This paper proposes a structure-constrained deep reconstruction framework for compressed sensing in shift-invariant spaces (SISs). The reconstruction is formulated as an inverse operator estimation problem derived from the matrix factorization H(ω)=W(ω)A and approximated using a hybrid CNN–Transformer architecture. Residual dilated convolutions capture localized signal structures, while the Transformer module models global frequency-domain dependencies. A variational inference-inspired regularization mechanism is incorporated to implicitly learn sparsity-aware priors. Experiments on both synthetic SIS signals and real-world ECG data demonstrate consistent improvements over classical optimization-based algorithms (ISTA, OMP) and a deep unfolding baseline (ISTA-Net+). At a 30% sampling rate, the proposed method achieves a PSNR of 35.46 dB. The feed-forward design eliminates iterative reconstruction, achieving a GPU inference time of 0.85 ms per signal.
Anomaly detection and multi-attack classification are major concerns for cyber defense. Several publicly available datasets have been used extensively for the evaluation of Intrusion Detection Systems (IDSs). However, most of the publicly available datasets may not contain attack…
Recent advances in the field of artificial intelligence (AI) have been pivotal in enhancing the effectiveness and efficiency of many systems and in all fields of knowledge, including medical diagnosis [...]
Despite the general improvement of living conditions and the ways of building buildings, the sense of security in or around them is often not satisfactory for their users, resulting in the search and implementation of increasingly effective protection measures. The insecurity tha…
Cyberbullying is a serious problem in online communication. It is important to find effective ways to detect cyberbullying content to make online environments safer. In this paper, we investigated the identification of cyberbullying contents from the Bangla and Chittagonian langu…
Fake news is one of the biggest challenging issues in today’s technological world and has a huge impact on the population’s decision-making and way of thinking. Disinformation can be classified as a subdivision of fake news, the main purpose of which is to manipulate and generate…
Software vulnerabilities pose significant risks to the security and reliability of modern systems, making automated vulnerability detection an essential research area. Traditional static and rule-based approaches are limited in scalability and adaptability, motivating the adoptio…