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A fast method for load detection and classification using texture image classification in intelligent transportation systems

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MULTIMEDIA TOOLS AND applications 2024年第32期83卷 78609页

作者： Eghbal, Najmeh Anaraki, Behzad Ghayoumi Cheraghi-Shami, Farideh Sadjad Univ Dept Elect & Biomed Engn Mashhad Iran Islamic Azad Univ Dept Biomed Engn Mashhad Branch Mashhad Iran Roshan Tolou Shargh Co Mashhad Iran

The surveillance and management of cargo fleets is a crucial objective of intelligent transportation systems. Load, especially overload, has a destructive effect on roads and bridges, and monitoring it can increase the life of road surface and its structure. For low-end hardware with lack of CPU power and no GPU support, this paper presents a rapid method to detect whether heavy vehicles have loads or not;then it proposes a fast method for classifying load types to distinguish soil and construction waste from other miscellaneous loads for heavy weight vehicles. This paper applies a method for classifying cargo types using image processing and texture image classification. This method extracts features for statistical analysis of texture images based on gray-level co-occurrence matrices and local binary patterns. The classification is carried out by support vector machine, k-nearest neighborhood, K-mean, artificial neural networks and random forest classifiers. A large number of positive and negative patterns have been used to train these classifiers. We compare the performance of proposed extracted features and classifiers. The simulation results demonstrate that soil and construction waste can be identified from other miscellaneous loads effective in real-time implementation.

关键词： Intelligent transportation system Texture classification Machine learning Overload detection Real-time image processing

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Light-induced negative differential resistance and neural oscillations in neuromorphic photonic semiconductor micropillar sensory neurons

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SCIENTIFIC REPORTS 2025年第1期15卷 1-13页

作者： Jacob, Bejoys Silva, Juan Figueiredo, Jose M. L. Nieder, Jana B. Romeira, Bruno INL Int Iberian Nanotechnol Lab Ave Mestre Jose Veiga S-N 4715 P-4715330 Braga Portugal Univ Lisbon Lab Instrumentacao & Fis Expt Particulas LIP Dept Fis Fac Ciencias P-1749016 Lisbon Portugal

Neuromorphic systems, inspired by nature, are sought to efficiently process analogue inputs in real and complex environments. This could lead to ultralow-power in-sensor intelligent edge computers. Here, we present an artificial sensory oscillator neuron consisting of a iii-V semiconductor micropillar quantum resonant tunnelling diode (RTD) with GaAs photosensitive absorption layers. The oscillatory optical neuron encodes incoming analogue optical data into spatiotemporal oscillatory signals. We demonstrate that near-infrared light within a certain intensity range activates a region of negative differential resistance, and subsequently, large-amplitude voltage oscillations. As a result, optic analogue information is encoded into electrical oscillations resulting in amplification of sensory light inputs. Under pulse-modulated light, excitation and inhibition of burst firing patterns can be controlled within a single oscillatory neuron, simulating neural activity in networks in the form of breather-type oscillatory phenomena. Such spatiotemporal oscillatory patterns (burst firing) form the basis for the combined sensing, pre-processing, and encoding abilities of the vision-nervous system found in biological organisms. This work paves the way for future artificial visual systems using iii-V semiconductor nano-optoelectronic circuits in applications for light-driven neurorobotics, bioinspired optoelectronics, and in-sensor neuromorphic computing systems for real-time processing of sensory data.

