Convolutional neural networks (CNNs) have achieved tremendous success in solving complex classification problems. Motivated by this success, there have been proposed various compression methods for downsizing the CNNs to deploy them on resource-const...
In this paper, a robust fractal image compression method based on M-estimator is presented. The proposed method applies the M-estimator to the parameter estimation in the fractal encoding procedure using Huber and Tukey’s robust statistics. The...
Despite the advance in deep learning technology, assuring the robustness of deep neural networks (DNNs) is challenging and necessary in safety-critical environments, including automobiles, IoT devices in smart factories, and medical devices, to name...
Deep learning (DL) models have been deployed in various platforms, including resource-constrained environments such as edge computing, smartphones, and personal devices. Such deployment requires models to have smaller sizes and memory footprints. To...
This paper addresses the problem of improving the robustness of image steganographic methods under lossy compression while preserving high perceptual quality and low computational complexity. The paper proposes an adaptive code-controlled steganograp...
In modern implementations of Cloud Radio Access Network (C-RAN), the fronthaul transport network will often be packet-based and it will have a multi-hop architecture built with general-purpose switches using network function virtualization (NFV) and...
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Impulsive acoustic interference can distort covariance estimates and destabilize direction-of-arrival (DOA) estimation with multiple signal classification (MUSIC). This study proposes adaptive logarithmic compression MUSIC (ALC-MUSIC) for acoustic ar...
Modern radar signal processing techniques make strong use of compressed sensing, affine rank minimization, and robust principle component analysis. The corresponding reconstruction algorithms should fulfill the following desired properties: complex v...
The sparse representation error (SRE) exists when the images are represented sparsely. The SRE is particularly large in unmanned aerial vehicles (UAV) images due to the disturbance of the harsh environment or the instability of its flight, which will...
Digital image watermarking is a vital tool for copyright protection and content authentication. However, most existing methods perform well only under single noise types, while real-world applications often involve composite noises with multiple dist...
Machine learning systems, particularly in the domain of image recognition, are susceptible to adversarial perturbations applied to input data. These perturbations, while imperceptible to humans, have the capacity to easily deceive deep learning class...
Reliable prediction of the compressive strength of self-compacting concrete with recycled coarse aggregate (SCRCAC) from mixture composition supports more rational mix design and fewer experimental tests. Using the benchmark dataset of the reference...
Most of the existing research on video watermarking schemes focus on improving the robustness of watermarking. However, in application scenarios such as judicial forensics and telemedicine, the distortion caused by watermark embedding on the original...
A common technique for compressing a neural network is to compute the k-rank
In the field of image steganography research, more and more attention is paid to the importance of stego image robustness. In order to make steganography accessible from laboratory to practical applications, it becomes critical that the stego images...
This paper investigates the usefulness of the normalized compression distance (NCD) for image similarity detection. Instead of the direct NCD between images, the paper considers the correlation between NCD based feature vectors extracted for each ima...
The compressive strength of recycled concrete is acknowledged to be largely conditioned by the incorporation ratio of Recycled Concrete Aggregate (RCA), although that ratio needs to be carefully assessed to optimize the design of structural applicati...
Superhydrophobic surfaces have been extensively studied for their unique interfacial interaction between water and the surface, and they can be used for self-cleaning, drag reduction, anti-icing, and other applications. To make the superhydrophobic s...
Differential operators often admit multiple algebraically equivalent symbolic formulations, yet those formulations can differ in the organization of their internal structure prior to solution analysis. A reproducible symbolic framework is introduced...
Sustainability and energy efficiency of additive manufacturing (AM) is an up-to-date industrial request. Likewise, the claim for 3D-printed parts with capable mechanical strength remains robust, especially for polymers that are considered high-perfor...
Both energy efficiency and robustness are popular demands for 3D-printed components nowadays. These opposing factors require compromises. This study examines the effects of seven general control variables on the energy demands and the compressive res...
In the context of video background–foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background...
Low-temperature combustions (LTCs), such as homogeneous charge compression ignition (HCCI), could achieve high thermal efficiency and low engine emissions by combining the advantages of spark-ignited (SI) engines and compression-ignited (CI) engines....
In multi-gateway LoRaWAN deployments, a single uplink packet may be received through several sensor–gateway links, creating link-graph redundancy. We formulate compression as minimum-cardinality selection under aggregate and per-sensor observed...
