Multitasking is an essential aspect of daily life; however, it significantly increases mental workload (MWL), which can affect cognitive performance, decision making, and overall effectiveness. Thus, accurately assessing MWL is significant in various fields, including human-computer interaction, aviation, and healthcare, where cognitive overload can lead to unsuitable decisions. The brain computer interface (BCI) based on electroencephalography (EEG) presents a viable, non-invasive option for real-time monitoring of MWL, allowing an adaptive system to improve performance and user experience. However, because EEG patterns vary widely among individuals, it is still challenging to develop a generalized MWL prediction model. Therefore, Light Gradient Boosting Machine (LightGBM) with manually extracted features is proposed. Our analysis was based on the "STEW" dataset, which includes two task conditions: "No task" and a multitasking activity using the SIMKAP framework. The proposed model achieved an average accuracy of 84.0% (±14.4%) and an average F1-score of 83.1% (±18.2%), showcasing its strong predictive performance while maintaining computational efficiency compared to deep learning methods. These results highlight LightGBM’s potential as a fast, subject-independent MWL classification tool, therefore enabling the design of scalable and flexible cognitive monitoring systems for practical use.

Early identification of cognitive skill impairments is crucial for timely clinical intervention. This study utilizes EEG data to classify cognitive states into three categories: no impairment, mild impairment, and severe impairment. EEG signals from 88 participants were segmented into 10 -second windows and analyzed using a modified EEGNet deep learning architecture, achieving a classification accuracy of 89 %. In addition to classification, statistical analyzes including Welch's t test and Benjamini-Hochberg's FDR correction were used to identify electrodes significantly affected, particularly F3, F4, C3,$\text{C4}, \text{T3}, \text{T4}, \text{P3}, \text{P4}, \text{O1}, \text{O2}, \text{Fz}, \text{Cz}$and Pz, implanted in memory, attention, and executive functions. Topographic brain activation maps highlighted these regional abnormalities, while spectral analysis revealed altered frequency band distributions across impairment levels. Connectivity analysis also showed decreased functional integration between brain regions in mild and severe cases. Combining deep learning, statistical inference, and EEG-based network features presents a robust framework for diagnosing and interpreting cognitive impairments.

In this paper, we propose an interpretable electroencephalogram (EEG)-based solution for the diagnostics of major depressive disorder (MDD). The acquisition of EEG experimental data involved 32 MDD patients and 29 healthy controls. A feature matrix is constructed involving frequency decomposition of EEG data based on power spectrum density (PSD) using the Welch method. Those PSD features were selected, which were statistically significant. To improve interpretability, the best features are first selected from feature space via the non-dominated sorting genetic (NSGA-II) evolutionary algorithm. The best features are utilized for support vector machine (SVM), and k-nearest neighbors (k-NN) classifiers, and the results are then correlated with features to improve the interpretability. The results show that the features (gamma bands) extracted from the left temporal brain regions can distinguish MDD patients from control significantly. The proposed best solution by NSGA-II gives an average sensitivity of 93.3%, specificity of 93.4% and accuracy of 93.5%. The complete framework is published as open-source at https://github.com/ehw-fit/eeg-mdd.

Extracting the background from a video in the presence of various moving patterns is the focus of several background-initialization approaches. To model the scene background using rank-one matrices, this paper proposes a background-initialization technique that relies on the singular-value decomposition (SVD) of spatiotemporally extracted slices from the video tensor. The proposed method is referred to as spatiotemporal slice-based SVD (SS-SVD). To determine the SVD components that best model the background, a depth analysis of the computation of the left/right singular vectors and singular values is performed, and the relationship with tensor-tube fibers is determined. The analysis proves that a rank-1 matrix extracted from the first left and right singular vectors and singular value represents an efficient model of the scene background. The performance of the proposed SS-SVD method is evaluated using 93 complex video sequences of different challenges, and the method is compared with state-of-the-art tensor/matrix completion-based methods, statistical-based methods, search-based methods, and labeling-based methods. The results not only show better performance over most of the tested challenges, but also demonstrate the capability of the proposed technique to solve the background-initialization problem in a less computational time and with fewer frames.

