527 research outputs found
A survey on artificial intelligence-based acoustic source identification
The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions
Technologies of information transmission and processing
Π‘Π±ΠΎΡΠ½ΠΈΠΊ ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ ΡΡΠ°ΡΡΠΈ, ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠ° ΠΊΠΎΡΠΎΡΡΡ
ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° Π½Π°ΡΡΠ½ΠΎ-ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ°ΠΌ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΡΠ΅ΡΠ΅ΠΉ ΡΠ΅Π»Π΅ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΉ, ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π±Π΅Π·ΠΎΠΏΠ°ΡΠ½ΠΎΡΡΠΈ, ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ ΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. ΠΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½ Π΄Π»Ρ Π½Π°ΡΡΠ½ΡΡ
ΡΠΎΡΡΡΠ΄Π½ΠΈΠΊΠΎΠ² Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠΎΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΉ, ΠΏΡΠ΅ΠΏΠΎΠ΄Π°Π²Π°ΡΠ΅Π»Π΅ΠΉ, Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ², ΠΌΠ°Π³ΠΈΡΡΡΠ°Π½ΡΠΎΠ² ΠΈ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
Π²ΡΠ·ΠΎΠ²
Frequency-Aware Transformer for Learned Image Compression
Learned image compression (LIC) has gained traction as an effective solution
for image storage and transmission in recent years. However, existing LIC
methods are redundant in latent representation due to limitations in capturing
anisotropic frequency components and preserving directional details. To
overcome these challenges, we propose a novel frequency-aware transformer (FAT)
block that for the first time achieves multiscale directional ananlysis for
LIC. The FAT block comprises frequency-decomposition window attention (FDWA)
modules to capture multiscale and directional frequency components of natural
images. Additionally, we introduce frequency-modulation feed-forward network
(FMFFN) to adaptively modulate different frequency components, improving
rate-distortion performance. Furthermore, we present a transformer-based
channel-wise autoregressive (T-CA) model that effectively exploits channel
dependencies. Experiments show that our method achieves state-of-the-art
rate-distortion performance compared to existing LIC methods, and evidently
outperforms latest standardized codec VTM-12.1 by 14.5%, 15.1%, 13.0% in
BD-rate on the Kodak, Tecnick, and CLIC datasets
Enhancing sparse representation of color images by cross channel transformation
Transformations for enhancing sparsity in the approximation of color images by 2D atomic decomposition are discussed. The sparsity is firstly considered with respect to the most significant coefficients in the wavelet decomposition of the color image. The discrete cosine transform is singled out as an effective 3 point transformation for this purpose. The enhanced feature is further exploited by approximating the transformed arrays using an effective greedy strategy with a separable highly redundant dictionary. The relevance of the achieved sparsity is illustrated by a simple encoding procedure. On typical test images the compression at high quality recovery is shown to significantly improve upon JPEG and WebP formats
Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021
As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity
Advanced Techniques for Ground Penetrating Radar Imaging
Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPRβSAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives
Entropy in Image Analysis III
Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future
Skin texture features for face recognition
Face recognition has been deployed in a wide range of important applications including surveillance and forensic identification. However, it still seems to be a challenging problem as its performance severely degrades under illumination, pose and expression variations, as well as with occlusions, and aging. In this thesis, we have investigated the use of local facial skin data as a source of biometric information to improve human recognition. Skin texture features have been exploited in three major tasks, which include (i) improving the performance of conventional face recognition systems, (ii) building an adaptive skin-based face recognition system, and (iii) dealing with circumstances when a full view of the face may not be avai'lable. Additionally, a fully automated scheme is presented for localizing eyes and mouth and segmenting four facial regions: forehead, right cheek, left cheek and chin. These four regions are divided into nonoverlapping patches with equal size. A novel skin/non-skin classifier is proposed for detecting patches containing only skin texture and therefore detecting the pure-skin regions. Experiments using the XM2VTS database indicate that the forehead region has the most significant biometric information. The use of forehead texture features improves the rank-l identification of Eigenfaces system from 77.63% to 84.07%. The rank-l identification is equal 93.56% when this region is fused with Kernel Direct Discriminant Analysis algorithm
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