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artificial Intelligence Based On-Board image Compression for the Φ-Sat-2 Mission

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 8063-8075页

作者： Guerrisi, Giorgia Del Frate, Fabio Schiavon, Giovanni Tor Vergata Univ Rome Dept Civil Engn & Comp Sci Engn I-00133 Rome Italy

The growing amount of data collected by Earth Observation (EO) satellites requires new processing procedures able to manage huge quantity of information. artificial intelligence (AI) and deep learning (DL) can provide advanced information also because of their ability to extract valuable information from complex data. Thanks to specific hardware platforms, these algorithms can be used also in space, opening the possibility for new procedures for intelligent data processing. The European Space Agency phi-Sat-2 mission was designed with the purpose of demonstrating the benefits of using AI in space by running AI-based applications on-board a CubeSat. We present here the convolutional autoencoder-based algorithm developed for on-board lossy image compression of the phi-Sat-2 mission and provide a first benchmark addressing a real space mission and a new image compression end-to-end architecture based on AI. image compression is a crucial application that allows to save transmission bandwidth and storage. In fact, images acquired by the sensor can be compressed on-board and sent to the ground where they are reconstructed. DL algorithms have already been successfully applied for image compression however performance degradation may occur in the context of a representative on-board environment. Therefore, besides analyzing the results for the local hardware environment, this article investigates the performance variation for the on-board setting. An additional piece of innovation is the introduction of an applicative metric for the evaluation of the compression to assess the applicability of the reconstructed images for other tasks. Such metric completes those more traditional based on the original-reconstructed image similarity.

关键词： artificial intelligence (AI) convolutional neural networks (CNNs) CubeSat image compression on-board processing

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Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 319-332页

作者： Xiaoqing Zhang Xiao Wu Zunjie Xiao Lingxi Hu Zhongxi Qiu Qingyang Sun Risa Higashita Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhenChina Tomey Corporation NagoyaJapan Guangdong Provincial Key Laboratory of Brain‐inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina Singapore Eye Research Institute SingaporeSingapore

Eye health has become a global health concern and attracted broad *** the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and ***,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model *** alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular *** MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer *** conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 *** results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and ***,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

关键词： artificial intelligence deep learning deep neural networks image analysis image classification medical applications medical image processing

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CT manifestations of gallbladder carcinoma based on neural network

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neural COMPUTING & applications 2023年第3期35卷 2039-2044页

作者： Chang, Yigang Wu, Qian Chi, Limin Huo, Huaying Shanxi Prov Peoples Hosp Taiyuan 030012 Shanxi Peoples R China Shanxi Tumor Hosp Taiyuan 030013 Shanxi Peoples R China Shanxi Med Univ Hosp 1 Taiyuan 030001 Shanxi Peoples R China

Gallbladder cancer is a relatively rare but highly malignant tumor. This study mainly explores the CT findings of gallbladder cancer based on neural networks. This study designed a gallbladder cancer LDCT image denoising network. Ability to process different doses of gallbladder cancer LDCT images with significant differences in noise and artifact distribution, this study designed the noise level estimation sub-network as a codec structure;the decoding part is used to generate the noise level of the gallbladder cancer LDCT image Artifact image. artificial neural network is a kind of artificial neural network that simulates the behavior characteristics of animal neural network and achieves the purpose of processing information by adjusting the interconnection between a large number of internal nodes. In order to meet the requirements of medical diagnosis for gallbladder cancer LDCT image quality, this study designed the backbone noise reduction network as a GAN framework that can be internally optimized. The discriminator network structure of this study is a multi-scale inception structure. As a sub-network of GAN, the discriminator network is used to distinguish true and false images and constrain the generator to make the generated images close to real images. In addition, it can be used as a noise evaluation sub-network to evaluate the noise gallbladder cancer LDCT. The treatment methods of gallbladder cancer include surgery, chemotherapy, radiation therapy, arterial interventional perfusion therapy, targeted therapy, etc. Surgery is currently the first choice for the treatment of gallbladder cancer, and the choice of surgery depends on the stage and growth site of gallbladder cancer. The image denoising network was used to evaluate the quality of the noise-reduced image. The average precision of GAN network for gallbladder cancer area is 91.0%, and the highest value is 95.2%. This study will provide a reliable reference value for the auxiliary diagnosis of gallbl

关键词： neural network Gallbladder cancer CT appearance LDCT image information GAN network

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Design of area-speed efficient Anurupyena Vedic multiplier for deep learning applications

