Multilayer Perceptron-Based Error Compensation for Automatic On-the-Fly Camera Orientation Estimation Using a Single Vanishing Point from Road Lane.

This study introduces a multilayer perceptron (MLP) error compensation method for real-time camera orientation estimation, leveraging a single vanishing point and road lane lines within a steady-state framework. The research emphasizes cameras with a roll angle of 0°, predominant in autonomous vehicle contexts. The methodology estimates pitch and yaw angles using a single image and integrates two Kalman filter models with inputs from image points (u, v) and derived angles (pitch, yaw). Performance metrics, including avgE, minE, maxE, ssE, and Stdev, were utilized, testing the system in both simulator and real-vehicle environments. The outcomes indicate that our method notably enhances the accuracy of camera orientation estimations, consistently outpacing competing techniques across varied scenarios. This potency of the method is evident in its adaptability and precision, holding promise for advanced vehicle systems and real-world applications.

Toxicological assessment of divalent ion-modified ZnO nanomaterials through artificial intelligence and in vivo study.

The nanotechnology-driven industrial revolution widely relies on metal oxide-based nanomaterial (NM). Zinc oxide (ZnO) production has rapidly increased globally due to its outstanding physical and chemical properties and versatile applications in industries including cement, rubber, paints, cosmetics, and more. Nevertheless, releasing Zn2+ ions into the environment can profoundly impact living systems and affect water-based ecosystems, including biological ones. In aquatic environments, Zn2+ ions can change water properties, directly influencing underwater ecosystems, especially fish populations. These ions can accumulate in fish tissues when fish are exposed to contaminated water and pose health risks to humans who consume them, leading to symptoms such as nausea, vomiting, and even organ damage. To address this issue, safety of ZnO NMs should be enhanced without altering their nanoscale properties, thus preventing toxic-related problems. In this study, an eco-friendly precipitation method was employed to prepare ZnO NMs. These NMs were found to reduce ZnO toxicity levels by incorporating elements such as Mg, Ca, Sr, and Ba. Structural, morphological, and optical properties of synthesized NMs were thoroughly investigated. In vitro tests demonstrated potential antioxidative properties of NMs with significant effects on free radical scavenging activities. In vivo, toxicity tests were conducted using Oreochromis mossambicus fish and male Swiss Albino mice to compare toxicities of different ZnO NMs. Fish and mice exposed to these NMs exhibited biochemical changes and histological abnormalities. Notably, ZnCaO NMs demonstrated lower toxicity to fish and mice than other ZnO NMs. This was attributed to its Ca2+ ions, which could enhance body growth metabolism compared to other metals, thus improving material safety. Furthermore, whether nanomaterials' surface roughness might contribute to their increased toxicity in biological systems was investigated utilizing computer vision (CV)-based AI tools to obtain SEM images of NMs, providing valuable image-based surface morphology data that could be correlated with relevant toxicology studies.

Implementation of Field-Programmable Gate Array Platform for Object Classification Tasks Using Spike-Based Backpropagated Deep Convolutional Spiking Neural Networks.

This paper investigates the performance of deep convolutional spiking neural networks (DCSNNs) trained using spike-based backpropagation techniques. Specifically, the study examined temporal spike sequence learning via backpropagation (TSSL-BP) and surrogate gradient descent via backpropagation (SGD-BP) as effective techniques for training DCSNNs on the field programmable gate array (FPGA) platform for object classification tasks. The primary objective of this experimental study was twofold: (i) to determine the most effective backpropagation technique, TSSL-BP or SGD-BP, for deeper spiking neural networks (SNNs) with convolution filters across various datasets; and (ii) to assess the feasibility of deploying DCSNNs trained using backpropagation techniques on low-power FPGA for inference, considering potential configuration adjustments and power requirements. The aforementioned objectives will assist in informing researchers and companies in this field regarding the limitations and unique perspectives of deploying DCSNNs on low-power FPGA devices. The study contributions have three main aspects: (i) the design of a low-power FPGA board featuring a deployable DCSNN chip suitable for object classification tasks; (ii) the inference of TSSL-BP and SGD-BP models with novel network architectures on the FPGA board for object classification tasks; and (iii) a comparative evaluation of the selected spike-based backpropagation techniques and the object classification performance of DCSNNs across multiple metrics using both public (MNIST, CIFAR10, KITTI) and private (INHA_ADAS, INHA_KLP) datasets.

Hydrothermal synthesis of CuO@MnO2 on nitrogen-doped multiwalled carbon nanotube composite electrodes for supercapacitor applications.

