Texture classification plays a crucial role in applications ranging from object recognition and product design to surface exploration. Utilizing deep learning methods with sensors, such as accelerometers, offers a way...
Texture classification plays a crucial role in applications ranging from object recognition and product design to surface exploration. Utilizing deep learning methods with sensors, such as accelerometers, offers a way to identify key surface features without the need to precisely replicate human touch. A Contextually Guided Convolutional Neural Network (CG-CNN) employs contextual guidance by developing auxiliary tasks during its training. These tasks offer implicit, yet rigorous, internal supervision signals. When trained with these subtasks, CG-CNN learns to represent the innate structure and patterns within the data, resulting in robust, transferrable, and local/contextual-neighborhood-preserving domain representations. This paper extends the CG-CNN framework for texture classification by integrating semisupervised learning. Empirical evaluations on the VibTac-12 texture dataset reveal that CG-CNN effectively generalizes to novel and unfamiliar textures, even when trained with scarce labeled examples. By harnessing vast amounts of unlabeled, contextually relevant data alongside the labeled samples, CG-CNN ensures robust and precise texture classification. Such advancements hold promise for applications in robotics, prosthetics, and haptic interfaces.
Undeniably, autonomous vehicles are the most dramatic evolution of transportation yet. They offer new safety, efficiency, and convenience levels that have never been seen before. Still, some natural and unexpected con...
详细信息
ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
Undeniably, autonomous vehicles are the most dramatic evolution of transportation yet. They offer new safety, efficiency, and convenience levels that have never been seen before. Still, some natural and unexpected conditions continually strike a blow against the safety of self-driving systems, such as potholes on roads. This paper introduces a new, cloud-based safety solution for autonomous vehicles through artificial intelligence and implementation with the YOLOv8 object detection model, coupled with the deep learning platform Weights & Biases, or wandb. As the real-time pothole-detecting system is developed, issues related to navigation appear while increasing the safety level. This work adopts the YOLOv8 object detection model in the use case due to its higher accuracy and much faster object detection rate. This can classify and predict the object boundaries of a single network, thus being a good fit for quick and accurate detection. Upgrades will be made in sync with new conditions in roads and types of potholes as the right solution gains ready to change the future of transportation about autonomous transportation, paving the path for safer and more reliable self-driving vehicles. It directly addresses the acute challenge of pothole-related hazards. It builds the way to an even safer and more efficient future for autonomous mobility, thus showing tremendous potential for transforming advanced AI technologies.
The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating “Air Canvas” facilitated by ...
详细信息
ISBN:
(数字)9798350354379
ISBN:
(纸本)9798350354386
The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating “Air Canvas” facilitated by OpenCV. This AI-driven virtual assistant offers users a natural and intuitive experience, allowing them to effortlessly perform tasks such as browsing the web, interacting with a chatbot, and executing dynamic voice- controlled actions. Moreover, the system showcases advanced capabilities including motion detection and facial recognition with an accuracy of 95% in multiple runs. Leveraging the power of computer vision, the Air Canvas feature empowers users to express creativity through fluid hand gestures, while voice commands effortlessly manage diverse tasks. This innovative project presents an approachable way to interact with technology in the world of AI.
Metamaterials have gained significant interest in antenna technology nowadays due to their exceptional features. This article presents metamaterial (MTM) based two new antennas with a vision to precisely enhance the n...
详细信息
ISBN:
(数字)9798331519094
ISBN:
(纸本)9798331519100
Metamaterials have gained significant interest in antenna technology nowadays due to their exceptional features. This article presents metamaterial (MTM) based two new antennas with a vision to precisely enhance the number of bands and maintain a suitable bandwidth level. A sequential rise in band numbers from 2 to 4 and then to 5 is noticed by the applications of split-ring resonator (SRR) and complementary structure of dual negative metamaterial (dual NG-MTM) in a standard patch antenna. Initially, a dual NG-MTM was attached at the back of the patch antenna and it presented four separate bands at 2 GHz, 6.8 GHz, 9.1 GHz, and 9.6 GHz, respectively with comparatively larger bandwidth. At last, inclusion of an SRR takes the band-width to another level in addition to providing five bands and its electrical size is then compact as 0.22λ 0 × 0.19λ 0 × 0.0006λ 0 , with a ka (antenna size indicator) value of less than 1. These extraordinary features of the above-mentioned MTM antennas lead to promising applications including television broadcasting and 5G system.
Wildfires are one of the most destructive natural disasters that cause significant harm to both humans and the environment. Predicting their spread is critical for disaster management and preparedness. In this study, ...
Wildfires are one of the most destructive natural disasters that cause significant harm to both humans and the environment. Predicting their spread is critical for disaster management and preparedness. In this study, we have utilized machine learning algorithms, including Decision Tree Regression, XG Boost Regression, and Artificial Neural Networks, to predict the spread of wildfires using the Next Day Wildfire dataset. The dataset includes satellite images, weather, and geography conditions aggregated across the United States from 2012 to 2020. We preprocessed and engineered the dataset which includes the features such as elevation, wind direction and speed, temperature, humidity, precipitation, drought index, vegetation index, energy release component, and population density. We evaluated the models using the Root Mean Squared Error (RMSE) metric and found that the Decision Tree Regression algorithm performed the best with the lowest RMSE score. Our study highlights the potential of machine learning algorithms in predicting the spread of wildfires, which can aid in better disaster management and preparedness efforts.
