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CMCL: Cross-Modal Compressive Learning for Resource-Constrained Intelligent IoT Systems

ieee INTERNET OF THINGS JOURNAL 2024年第15期11卷 25534-25542页

作者： Chen, Bin Tang, Dong Huang, Yujun An, Baoyi Wang, Yaowei Wang, Xuan Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China Guangdong Prov Key Lab Novel Secur Intelligence Te Shenzhen Peoples R China Peng Cheng Lab Res Ctr Artificial Intelligence Shenzhen 518055 Guangdong Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Guangdong Peoples R China Huawei Technol Co Ltd Network Technol Lab Shenzhen 518055 Peoples R China

Compressive learning (CL) has proven to be highly successful in executing joint signal sampling and inference for intricate vision tasks through resource-limited Internet of Things (IoT) devices. Recent studies have turned their attention toward utilizing the deep neural networks (DNNs) methodology, also known as DeepCL, to enhance performance in unimodal vision tasks. This approach incorporates learnable compressed sensing in a comprehensive, end-to-end manner. Current DeepCL techniques typically employ initial signal reconstruction as the input for subsequent DNNs for inference. However, this practice presents potential risks, such as privacy breaches and reduced performance due to information processing inequality. To address these issues, this article introduces the first cross-modal CL (CMCL) approach that enables image captioning directly on compressed measurements. When compared to previous DeepCL strategies, the proposed CMCL offers significant improvements in computational efficiency and privacy protection. Extensive experiments demonstrate that CMCL performance is nearly on par with leading image captioning methods, showcasing a metric value that is merely 2.75% lower than the uncompressed method when the data is compressed eightfold.

关键词： Compressed sensing Task analysis Internet of Things image coding Transformers Servers Privacy Compressive learning (CL) image captioning Internet of Things (IoT) transformer

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Framework for Automatic signal Classification Techniques (FACT) for Software Defined Radios

Framework for Automatic Signal Classification Techniques (FA...

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ieee symposium on computational intelligence for Security and Defense Applications (CISDA)

作者： Jagannath, Jithin Saarinen, Hanne M. Drozd, Andrew L. ANDRO Computat Solut LLC ANDRO Adv Appl Technol Rome NY USA

ISBN: (纸本)9781467375573

The objective of this work is to design and implement a novel framework for automatic signal classification techniques (FACT) for software defined radios (SDR) capable of classifying multiple signals simultaneously. The focus of this work is to create a modular classification framework to facilitate the testing and implementation of new classification methods. The framework is divided into three parts: (i) Sensor resource manager (SRM), which performs the initial signal detection, preprocessing and the delegation of secondary receivers to the corresponding signals of interest (SOls);(ii) Modulation classifier block (MCB), which takes the received signal from SRM and performs the required modulation classification and (iv) Data library and statistical block contains all the templates required to perlin-m classification, thresholds fir signal detection and also known parameters of expected signals. To prove the feasibility of the framework, FACT is implemented and tested on a Universal Software Radio Peripheral (USRP) test bed using GNU radio signal processing toolkit. We evaluate the perlin-mance of signal detection based on the probability of detection (Pa) in varying signal-to-noise ratios (SNR). Additionally, the USRP based experiments demonstrate FACT operating as a single unit, preforming both blind detection and classification of multiple SOls using different classification methods.

关键词： Detectors Libraries Modulation Receivers signal detection signal to noise ratio

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FS-NET: FILTER SELECTION NETWORK FOR HYPERSPECTRAL RECONSTRUCTION

FS-NET: FILTER SELECTION NETWORK FOR HYPERSPECTRAL RECONSTRU...

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ieee International Conference on image processing (ICIP)

作者： Yang, Liutao Li, Zhongnian Pei, Zongxiang Zhang, Daoqiang Nanjing Univ Aeronaut & Astronaut MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China

ISBN: (纸本)9781665441155

Optimizing spectral filters for hyperspectral reconstruction has received increasing attentions recently. However, current filter selection methods suffer from extremely high computational complexity due to exhaustive optimization. In this paper, in order to reduce the computational complexity, we propose a novel Filter Selection Network (FS-Net) to select filters and learn the reconstruction network simultaneously. Specifically, we propose an end-to-end method to embed filter selection in FS-Net by setting spectral response functions as the input layer. Furthermore, we propose a non-negative L1 sparse regularization (NN-L1) to select optical filters automatically by sparsifying the input layer. Besides, we develop a two-stage training strategy for adjusting the number of selected filters. Experiments on public datasets show that our proposed method can considerably improve the reconstruction quality.