Plastic surgery is considered as a challenging research issue in the field of face recognition. Nevertheless, it has yet to be studied from theoretical and experimental perspectives. In this study, the authors proposed a facial recognition system for recognising faces after plastic surgery, which fuses the scores of two feature‐based and texture‐based algorithms. The feature based algorithm is the image GIST global descriptor and the texture‐based algorithm is the local binary pattern (LBP) of silence points. First, the local texture descriptor LBP was applied over a set of key points (silence points) in the face image rather than applying it over the entire face area. This proposed feature set is based on the assumption that only those LBP patterns with certain meaning, such as an edge or corner, will be useful for recognising faces that have undergone plastic surgery. The second set of features was extracted using a global descriptor, which is the GIST descriptor, to obtain a basic and a subordinate level description of the perceptual dimension. The performance of the proposed system surpassed the performance of a number of state‐of‐the‐art face recognition after plastic surgery, with a maximum verification accuracy of more than 91%.

Multi-modality data acquisition is a topic of research that gained interest in the recent years. It provides the opportunity to gather detailed information for analysis. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging is one good example of it. The information we get after fusing data from EEG and fMRI have both high temporal and spatial resolution. On the other side, this EEG recording suffers from some additional artifacts due to the fMRI environment, in particular, the Ballistocardiogram artifact. In this article, a new method of removing Ballistocardiogram Artifact from evoked potential studies is proposed. The method does not require any reference signal or prior information. The results presented are using the data of three subjects (volunteers). The results show that the proposed method can efficiently reduce Ballistocardiogram artifact and has performed better compared to the conventional methods.

Whilst facial recognition systems are vulnerable to different acquisition conditions, most notably lighting effects and pose variations, their particular level of sensitivity to facial aging effects is yet to be researched. The face recognition vendor test (FRVT) 2012's annual statement estimated deterioration in the performance of face recognition systems due to facial aging. There was about 5% degradation in the accuracies of the face recognition systems for each single year age difference between a test image and a probe image. Consequently, developing an age-invariant platform continues to be a significant requirement for building an effective facial recognition system. The main objective of this work is to address the challenge of facial aging which affects the performance of facial recognition systems. Accordingly, this work presents a geometrical model that is based on extracting a number of triangular facial features. The proposed model comprises a total of six triangular areas connecting and surrounding the main facial features (i.e. eyes, nose and mouth). Furthermore, a set of thirty mathematical relationships are developed and used for building a feature vector for each sample image. The areas and perimeters of the extracted triangular areas are calculated and used as inputs for the developed mathematical relationships. The performance of the system is evaluated over the publicly available face and gesture recognition research network (FG-NET) face aging database. The performance of the system is compared with that of some of the state-of-the-art face recognition methods and state-of-the-art age-invariant face recognition systems. Our proposed system yielded a good performance in term of classification accuracy of more than 94%.

Image-subspace-based approach for speckle noise removal from synthetic aperture radar (SAR) images is proposed. The underlying principle is to apply homomorphic framework in order to convert multiplicative speckle noise into additive and then to decompose the vector space of the noisy image into signal and noise subspaces. Enhancement is performed by nulling the noise subspace and estimating the clean image from the remaining signal subspace. Linear estimator minimizing image distortion while maintaining the residual noise energy below some given threshold is used to estimate the clean image. Experiments are carried out using synthetically generated data set with controlled statistics and real SAR image of Selangor area in Malaysia. The performance of the proposed technique is compared with Lee and homomorphic wavelet in terms of noise variance reduction and preservation of radiometric edges. The results indicate moderate noise reduction by the proposed filter in comparison to Lee but with a significantly less blurry effect and a comparable performance in terms of noise reduction to wavelet but with less artifacts. The results also show better preservation of edges, texture, and point targets by the proposed filter than both Lee and wavelet and less required computational time.

Intravenous (IV) catheterization is the most important phase in medical practices of daily life. It is hard to localize veins in patients who have deep veins, minor age or dark skin; hence multiple attempts become indispensable for proper catheterization in such cases. Near Infrared (NIR) Imaging allow to visualize the veins underneath the skin of persons having non-visibility of veins problem. This paper reports the pre-selection of illuminants that ensure best veins/tissues contrast for patients having different skin tone. The sample subjects have been divided in four different classes based on the Luminance value of their skin tone in order to extract the best illuminant wavelengths range for each class. A multispectral approach has been used which provides the flexibility of wavelength range from visible to NIR (380 to 1040nm). The veins/tissue reflectance contrast obtained helps in determining the best wavelengths range where the contrast is maximum for each of the four classes. Using these results, we are planning to build a prototype system which can automatically select the illuminants based on different physiological characteristics of a subject.