Multi-modality classification has flourished in recent years. Traditional methods mainly focus on advancing deep neural networks (DNN) to meet high performance. However, the interpretability of these methods remains b...
Multi-modality classification has flourished in recent years. Traditional methods mainly focus on advancing deep neural networks (DNN) to meet high performance. However, the interpretability of these methods remains blind due to the complexity and ambiguity of DNN, which also causes distrust. This problem is enlarged in sensitive areas, such as biomedical computing. Hence, we propose a novel dual trustworthy mechanism for multi-modality classification (DTMC), which can make the process and results of DNN more credible and interpretable while increasing performance. Specifically, a confidence attention mechanism is performed from local and global views to improve the process’ confidence by evaluating the attention scores and distinguishing the abnormal information. A confidence probability mechanism from local and global perspectives is conducted in the prediction stage to enhance the results’ confidence. Extensive experiments on multi-modality medical classification datasets show superior performance with the interpretability of the proposed method compared to the state-of-the-art (SOTA) methods. Our resources are open at https://***/ghh1125/data.
In most digital cameras, the sensor uses a Bayer filter array to capture the image. This array records only one colour, either blue or green or red for every image pixel, resulting in a mosaic image. To retrieve the m...
详细信息
ISBN:
(数字)9798331518394
ISBN:
(纸本)9798331518400
In most digital cameras, the sensor uses a Bayer filter array to capture the image. This array records only one colour, either blue or green or red for every image pixel, resulting in a mosaic image. To retrieve the missing colour information in the captured image making use of cross-channel interpolation is referred to as demosaicing. Demosaicing, especially in the context of colour demosaicing (CDM), plays a vital role as the first step in obtaining high-quality colour images with single-chip cameras. Traditional demosaicing methods, used to reconstruct colour images from raw sensor data in digital cameras, have several drawbacks. They often result in a loss of spatial resolution, introduce artifacts, lack robustness in challenging conditions, and may require manual tuning for optimal performance. In contrast, neural networks offer advantages like end-to-end learning, improved image quality, robustness, flexibility, and state-of-the-art performance. They learn complex mappings directly from raw data, adapt to various conditions, and produce visually pleasing, high-resolution images. However, they require substantial training data and computational resources, making them less suitable for resource-constrained applications. In this paper, conventional interpolation methods are compared to deep-learning based approaches for image demosaicing. To validate the approach to image demosaicing, aerial images captured from the Mars Colour Camera have been used.
This research analyzes the challenges faced by Sign Language Recognition (SLR) systems by evaluating the performance of EfficientNetB3, Inception-v3, and GoogLeNet models. Using a dataset of 27,000 American Sign Langu...
详细信息
ISBN:
(数字)9798331532420
ISBN:
(纸本)9798331532437
This research analyzes the challenges faced by Sign Language Recognition (SLR) systems by evaluating the performance of EfficientNetB3, Inception-v3, and GoogLeNet models. Using a dataset of 27,000 American Sign Language (ASL) images across 27 classes, EfficientNetB3 emerged as the top performer. After fine-tuning the model with the full dataset, it achieved a test accuracy of 99.53%. We then integrated this optimized model into a real-time, user-friendly Android app for sign language recognition, offering a valuable tool for enhancing communication accessibility, particularly for the deaf and hard-of-hearing. This research work contributes to advancing SLR and promoting inclusive communication.
Rigorous simulations challenge recent claims that metalenses outperform conventional diffractive lenses, such as fresnel zone plates (FZPs), in focusing efficiency at high numerical apertures (NAs). Across various len...
详细信息
The growing demand for improved spectral efficiency is one of the main challenges for the upcoming beyond fifth-generation wireless mobile communications networks. While massive multiple-input multiple-output (MIMO) t...
详细信息
ISBN:
(数字)9798350387414
ISBN:
(纸本)9798350387421
The growing demand for improved spectral efficiency is one of the main challenges for the upcoming beyond fifth-generation wireless mobile communications networks. While massive multiple-input multiple-output (MIMO) technology has been demonstrating its potential in achieving higher spectral efficiency, the persistent problem of pilot contamination poses a significant hurdle for these systems. To address this issue, the Rate-Splitting Multiple Access (RSMA) framework has emerged as a potential solution. In this paper, we present a novel approach that leverages reinforcement learning (RL) with the Deep Deterministic Policy Gradient (DDPG) algorithm to maximize the sum spectral efficiency (SUM-SE) in a massive MIMO system implementing the RSMA framework with all users sharing a single pilot. The numerical results indicate that the proposed DDPG-based method is a competitive tool for optimizing the SUM-SE in massive MIMO scenarios employing the RSMA framework.
暂无评论