关键词： hyperspectral reconstruction filter selection deep learning sparse regularization

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IMPATIENT MRI: ILLINOIS MASSIVELY PARALLEL ACCELERATION TOOLKIT FOR image RECONSTRUCTION WITH ENHANCED THROUGHPUT IN MRI

IMPATIENT MRI: ILLINOIS MASSIVELY PARALLEL ACCELERATION TOOL...

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8th ieee International symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Wu, Xiao-Long Gai, Jiading Lam, Fan Fu, Maojing Haldar, Justin P. Zhuo, Yue Liang, Zhi-Pei Hwu, Wen-Mei Sutton, Bradley P. Univ Illinois Dept Elect & Comp Engn 1406 W Green St Urbana IL 61801 USA Univ Illinois Beckman Inst Urbana IL 61801 USA Univ Illinois Dept Bioengn Urbana IL 61801 USA

ISBN: (纸本)9781424441280

Much progress has been made in the design of efficient acquisition trajectories for high spatial and temporal resolution in magnetic resonance imaging (MRI). Additionally, significant developments in image reconstruction have enabled the reconstruction of reasonable images from massively undersampled or noisy data that is corrupted by a variety of physical effects, including magnetic field inhomogeneity. Translation of these techniques into clinical imaging has been impeded by the need for expertise and computational facilities to realize the potential of these methods. We present the Illinois Massively Parallel Acceleration Toolkit for image reconstruction with ENhanced Throughput in MRI (IMPATIENT MRI), a reconstruction utility that enables advanced techniques within clinically relevant computation times by using the computational power available in low-cost graphics processing cards.

关键词： magnetic resonance imaging graphics processing cards field inhomogeneity image regularization

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Unsupervised Hyperspectral image Classification: Spatial and Spectral Feature Fusion with Masked Autoencoders 20

Unsupervised Hyperspectral Image Classification: Spatial and...

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20th CSI International symposium on Artificial intelligence and signal processing, AISP 2024

作者： Mirsharji, Seyed Ali Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

In this study, we present an innovative unsupervised hyperspectral image classification method using a dual-branch architecture that merges spatial and spectral feature extraction. Our unique approach employs masked autoencoders, significantly outperforming traditional methods with an impressive overall accuracy of 97.1%. The paper details the model's performance evaluation, offers visual insights into its classification capabilities, and compares it with existing techniques, demonstrating its effectiveness and potential for advancing remote sensing applications. © 2024 ieee.

关键词： image classification

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Selection Method of Optimum Wavelet Bases for image Signature

Selection Method of Optimum Wavelet Bases for Image Signatur...

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ieee 10th International Conference on signal processing

作者： Wang, Xiaofeng Dou, Chunni Tian, Lihua Xian Univ Technol Sch Sci Xian 710048 Shaanxi Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

ISBN: (纸本)9781424458981

The properties of wavelet bases have greatly influence on algorithm performances when wavelet functions are used to construct image signature. Characteristics of image signature, such as robustness, tamper sensitivity, tamper localization, and compactness are analyzed in this work. Moreover, properties of Daubechies wavelet and its decomposition levels are analyzed. According to the results of our study, image signature generated by db1, db2, or db3 show stronger robustness and compactness yet lower tamper sensitivity and tamper localization capability with a higher wavelet decomposition level;and it exhibit higher computational complexity and lower compactness with a higher wavelet vanishing moment. Our results suggest that db2 wavelet is suitable for the construction of image signature, and also, its decomposition level has been recommended.

关键词： image signature image authentication Daubechies wavelet Wavelet decomposition level

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Detecting Pixel-Value Differencing Steganography Using Levenberg-Marquardt Neural Network

Detecting Pixel-Value Differencing Steganography Using Leven...

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ieee symposium on computational intelligence and Data Mining (CIDM)

作者： El-Alfy, El-Sayed M. King Fahd Univ Petr & Minerals Coll Comp Sci & Engn Dhahran 31261 Saudi Arabia

ISBN: (纸本)9781467358958

With the wide use of steganographic techniques, several security challenges emerge, e. g. criminals and network intruders can hide any information they want into legitimate multimedia data and exchange it over the Internet. This requires network designers and service providers to investigate new tools for detecting such misuse. In this paper, we explore a detection method based on neural network approach with Levenberg-Marquardt back propagation learning algorithm. This learning technique has been known to overcome the slow convergence of traditional back propagation and the instability problem of the steepest descent optimization method. We focus on digital images containing messages embedded by one of the recently proposed steganographic methods, known as pixel-value differencing. The idea is to analyze images before and after embedding to extract discriminating features and then build a neural network recognition model. The proposed approach is empirically evaluated and compared with four other machine-learning methods. The results show that more than 99% detection rate can be attained with very few false alarms.