The low-distortion processing of well-testing geological parameters is a key way to provide decision-making support for oil and gas field development. However, the classical processing methods face many problems, such as the stochastic nature of the...
Deploying medical visual question answering (VQA) systems over wireless networks introduces a fundamental challenge: channel-induced image degradation may corrupt the visual representations extracted by large vision-language models (VLMs), leading to...
TinyML models now run inference directly on microcontrollers with only kilobytes of memory, inside sensors, wearables, and industrial devices that often have no cloud link and no way to receive a patch once deployed. This makes them an easy target, s...
The rapid proliferation of the Internet of Things (IoT) has created an urgent need for on-device intelligence that balances high computational demands with stringent energy constraints. Existing edge inference frameworks struggle to deploy generative...
Dynamic facial expression recognition (DFER) has advanced rapidly, but leading systems use transformer backbones with more than 80 million parameters and assume fully decoded, high-quality video. Real deployments instead deliver small, heavily compre...
Power data terminals deployed in smart-grid environments generate massive amounts of operational data, yet the sensitive nature of these data and the existence of cross-region silos make centralized model training difficult in practice. Federated lea...
An adaptive beamformer is sensitive to model mismatch, especially when the desired signal exists in the training samples. Focusing on the problem, this paper proposed a novel adaptive beamformer based on the interference-plus-noise covariance (INC) m...
Data-driven approaches and calibration techniques for mathematical models, starting from observed data, are attracting more and more interest in the field of civil engineering. Among them, evolutionary polynomial regression (EPR) is an artificial int...
Recently, compressive sensing (CS) has attracted increasing attention in the areas of signal processing, computer vision and pattern recognition. In this paper, a new method based on the CS theory is presented for robust facial expression recognition...
This paper improves the existing literature on the shrinkage of high dimensional model and parameter spaces through Bayesian priors and Markov Chains algorithms. A hierarchical semiparametric Bayes approach is developed to overtake limits and misspec...
Classic video compression methods usually suffer from long encode time and requires large memories, making it hard to deploy on edge devices; thus, video compressive sensing, which requires less resources during encoding, is receiving more attention....
In recent years, the internet has developed rapidly. With the popularity of social media, uploading and backing up digital images has become the norm. A huge number of digital images are circulating on the internet daily, and issues related to inform...
This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI) techniques, namely Gaussian Process Regression (GPR) with five different kernels (Matern32, Matern52, Exponential, Squared Exponential, and Rational Quadrat...
The digital watermarking technique is a quite promising technique for both image copyright protection and secure transmission. However, many existing techniques are not as one might have expected for robustness and capacity simultaneously. In this pa...
Finite-bit channel-state-information (CSI) feedback couples the number of retained coefficients with their encoding precision. This study combines representation-specific payload accounting, an exact orthogonal principal component analysis (PCA) erro...
The compressive sensing (CS) framework offers a cost-effective alternative to dense alias-free sampling. Designing seismic layouts based on the CS technique imposes the use of specific sampling patterns in addition to the logistical and geophysical r...
Compressed sensing (CS) is a popular data compression theory for many computer vision tasks, but the high reconstruction complexity for images prevents it from being used in many real-world applications. Existing end-to-end learning methods achieved...
Integrated energy system (IES) is an important direction for the future development of the energy industry, and the stable operation of the IES can ensure heat and power supply. This study established an integrated system composed of an IES and advan...
Thin-walled carbon fiber reinforced plastic (CFRP) shells are increasingly used in aerospace industry. Such shells are prone to the loss of stability under compressive loads. Furthermore, the instability onset of monocoque shells exhibits a pronounce...
Recently, the H.265/HEVC video coding has been standardised by the ITU-T VCEG and the ISO/IEC MPEG. The improvements in H.265/HEVC video coding structure (CTU, motion compensation, inter- and intra-prediction, etc.) open up new possibilities to reali...
As an emerging and promising technique, device-free localization (DFL) estimates target positions by analyzing their shadowing effects. Most existing compressive sensing (CS)-based DFL methods use the changes of received signal strength (RSS) to appr...
Accurate prediction of concrete compressive strength remains a challenging problem due to the complex and nonlinear interactions among mixture components and curing conditions. While machine learning approaches have shown promising results, existing...
Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Trad...
Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs t...
This paper presents an underwater passive source localization method by forming an underdetermined linear inversion problem. The signal strength on a specified grid is evaluated using sparse reconstruction algorithms by exploiting the spatial sparsit...
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