Nitrogen-doped multiwalled carbon nanotubes (N-MWCNTs) have been used to fabricate nanostructured materials for various energy devices, such as supercapacitors, sensors, batteries, and electrocatalysts. Nitrogen-doped carbon-based electrodes have been widely used to improve supercapacitor applications via various chemical approaches. Based on previous studies, CuO@MnO2 and CuO@MnO2/N-MWCNT composites were synthesized using a sonication-supported hydrothermal reaction process to evaluate their supercapacitor properties. The structural and morphological properties of the synthesized composite materials were characterized via Raman spectroscopy, XRD, SEM, and SEM-EDX, and the morphological properties of the composite materials were confirmed by the nanostructured composite at the nanometer scale. The CuO@MnO2 and CuO@MnO2/N-MWCNT composite electrodes were fabricated in a three-electrode configuration, and electrochemical analysis was performed via CV, GCD, and EIS. The composite electrodes exhibited the specific capacitance of ~ 184 F g-1 at 0.5 A g-1 in the presence of a 5 M KOH electrolyte for the three-electrode supercapacitor application. Furthermore, it exhibited significantly improved specific capacitances and excellent cycling stability up to 5000 GCD cycles, with a 98.5% capacity retention.

Sonication-supported synthesis of cobalt oxide assembled on an N-MWCNT composite for electrochemical supercapacitors via three-electrode configuration.

The Co3O4@N-MWCNT composite was synthesized by a sonication-supported thermal reduction process for supercapacitor applications. The structural and morphological properties of the materials were characterized via Raman, XRD, XPS, SEM-EDX, and FE-TEM analysis. The composite electrode was constructed into a three-electrode configuration and examined by using CV, GCD and EIS analysis. The demonstrated electrochemical value of ~ 225 F/g at 0.5 A/g by the electrode made it appropriate for potential use in supercapacitor applications.

Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platforms.

In recent times, the usage of modern neuromorphic hardware for brain-inspired SNNs has grown exponentially. In the context of sparse input data, they are undertaking low power consumption for event-based neuromorphic hardware, specifically in the deeper layers. However, using deep ANNs for training spiking models is still considered as a tedious task. Until recently, various ANN to SNN conversion methods in the literature have been proposed to train deep SNN models. Nevertheless, these methods require hundreds to thousands of time-steps for training and still cannot attain good SNN performance. This work proposes a customized model (VGG, ResNet) architecture to train deep convolutional spiking neural networks. In this current study, the training is carried out using deep convolutional spiking neural networks with surrogate gradient descent backpropagation in a customized layer architecture similar to deep artificial neural networks. Moreover, this work also proposes fewer time-steps for training SNNs with surrogate gradient descent. During the training with surrogate gradient descent backpropagation, overfitting problems have been encountered. To overcome these problems, this work refines the SNN based dropout technique with surrogate gradient descent. The proposed customized SNN models achieve good classification results on both private and public datasets. In this work, several experiments have been carried out on an embedded platform (NVIDIA JETSON TX2 board), where the deployment of customized SNN models has been extensively conducted. Performance validations have been carried out in terms of processing time and inference accuracy between PC and embedded platforms, showing that the proposed customized models and training techniques are feasible for achieving a better performance on various datasets such as CIFAR-10, MNIST, SVHN, and private KITTI and Korean License plate dataset.

Ultrasonication-mediated nitrogen-doped multiwalled carbon nanotubes involving carboxy methylcellulose composite for solid-state supercapacitor applications.

In this study, a novel nanohybrid composite containing nitrogen-doped multiwalled carbon nanotubes/carboxymethylcellulose (N-MWCNT/CMC) was synthesized for supercapacitor applications. The synthesized composite materials were subjected to an ultrasonication-mediated solvothermal hydrothermal reaction. The synthesized nanohybrid composite electrode material was characterized using analytical methods to confirm its structure and morphology. The electrochemical properties of the composite electrode were investigated using cyclic voltammetry (CV), galvanic charge-discharge, and electrochemical impedance spectroscopy (EIS) using a 3 M KOH electrolyte. The fabricated composite material exhibited unique electrochemical properties by delivering a maximum specific capacitance of approximately 274 F g-1 at a current density of 2 A g-1. The composite electrode displayed high cycling stability of 96% after 4000 cycles at 2 A g-1, indicating that it is favorable for supercapacitor applications.

Vision-Based Tactile Sensor Mechanism for the Estimation of Contact Position and Force Distribution Using Deep Learning.

This work describes the development of a vision-based tactile sensor system that utilizes the image-based information of the tactile sensor in conjunction with input loads at various motions to train the neural network for the estimation of tactile contact position, area, and force distribution. The current study also addresses pragmatic aspects, such as choice of the thickness and materials for the tactile fingertips and surface tendency, etc. The overall vision-based tactile sensor equipment interacts with an actuating motion controller, force gauge, and control PC (personal computer) with a LabVIEW software on it. The image acquisition was carried out using a compact stereo camera setup mounted inside the elastic body to observe and measure the amount of deformation by the motion and input load. The vision-based tactile sensor test bench was employed to collect the output contact position, angle, and force distribution caused by various randomly considered input loads for motion in X, Y, Z directions and RxRy rotational motion. The retrieved image information, contact position, area, and force distribution from different input loads with specified 3D position and angle are utilized for deep learning. A convolutional neural network VGG-16 classification modelhas been modified to a regression network model and transfer learning was applied to suit the regression task of estimating contact position and force distribution. Several experiments were carried out using thick and thin sized tactile sensors with various shapes, such as circle, square, hexagon, for better validation of the predicted contact position, contact area, and force distribution.

