In this article, the capability of encoding information using a homologous series of monodisperse monomethoxypolyethylene glycols (mPEG), with a number of ethylene oxide units ranging from nEO = 5 to 8, and monodisperse linear aliphatic isocyanates c...
Image manipulation localization is one of the most challenging tasks because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned. Unlike many existing solutions, we employ a sema...
This paper analyzes the semantic rate–distortion problem motivated by task-oriented data compression with side information. The semantic information related to a task is not directly accessible to the encoder but implicitly impacts the observat...
Link prediction for temporal heterogeneous networks is an important task in the field of network science, and it has a wide range of real-world applications. Traditional link prediction methods are mainly based on static homogeneous networks, which d...
Machine learning techniques have revolutionized physical layer security (PLS) and provided opportunities for optimizing the performance and security of modern communication systems. In this study, we propose the first machine learning-assisted random...
Quantum image transmission over noisy communication channels remains a challenge due to the fragility of quantum states and their susceptibility to channel impairments. Existing quantum encoding schemes often exhibit limited noise resilience, while a...
Information embedding on actions is a new channel model in which a specific decoder is used to observe the actions taken by the encoder and retrieve part of the message intended for the receiver. We revisit this model and consider a different scenari...
We extend the problem of secure source coding by considering a remote source whose noisy measurements are correlated random variables used for secure source reconstruction. The main additions to the problem are as follows:
A coding ruler is a device that marks position information in the fordigital signals, and a code reader is a device that decodes the signals on the coding ruler and converts them into digital signals. The code reader and encoder ruler are key devices...
This study proposes the Huffman Tree and Binary Conversion (HTB) which is a preprocessing algorithm to transform the Huffman tree into binary representation before the encryption process. In fact, HTB can improve the structural readiness of plaintext...
Ensuring information integrity requires not only reducing decoding errors but also reducing the probability that corrupted data are accepted as valid. This research presents a hybrid integrity protection system that incorporates seeded universal hash...
Both the act of keeping information secret and the research on how to achieve it are included in the broad category of cryptography. When people refer to “information security,” they are referring to the study and use of methods that make...
The non-destructive study of soil micromorphology via computed tomography (CT) imaging has yielded significant insights into the three-dimensional configuration of soil pores. Precise pore analysis is contingent on the accurate transformation of CT i...
Plants exhibit complex internal dynamics in response to environmental conditions, yet whether these dynamics reflect structured affective regimes remains unclear. This study investigates whether internal plant signals encode information about affecti...
Multi-focus image fusion has become a very practical image processing task. It uses multiple images focused on various depth planes to create an all-in-focus image. Although extensive studies have been produced, the performance of existing methods is...
Neural networks are a state-of-the-art technology for fast and accurate holographic image reconstruction. However, at present, neural network-based reconstruction methods are predominantly applied to objects with simple, homogeneous spatial structure...
Sensor-integrated Quick Response (QR) codes are a promising route for combining product traceability with sensing capabilities. However, most reported platforms rely on binary read/no-read mechanisms in which sensor activation affects QR readability....
In this work, we establish a full single-letter characterization of the rate-distortion region of an instance of the Gray–Wyner model with side information at the decoders. Specifically, in this model, an encoder observes a pair of memoryless, arbitr...
Articulation modeling, feature extraction, and classification are the important components of pedestrian segmentation. Usually, these components are modeled independently from each other and then combined in a sequential way. However, this approach i...
Forward error correction (FEC) codes combined with high-order modulator formats, i.e., coded modulation (CM), are essential in optical communication networks to achieve highly efficient and reliable communication. The task of providing additional err...
Cross-modal retrieval aims to elucidate information fusion, imitate human learning, and advance the field. Although previous reviews have primarily focused on binary and real-value coding methods, there is a scarcity of techniques grounded in deep re...
Ransomware is one type of malware that involves restricting access to files by encrypting files stored on the victim’s system and demanding money in return for file recovery. Although various ransomware detection technologies have been introduc...
With the digitalization of mechatronic systems in the conditions of a shortage of available bandwidth of digital communication channels, the problem of ensuring the transfer of information between various components of the system can arise. This prob...
As an inevitable phenomenon in most optical remote-sensing images, the effect of shadows is prominent in urban scenes. Shadow detection is critical for exploiting shadows and recovering the distorted information. Unfortunately, in general, automatic...
Rapid post-disaster building damage assessment requires recognizing explicit structural failures and interpreting implicit situational cues in visually complex scenes. Whereas conventional automated methods are often confined to detecting explicit da...
Alzheimer’s disease (AD) is one of the most common diseases causing cognitive impairment in middle-aged and elderly people, and the high cost of the disease poses a challenge for health systems to cope with the expected increasing number of cas...
Reversible data hiding (RDH) in structured images remains challenging: most deep-learning methods target natural images, where distortions can be spread across the entire picture. When the carrier contains functionally critical regions—the find...
Building extraction is a binary classification task that separates the building area from the background in remote sensing images. The conditional random field (CRF) is directly modelled by the maximum posterior probability, which can make full use o...
In recent years, advances in deep learning have boosted the practical development, distribution and implementation of deep neural networks (DNNs). The concept of symmetry is often adopted in a deep neural network to construct an efficient network str...
Image segmentation enables accurate object identification, a key requirement in computer vision applications. In aquaculture, this technology is essential for monitoring and management of species such as Oncorhynchus mykiss. In the Peruvian highlands...
Binary change detection (BCD) using heterogeneous optical and SAR imagery faces challenges due to modality-specific noise and the lack of adaptive fusion strategies. Existing methods often fail to suppress SAR speckle noise and accurately localize fi...
High-resolution remote sensing imagery (HRRSI) presents significant challenges for building extraction tasks due to its complex terrain structures, multi-scale features, and rich spectral and geometric information. Traditional methods often face limi...
Firm-level financial statement data form multivariate annual time series with strong cross-variable dependencies and temporal dynamics, yet publicly available panels are often short and incomplete, limiting the generalization of predictive models. We...
Generative image steganography is a key technology for secure information transmission, but existing deep learning-based generative steganographic methods suffer from an extremely high bit error rate (BER) and degraded steganographic image quality in...
Accurate localization of colon polyps plays a crucial role in the early diagnosis of colon cancer. In this paper, we propose a novel network architecture, X-Net, which integrates the Pyramid Vision Transformer (PVT) and EfficientNet-B0. We selected E...
Due to the absence of communication and coordination with external spacecraft, non-cooperative spacecraft present challenges for the servicing spacecraft in acquiring information about their pose and location. The accurate segmentation of non-coopera...
Recently, deep learning methods have seen major advancements and are preferred for medical image analysis. Clinically, deep learning techniques for cancer image analysis are among the main applications for early diagnosis, detection, and treatment. C...
Heat convection is one of the main mechanisms of heat transfer, and it involves both heat conduction and heat transportation by fluid flow; as a result, it usually requires numerical simulation for solving heat convection problems. Although the deriv...
Breast cancer accounted for approximately 2.4 million new cases and 694,000 deaths worldwide in 2024. Deep learning approaches, particularly encoder–decoder architectures, have been investigated for mammographic mass segmentation. However, eval...