关键词： Internet image processing learning (artificial intelligence) multimedia computing neural nets steganography

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A discriminative fusion framework for skin detection

A discriminative fusion framework for skin detection

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16th CSI International symposium on Artificial intelligence and signal processing, AISP 2012

作者： Ahmadi, Ehsan Garmsirian, Fahimeh Azimifar, Zohreh School of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (纸本)9781467314794

Skin detection is one of the preprocessing steps of machine vision applications. In this paper, a discriminative fusion framework is proposed for skin/non-skin classification of image pixels. The method utilizes conditional random fields (CRFs) to statistically combine the information of original raw image with the decisions made by a group of intermediate detectors to improve the accuracy and robustness of the detection task. The experimental result shows the success of the proposed fusion approach in comparison to the primary detectors. © 2012 ieee.

关键词： image enhancement

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Customized EfficientNet for Histopathology image Representation

Customized EfficientNet for Histopathology Image Representat...

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Mazaheri, Pooria Bidgoli, Azam Asilian Rahnamayan, Shahryar Dehkharghanian, Taher Tizhoosh, Hamid R.

ISBN: (纸本)9781665487689

In recent years, and amplified by the COVID-19 pandemic, the digitization of pathology has gained a considerable attention. Digital pathology provides crucial advantages compared to conventional light microscopy, including more efficient workflows, more accurate diagnosis and treatment planning, and easier collaboration. Despite promising progress, there are some critical challenges related to classifying images in digital pathology, such as huge input sizes (e.g., gigapixels) and expensive processing time. Most of the existing models for classification of histopathology images are very large and accordingly have many parameters to be learned/optimized. In addition, due to the large size of Whole Slide images (WSIs), e.g., 100,000 x 100,000 pixels, models require enormous computational times to achieve reliable results. In order to address these challenges, we propose a more compact network which is customized to classify cancer subtypes with lower computation time and memory complexity. This model is based on EfficientNet topology, but it is tailored for classifying histopathology images. The utilized model is evaluated over three tumor types brain, lung, and kidney from The Cancer Genome Atlas (TCGA) public repository. Since the pre-trained EfficientNet works properly with the specific size of images, an effective approach is proposed to adjust the size of input images. The proposed model can be trained with a much smaller training set for applications such as image search that require robust and compact representations. The results show that the proposed model, compared to state-of-the-art models, i.e., KimiaNet, can classify cancer subtypes more accurately and provides superior results. In addition, the proposed model achieves memory and computational efficiency in the training phase and is a more compact deep topology compared to KimiaNet.

关键词： EfficientNet Patch Extraction Histopathology images Classification image Search KimiaNet image Representation Deep Neural Network

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computational Imaging and Artificial intelligence: The Next Revolution of Mobile Vision

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PROCEEDINGS OF THE ieee 2023年第12期111卷 1607-1639页

作者： Suo, Jinli Zhang, Weihang Gong, Jin Yuan, Xin Brady, David J. Dai, Qionghai Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Inst Brain & Cognit Sci Beijing 100084 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 200232 Peoples R China Westlake Univ Res Ctr Ind Future Hangzhou 310030 Zhejiang Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Zhejiang Peoples R China Univ Arizona Wyant Coll Opt Sci Tucson AZ 85721 USA

signal capture is at the forefront of perceiving and understanding the environment;thus, imaging plays a pivotal role in mobile vision. Recent unprecedented progress in artificial intelligence (AI) has shown great potential in the development of advanced mobile platforms with new imaging devices. Traditional imaging systems based on the "capturing images first and processing afterward" mechanism cannot meet this explosive demand. On the other hand, computational imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real-life systems by integrating deep learning algorithms into the mobile vision platform to achieve a closed loop of intelligent acquisition, processing, and decision-making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advancement of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Although new-generation mobile platforms, represented by smart mobile phones, have deeply integrated CI and AI for better image acquisition and processing, most mobile vision platforms, such as self-driving cars and drones only loosely connect CI and AI, and are calling for a closer integration. Motivated by this fact, at the end of this work, we propose some potential technologies and disciplines that aid the deep integration of CI and AI and shed light on new directions in the future generation of mobile vision platforms.

关键词： Artificial intelligence (AI) autonomous driving brain science cameras cloud computing computational imag-ing (CI) deep learning edge cloud edge computing machine learning machine vision mobile vision neural networks optics self-driving

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