Surface Thermo-Dynamic Characterization of Poly (Vinylidene Chloride-Co-Acrylonitrile) (P(VDC-co-AN)) Using Inverse-Gas Chromatography and Investigation of Visual Traits Using Computer Vision Image Processing Algorithms.

The Inverse Gas Chromatography (IGC) technique has been employed for the surface thermo-dynamic characterization of the polymer Poly(vinylidene chloride-co-acrylonitrile) (P(VDC-co-AN)) in its pure form. IGC attributes, such as London dispersive surface energy, Gibbs free energy, and Guttman Lewis acid-base parameters were analyzed for the polymer (P(VDC-co-AN)). The London dispersive surface free energy ( γ S L ) was calculated using the Schultz and Dorris-Gray method. The maximum surface energy value of (P(VDC-co-AN )) is found to be 29.93 mJ·m - 2 and 24.15 mJ·m - 2 in both methods respectively. In our analysis, it is observed that the γ S L values decline linearly with an increase in temperature. The Guttman-Lewis acid-base parameter K a , K b values were estimated to be 0.13 and 0.49. Additionally, the surface character S value and the correlation coefficient were estimated to be 3.77 and 0.98 respectively. After the thermo-dynamic surface characterization, the (P(VDC-co-AN)) polymer overall surface character is found to be basic. The substantial results revealed that the (P(VDC-co-AN)) polymer surface contains more basic sites than acidic sites and, hence, can closely associate in acidic media. Additionally, visual traits of the polymer (P(VDC-co-AN)) were investigated by employing Computer Vision and Image Processing (CVIP) techniques on Scanning Electron Microscopy (SEM) images captured at resolutions ×50, ×200 and ×500. Several visual traits, such as intricate patterns, surface morphology, texture/roughness, particle area distribution ( D A ), directionality ( D P ), mean average particle area ( µ a v g ) and mean average particle standard deviation ( σ a v g ), were investigated on the polymer's purest form. This collective study facilitates the researches to explore the pure form of the polymer Poly(vinylidene chloride-co-acrylonitrile) (P(VDC-co-AN )) in both chemical and visual perspective.

Automatic Distortion Rectification of Wide-Angle Images Using Outlier Refinement for Streamlining Vision Tasks.

The study proposes an outlier refinement methodology for automatic distortion rectification of wide-angle and fish-eye lens camera models in the context of streamlining vision-based tasks. The line-members sets are estimated in a scene through accumulation of line candidates emerging from the same edge source. An iterative optimization with an outlier refinement scheme was applied to the loss value, to simultaneously remove the extremely curved outliers from the line-members set and update the robust line members as well as estimating the best-fit distortion parameters with lowest possible loss. The proposed algorithm was able to rectify the distortions of wide-angle and fish-eye cameras even in extreme conditions such as heavy illumination changes and severe lens distortions. Experiments were conducted using various evaluation metrics both at the pixel-level (image quality, edge stretching effects, pixel-point error) as well as higher-level use-cases (object detection, height estimation) with respect to real and synthetic data from publicly available, privately acquired sources. The performance evaluations of the proposed algorithm have been investigated using an ablation study on various datasets in correspondence to the significance analysis of the refinement scheme and loss function. Several quantitative and qualitative comparisons were carried out on the proposed approach against various self-calibration approaches.

Feasible Self-Calibration of Larger Field-of-View (FOV) Camera Sensors for the Advanced Driver-Assistance System (ADAS).

This paper proposes a self-calibration method that can be applied for multiple larger field-of-view (FOV) camera models on an advanced driver-assistance system (ADAS). Firstly, the proposed method performs a series of pre-processing steps such as edge detection, length thresholding, and edge grouping for the segregation of robust line candidates from the pool of initial distortion line segments. A novel straightness cost constraint with a cross-entropy loss was imposed on the selected line candidates, thereby exploiting that novel loss to optimize the lens-distortion parameters using the Levenberg-Marquardt (LM) optimization approach. The best-fit distortion parameters are used for the undistortion of an image frame, thereby employing various high-end vision-based tasks on the distortion-rectified frame. In this study, an investigation was carried out on experimental approaches such as parameter sharing between multiple camera systems and model-specific empirical γ -residual rectification factor. The quantitative comparisons were carried out between the proposed method and traditional OpenCV method as well as contemporary state-of-the-art self-calibration techniques on KITTI dataset with synthetically generated distortion ranges. Famous image consistency metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and position error in salient points estimation were employed for the performance evaluations. Finally, for a better performance validation of the proposed system on a real-time ADAS platform, a pragmatic approach of qualitative analysis has been conducted through streamlining high-end vision-based tasks such as object detection, localization, and mapping, and auto-parking on undistorted frames.