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ANALOG INTEGRATED CIRCUITS AND SIGNAL processing 2024年第3期119卷 521-533页

作者： Kalaiselvi, C. M. Sabeenian, R. S. Sona Coll Technol Salem India Sona Coll Technol Dept ECE Salem India

Hardware such as multipliers and dividers is necessary for all electronic systems. This paper explores Vedic mathematics techniques for high-speed and low-area multiplication. In the study of multiplication algorithms, various bits-width ranges of the Anurupyena sutra are used. Parallelism is employed to address challenging problems in recent studies. Various designs have been developed for the Field Programmable Gate Array (FPGA) implementation employing Very Large-Scale integration (VLSI) design approaches and parallel computing technology. Signal processing, machine learning, and reconfigurable computing research should be closely monitored as artificial intelligence develops. Multipliers and adders are key components of deep learning algorithms. The multiplier is an energy-intensive component of signal processing in Arithmetic Logic Unit (ALU), Convolutional neural networks (CNN), and Deep neural networks (DNN). For the DNN, this method introduces the Booth multiplier blocks and the carry-save multiplier in the Anurupyena architecture. Traditional multiplication methods like the array multiplier, Wallace multiplier, and Booth multiplier are contrasted with the Vedic mathematics algorithms. On a specific hardware platform, Vedic algorithms perform faster, use less power, and take up less space. Implementations were carried out using Verilog HDL and Xilinx Vivado 2019.1 on Kintex-7. The area and propagation delay were reduced compared to other multiplier architectures.

关键词： Vedic mathematics algorithms Anurupyena sutra Multiplier optimization Priority encoder Bit Reduction

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Competitive dual-students using bi-level contrastive learning for semi-supervised medical image segmentation

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ENGINEERING applications OF artificial INTELLIGENCE 2025年 144卷

作者： Hu, Gang Zhao, Feng Houssein, Essam H. Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Minia Univ Fac Comp & Informat Al Minya Egypt

Semi-supervised image segmentation aims to train the neural network with a small number of labeled images and a large number of unlabeled images, which helps to alleviate the burden of having less manually labeled medical data. However, the Mean-Teacher (MT) model, a benchmark method for semi-supervised medical segmentation, leads to a performance bottleneck as its student model eventually converges to the teacher model. In addition, existing segmentation methods treat all pixels equally and underestimate the importance of indistinguishable and underrepresented pixels, failing to mine the potential information in these regions effectively. To address the above issues, this paper proposes a Competitive Dual-Student (CDS) incorporating bi-level contrastive learning. First, an additional competitive dual-student model is added to the MT model and promoting knowledge sharing and complementarity among networks. Competitive instruction by the teacher through feature information exchange and positive comparisons reduces the accumulation of biased knowledge in the model. It stimulates the potential for further optimization of the model as a whole. Furthermore, a bi-level contrastive learning is designed. The high-level contrastive learning encourages competitive dual students to learn high-quality features from each other by constructing reliability constraints. The low-level contrastive achieved deep mining and accurate processing of local edge features by introducing class prototypes of high-quality features for teacher networks. Finally, the comprehensive experimental results on left atrium, brain tumor segmentation 2019 and automated cardiac diagnosis challenge datasets indicate that the segmentation performance of the proposed CDS outperforms the state-of-the-art compared methods. Code is released at https://***/FengZhao2001/CDS.

