. The proposed scheme includes two phases. The first phase uses the server finding process and determines the number of users that are accommodated from each region by each server, instead of actual server selection, to reduce the computational complexity. The second phase uses the delay improvement process and determines the overall delay and the selected server for each user. We formulate an integer linear programming problem for the server selection in the proposed scheme and evaluate the performance in terms of computation time and delay. The numerical results indicate that the computational time using the proposed scheme is smaller than that of the conventional scheme, and the effectiveness of the proposed scheme enhances as the number of users increases.

Solving the forward kinematics of parallel robots efficiently is important for real-time applications. However, it remains a difficult problem due to its high nonlinearity. This paper combines artificial neural networks and the Global Newton-Raphson with Monotonic Descent (GNRMD) algorithm to decrease the training sets of neural networks while avoiding divergence problem. Furthermore, simplified Newton iteration is introduced to reduce the duration of solution time. The proposed method is demonstrated taking a Stewart platform as an example and the nonlinear equations are established with the geometrical method. Based on the continuous characteristic of real-time applications, the result of the previous solution cycle is used as the initial value of the current solution cycle. Moreover, a threshold adjusting the effective scope of GNRMD algorithm and simplified Newton iteration is set to balance the efficiency and number of iteration. The performance of the algorithm is verified in the environment of Microsoft Visual Studio 2013 based on the continuous feedback of the Stewart platform. Besides, it is compared with GNRMD algorithm and a higher-order numerical method. The results indicate that the proposed algorithm can improve the efficiency of solving the forward kinematics problem.

In the real-time outdoor application of computer vision-based systems, haze removal, a preprocessing technique that can recover clear images from foggy ones, is necessary for object detection. Therefore, in this paper, an efficient haze removal method suitable for hardware design is proposed to obtain high quality fog-free images. Based on the atmosphere scattering model and the dark channel prior method, the atmospheric light of the whole image and the transmission map could be extracted. These are important and necessary parameters of the recovery model. Instead of using single global atmospheric light to restore foggy image, a local atmospheric light estimation method is applied in the proposed design to achieve optimal results. To ensure that the overall image is consistent without block artifacts, dynamic adjustment of local atmospheric light is made based on global atmospheric light. Additionally, to obtain a transmission map, a refined estimation method is performed to ease the halo effect. In terms of both quantitative and qualitative evaluations, the simulation results indicate that the proposed design exhibits superior performance without color oversaturation and distortion. To meet the requirements of real-time applications, a six-stage very-large-scale integration (VLSI) architecture for the proposed algorithm is implemented by using TSMC 0.13-um technology. The synthesis results show that the design yields a processing rate of approximately 200 Mpixels/s, which is rapid enough to facilitate Full HD resolution at 30 fps in real time.

In this paper a single-phase transformer-less hybrid series active filter (THSeAF) based on duo-neutral-point-clamped (D-NPC) converter to address distribution level power quality is proposed to investigate experimentally the efficiency of the hardware-in-the-loop (HIL) implementation for power electronics applications. This benchmark contributes to demonstrating the capability and efficiency of such real-time implementation for smart grid power quality (PQ) analysis which requires fast switching process with small sampling time. Such applications require the compensator to address major power quality issues related to a nonlinear load. This compensator presents an efficient and reliable solution for future grid applications to overcome voltage and current related issues as well as assisting the integration of renewables for a sustainable supply. The controller extracts voltage and current harmonics to be compensated. A proportional and resonant (P + R) regulator produces switching signals for the D-NPC converter. The paper demonstrates the reliability of the HIL simulation for power electronic applications assessing power quality related issues where a wide range of switching frequency is under study. A combination of simulation and real-time results are carried out to validate the performance and viability of the HIL implementation.

Lane departure warning is one important feature in Advanced Driver Assistance Systems (ADAS), which aims to improve overall safety on the road. However, challenges such as inconsistent shadows and fading lane markings often plague the road surface and cause the lane detection system to produce false warnings. Users are aggravated by the warning and tend to disable this safety feature. This paper proposes an efficient Gabor filtering-based lane detection method to overcome the aforementioned conditions and improves the accuracy of lane departure warning system. Furthermore, it serves as a cost-effective solution to a lane departure warning problem, allowing it to be widely deployed. It is heuristically found that lane marking has a general directional property, which can be further enhanced by Gabor filter while suppressing inconsistent road shadows and road markers. Enhanced lane markings are then subjected to adaptive canny edge detection to extract distinct edge markings. Lastly, Hough transformation is applied to label the correct lane candidates on the road surface. Furthermore, we generate a dataset of Malaysia road with various driving conditions. As a proof of concept, a lane departure warning system is built based on the proposed lane detection method, which is able to achieve an accuracy of 93.67% for lane detection and 95.24% for lane departure warning tested on our challenging dataset. The codes are implemented on Raspberry pi 3B and installed in a vehicle for real-time application. The codes are multithreaded and found to achieve a desirable frame speed of 20 fps at 75% CPU utilization. [ABSTRACT FROM AUTHOR]

This paper presents a vector painting system for digital artworks. We first propose Temporal Diffusion Curve (TDC), a new form of vector graphics, and a novel random-access solver for modeling the evolution of strokes. With the help of a procedural stroke processing function, the TDC strokes can achieve various shapes and effects for multiple art styles. Based on these, we build a painting system of great potential. Thanks to the random-access solver, our method has real-time performance regardless of the rendering resolution, provides straightforward editing possibilities on strokes both at runtime and afterward, and is effective and straightforward for art production. Compared with the previous Diffusion Curve, our method uses strokes as the basic graphics primitives, which are able to intersect each other and much more consistent with the intuition and painting habits of human. We finally demonstrate that professional artists can create multiple genres of artworks with our painting system. [ABSTRACT FROM AUTHOR]

Load shedding (LS) is implemented by distribution utilities (DUs) in addressing power supply insufficiency problems to avoid DU system damages. This is commonly implemented by the installation of LS relays in every DU feeder. However, in either scheduled or unscheduled supply disruptions, a huge amount of unnecessary de-loading is taking place in a feeder level LS implementation. In addition, the consumers connected to a de-loaded feeder are in total blackout, that is, consumers have no choice over which appliances to spare from being de-loaded. This study proposes an LS implementation that replaces feeder-level de-loading by a finer consumer-appliance-level de-loading and allows consumers to have some control over their de-loading. In this method, consumers can set an appliance priority level to their selected connected loads at a given time, to avoid a total blackout. Furthermore, to deal with the enormous data involved in this proposed method, both centralised and distributed optimisation approaches are employed to expedite the system processing response. Simulations are conducted to verify the proposed method's functionality. Lastly, economic analysis is done to assess the proposed method's viability.