Abstract
In this letter, we introduce the computation-limited (comp-limited) signals, a communication capacity regime where the computational complexity of signal processing is the primary constraint for communication performance, overriding factors such as power or bandwidth. We present the Spectro-Computational (SC) analysis, a novel mathematical framework designed to enhance classic concepts of information theory -such as data rate, spectral efficiency, and capacity - to accommodate the computational complexity overhead of signal processing. We explore a specific Shannon regime where capacity is expected to increase indefinitely with channel resources. However, we identify conditions under which the time complexity overhead can cause capacity to decrease rather than increase, leading to the definition of the comp-limited signal regime. Furthermore, we provide examples of SC analysis and demonstrate that the Orthogonal Frequency Division Multiplexing (OFDM) waveform falls under the comp-limited regime unless the lower-bound computational complexity of the N-point Discrete Fourier Transform (DFT) problem verifies as ohm (N)$ , which remains an open challenge in the theory of computation.

Abstract
Surface plasmon resonance (SPR) conventionally occurs at the interface of a thin metallic film and an external dielectric medium in fiber optics through core-guided light. However, this work introduces theoretical and experimental evidence suggesting that the SPR in optical fibers can also be induced through light scattering from Au nanoparticles (NPs) on the thin metallic film, defined as nanoparticle-induced SPR (NPI-SPR). This method adheres to phase-matching conditions between SPR dispersion curves and the wave vectors of scattered light from Au NPs. Experimentally, these conditions are met on an etched optical fiber, enabling direct interaction between light and immobilized Au NPs. Compared to SPR, NPI-SPR exhibits stronger field intensity in the external region and wavelength tuning capabilities (750 to 1250 nm) by varying Au NP diameters (20 to 90 nm). NPI-SPR demonstrates refractive index sensitivities of 4000 to 4416 nm per refractive index unit, nearly double those of typical SPR using the same optical fiber configuration sans Au NPs. Additionally, NPI-SPR fiber configuration has demonstrated its applicability for developing biosensors, achieving a remarkable limit of detection of 0.004 nm for thrombin protein evaluation, a twenty-fold enhancement compared to typical SPR. These findings underscore the intrinsic advantages of NPI-SPR for sensing. Surface plasmon resonance (SPR) typically occurs at the interface of a thin metallic film and a dielectric medium in fiber optics. This work presents evidence of nanoparticle-induced SPR (NPI-SPR) in optical fibers through light scattering from Au nanoparticles on the thin metallic film. NPI-SPR offers stronger field intensity, wavelength tuning, and enhanced refractive index sensitivities, making it advantageous for biosensing applications. image

Abstract
Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 - 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches. Machine learning, particularly architectures integrating denoising autoencoders with convolutional neural networks, emerged as highly effective in enhancing image quality without compromising detail. The discussion highlights the success of these techniques in improving mammography images' visual quality. However, challenges such as high noise ratios, inconsistent evaluation metrics, and limited open-source datasets persist. Addressing these issues offers opportunities for future research to further advance mammography image enhancement methodologies.

Abstract
Chest radiography has been widely used for automatic analysis through deep learning (DL) techniques. However, in the manual analysis of these scans, comparison with images at previous time points is commonly done, in order to establish a longitudinal reference. The usage of longitudinal information in automatic analysis is not a common practice, but it might provide relevant information for desired output. In this work, the application of longitudinal information for the detection of cardiomegaly and change in pairs of CXR images was studied. Multiple experiments were performed, where the inclusion of longitudinal information was done at the features level and at the input level. The impact of the alignment of the image pairs (through a developed method) was also studied. The usage of aligned images was revealed to improve the final mcs for both the detection of pathology and change, in comparison to a standard multi-label classifier baseline. The model that uses concatenated image features outperformed the remaining, with an Area Under the Receiver Operating Characteristics Curve (AUC) of 0.858 for change detection, and presenting an AUC of 0.897 for the detection of pathology, showing that pathology features can be used to predict more efficiently the comparison between images. In order to further improve the developed methods, data augmentation techniques were studied. These proved that increasing the representation of minority classes leads to higher noise in the dataset. It also showed that neglecting the temporal order of the images can be an advantageous augmentation technique in longitudinal change studies.

Abstract
Multi -objective optimization (MOO) considers several objectives to find a feasible set of solutions. Selecting a solution from Pareto frontier (PF) solutions requires further effort. This work proposes a new classification procedure that fits into the analytic hierarchy Process (AHP) to pick the best solution. The method classifies PF solutions using pairwise comparison matrices for each objective. Sectorization is the problem of splitting a region into smaller sectors based on multiple objectives. The efficacy of the proposed method is tested in such problems using our instances and real data from a Portuguese delivery company. A non -dominated sorting genetic algorithm (NSGA-II) is used to obtain PF solutions based on three objectives. The proposed method rapidly selects an appropriate solution. The method was assessed by comparing it with a method based on a weighted composite single -objective function.

Abstract
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases privacy concerns, among others. This paper presents an overview of the 2(nd) edition of the Face Recognition Challenge in the Era of Synthetic Data (FRCSyn) organized at CVPR 2024. FRCSyn aims to investigate the use of synthetic data in face recognition to address current technological limitations, including data privacy concerns, demographic biases, generalization to novel scenarios, and performance constraints in challenging situations such as aging, pose variations, and occlusions. Unlike the 1(st) edition, in which synthetic data from DCFace and GANDiffFace methods was only allowed to train face recognition systems, in this 2(nd) edition we propose new subtasks that allow participants to explore novel face generative methods. The outcomes of the 2(nd) FRCSyn Challenge, along with the proposed experimental protocol and benchmarking contribute significantly to the application of synthetic data to face recognition.