关键词： neural network Semi-supervised image segmentation Competitive dual-student Bi-level contrastive learning Class prototypes

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Classification of Vernacular Landscape Elements and Design applications Based on Deep Learning Fully Convolutional neural networks

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ADVANCES IN CIVIL ENGINEERING 2025年第1期2025卷

作者： Li, Ya Zhang, Yun Zhejiang Ind Polytech Coll Sch Architectural Engn Shaoxing 312000 Zhejiang Peoples R China Yulin Normal Univ Fac Art & Design Yulin 537000 Guangxi Peoples R China

The integration of vernacular landscape elements is a crucial aspect of contemporary garden landscape planning and design, serving as a significant means of expressing traditional culture and evoking folk nostalgia. The external regional, ecological, and symbolic, as well as the internal cultural aspects of vernacular landscape elements, directly influence the overall appearance of residential planning. However, landscape image processing is still largely confined to the designer's professional cognition and experience. Traditional methods for classifying landscape elements primarily rely on manual processing. By integrating an advanced convolutional neural network (CNN) architecture, this study demonstrates its practical utility through rigorous experiments. This paper presents squeeze-and-excitation (SE)-DenseNet, a deep-learning model that uses SE blocks to classify vernacular landscape elements. It achieves 97.3% accuracy, outperforming traditional methods. This reduces manual work and helps integrate cultural elements into modern design.

关键词： artificial intelligence classification deep learning landscape images vernacular landscape

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Impact of the Preprocessing Steps in Deep Learning-Based image Classifications

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NATIONAL ACADEMY SCIENCE LETTERS-INDIA 2024年第6期47卷 645-647页

作者： Koresh, H. James Deva KPR Inst Engn & Technol Dept Elect & Commun Engn Coimbatore India

Deep learning softwares are designed using artificial neural networks for various applications by training and testing them with an appropriate dataset. The raw image samples available in the dataset may contain noisy and unclear information due to radiation, heat and poor lighting conditions. Therefore, the researchers are trying to filter and enhance such noisy images through preprocessing steps for providing a valid feature information to the neural network layers included in the deep learning software. However, there are certain claims that roam around the researchers such as an image may lose some useful information when it is not preprocessed with an appropriate filter or enhancement technique. Hence, the work reviews the efficacy of the methodologies that are designed with and without a preprocessing step. Also, the work summarizes the common reasons and statements highlighted by the researchers for using and avoiding the preprocessing steps on designing a deep learning approach. The study is conducted to provide a clarity toward the requirement and non-requirement of preprocessing step in a deep learning software.

关键词： image enhancement Filters Feature processing Classification Computation reduction

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GMDIC: a digital image correlation measurement method based on global matching for large deformation displacement fields

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND VISION 2024年第11期41卷 2263-2276页

作者： Wang, Linlin Shao, Jing Wang, ZhuJun Gao, Qian Wang, ChuanYun Yan, Zhuo Li, ZhongYi Zhang, Tong ShenYang Aerosp Univ Coll Artificial Intelligence Shenyang 110136 Liaoning Peoples R China ShenYang Aerosp Univ Sch Automat Shenyang 110136 Liaoning Peoples R China

The digital image correlation method is a non-contact optical measurement method, which has the advantages of full-field measurement, simple operation, and high measurement accuracy. The traditional DIG method can accurately measure displacement and strain fields, but there are still many limitations. (i) In the measurement of large displacement deformations, the calculation accuracy of the displacement field and strain field needs to be improved due to the unreasonable setting of parameters such as subset size and step size. (ii) It is difficult to avoid under-matching or over-matching when reconstructing smooth displacement or strain fields. (iii) When processing large-scale image data, the computational complexity will be very high, resulting in slow processing speeds. In recent years, deep-learning-based DIG has shown promising capabilities in addressing the aforementioned issues. We propose a new, to the best of our knowledge, DIG method based on deep learning, which is designed for measuring displacement fields of speckle images in complex large deformations. The network combines the multi-head attention Swin-Transformer and the high-efficient channel attention module EGA and adds positional information to the features to enhance feature representation capabilities. To train the model, we constructed a displacement field dataset that conformed to the real situation and contained various types of speckle images and complex deformations. The measurement results indicate that our model achieves consistent displacement prediction accuracy with traditional DIG methods in practical experiments. Moreover, our model outperforms traditional DIG methods in cases of large displacement scenarios. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Computational imaging Deep learning Imaging techniques Machine vision neural networks Speckle imaging

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