In this paper, a novel Joint Watermarking and Encryption scheme is presented. The design of proposed joint watermarking and encryption scheme involves the development of the encryption and watermarking schemes such that both the techniques are compliant with each other. Based on statistical analysis of continuous-tone still images, media data, in DCT domain, is partitioned into two parts. The encryption scheme exploits the energy distribution of AC component and DC bitplanes, and is carried out by encrypting those AC coefficients and selected DC component's bitplanes. For the purpose of watermarking, a DC component based watermarking scheme that is presented in [1] is adopted. Additionally, as majority of the data is transmitted in compressed form thus the proposed scheme is compliant with image/video compression standards (i.e. JPEG, MPEG 2 and MPEG 4 / H.264). In last, the performance of the joint scheme is evaluated in terms of security analysis.

New polymers are frequently appearing in the literature with a wide range of applications, e.g., they are being used in applications like fuel cell membrane, fertilizers, coating elements, etc. One of the requirements of the researchers involved in polymer science is to quickly compare the samples being fabricated. One of the measures for comparison is surface roughness. However, the high cost involved in the estimation of surface roughness limits its extensive and exhaustive usage. In this paper, we propose to estimate the surface roughness using the image focus, i.e., shape from focus (SFF). We show that the results from SFF are encouraging for comparison of polymers using surface roughness.

In this paper we present a method for depth estimation using image focus for 3D shape recovery. The method is based on the regression model. First the sequence of images is processed using Laplacian operator and the first estimate for the maximum value is found. Two datasets are selected for each pixel based on that maximum value. For each of the two datasets, a straight line is approximated to fit each of the dataset. Least squares method is used to find the best fit. The intersection point of the two lines is computed and weights are used so that the final depth map can be obtained. Finally, averaging filter is used to remove noise. The proposed method is compared with earlier depth estimation algorithms using various test objects.

In the area of machine vision, shadows occur frequently in a wide variety of scenes. An inherent weakness of focus-based methods is that they require the imaged scene to have significant texture. In many real-world applications, surfaces can be smoothly shaded and lacking in detectable texture. In such cases, Shape-Form-Focus (SFF) generates inaccurate and sparse depth maps. For accurate calculation of depth map, we consider the affects of illumination. In SFF the images are taken with one light source and the shadows occur in each frame. Thus, the more bright regions of the images give more accurate depth map, whereas, the less bright regions give less accurate depth map. We have also shown the results and compared them with the previous results. From simulation results we see that the results improved with the proposed algorithm.

There are many factors affecting the depth estimation for 3D shape recovery using passive optical methods. In this paper, we consider the effects of noise, source illumination and texture reflectance for shape from focus technique. We present a focus measure which shows consistent performance for varying noise levels, source illumination levels and different texture reflectance. The focus measure is based on an optical transfer function implemented in the Fourier domain and its results are compared with four other focus measures. The additive Gaussian noise is considered for noise analysis. Three illumination levels are considered for source illumination and three different textures are studied for reflectance analysis.

Accurate estimation of depth map leads to precise three-dimensional shape recovery. In this paper, we present a new focus measure for calculation of depth map. This new focus measure is based on an optical transfer function implemented in the frequency domain and it has shown robustness in the presence of noise as compared to the earlier focus measures. The results of the proposed focus measure have shown considerable improvement in the presence of noise with respect to other focus measures.

We propose a new method for depth from defocus (DFD) using wavelet transform. Most of the existing DFD methods use inverse filtering to determine the measure of defocus. These methods suffer from inaccuracies in finding the frequency domain representation due to windowing and border effects. The proposed method uses wavelets that allow performing both the local analysis and windowing with variable-sized regions for images with varying textural properties. We show that normalized image ratio of wavelet power by Parseval's theorem is closely related to blur parameter and depth. Experimental results have been presented demonstrating that the proposed DFD method is faster and generates more accurate depth maps than the previous methods.