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Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

Smart health surveillance technology has attracted wide attention between patients and professionals or specialists to provide early detection of critical abnormal situations without the need to be in direct contact with the patient. This paper presents a secure smart monitoring portable multivital signal system based on Internet-of-Things (IoT) technology. The implemented system is designed to measure the key health parameters: heart rate (HR), blood oxygen saturation (SpO2), and body temperature, simultaneously. The captured physiological signals are processed and encrypted using the Advanced Encryption Standard (AES) algorithm before sending them to the cloud. An ESP8266 integrated unit is used for processing, encryption, and providing connectivity to the cloud over Wi-Fi. On the other side, trusted medical organization servers receive and decrypt the measurements and display the values on the monitoring dashboard for the authorized specialists. The proposed system measurements are compared with a number of commercial medical devices. Results demonstrate that the measurements of the proposed system are within the 95% confidence interval. Moreover, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Error (MRE) for the proposed system are calculated as 1.44, 1.12, and 0.012, respectively, for HR, 1.13, 0.92, and 0.009, respectively, for SpO2, and 0.13, 0.11, and 0.003, respectively, for body temperature. These results demonstrate the high accuracy and reliability of the proposed system.

Post-Translational Modifications (PTMs) are covalent chemical alterations that occur after protein synthesis, critically regulating protein function, localization, and interactions. β-hydroxybutyrylation (Kbhb), a metabolically derived histone modification discovered in 2016, influences gene activation and cellular metabolism. While accurate PTM site identification is essential for understanding protein regulation and disease mechanisms, experimental approaches face significant limitations, including low modification abundance, high cost, and limited proteome coverage. Kbhb remains computationally underexplored, with only three existing prediction tools exhibiting modest accuracy and limited cross-species applicability. To address this gap, we developed BiGKbhb, a deep learning framework that depends on Bidirectional Gated Recurrent Units (BiGRU). With BiGKbhb, we systematically evaluate seven protein sequence encoding strategies, and compare six deep learning architectures using datasets from human, mouse, and fungal species. Results demonstrated that BLOSUM62 evolutionary encoding combined with BiGRU architecture achieves optimal performance, with BiGKbhb consistently achieving higher accuracy than those of existing methods with test set accuracies of 0.824, 0.832, and 0.871 for human, mouse, and fungal balanced datasets, respectively, with corresponding Area Under Curve (AUC) values of 0.920, 0.902, and 0.945, while additional evaluation on imbalanced datasets confirmed model robustness under realistic conditions. Cross-species analysis revealed enhanced transferability of the general multi-species model, and statistical validation confirmed significant improvements over existing predictors ( p  < 0.05). These findings contribute a robust computational tool for Kbhb prediction and provide insights into sequence determinants of this important modification across evolutionarily diverse species.

Post-Translational Modifications (PTMs), particularly lysine 2-hydroxyisobutyrylation (Khib), represent critical regulatory mechanisms governing protein structure and function, with mounting evidence underscoring their important implications in cellular metabolism, transcriptional regulation, and pathological processes. Despite this significance, the experimental identification of Khib sites remains constrained by resource-intensive methodologies and the transient nature of these modifications. To overcome these limitations, we introduce HyLightKhib, a computational framework that leverages Light Gradient Boosting Machine architecture for accurate Khib site prediction. Our approach depends on a hybrid feature extraction strategy, integrating Evolutionary Scale Modeling (ESM-2) embeddings with comprehensive Composition, Transition, and Distribution (CTD) descriptors as well as curated amino acid physicochemical properties for fixed-length peptides of 43 amino acids. The proposed classifier demonstrated considerable performance over contemporary algorithms, including XGBoost and CatBoostimplementations through mutual information-based feature selection optimization. Cross-species validation on diverse organisms including, human, parasite , and rice achieved improved Area Under the Receiver Operating Characteristic Curve (AUC-ROC) scores of 0.893, 0.876, and 0.847, respectively, outperforming existing predictors, such as DeepKhib, and ResNetKhib. HyLightKhib represents an advancement in computational PTM prediction, providing enhanced predictive performance and valuable biological insights with direct implications for functional proteomics and PTM-targeted therapies.

A low-power airborne synthetic aperture radar (SAR) transceiver is presented for high-resolution detection of shallow buried structures, particularly underground tunnels. The system operates in the VHF band to exploit its strong ground-penetration capability, where the limited available bandwidth necessitates advanced waveform shaping to achieve sufficient imaging resolution. To address this challenge, an optimized piecewise-linear nonlinear frequency modulation (PWL-NLFM) chirp is designed using particle swarm optimization (PSO), jointly minimizing sidelobe levels while preserving the required pulse-compression ratio. The tunable parameter controls the number of PWL segments, enabling a flexible trade-off between sidelobe suppression and pulse compression ratio according to mission requirements. Quantitative evaluation demonstrates that the proposed waveform significantly outperforms standard LFM and quadratic NLFM pulses, reducing the peak sidelobe level ratio (PSLR) to -33.0 dB and improving the integrated sidelobe ratio (ISLR) to -21.8 dB. A full two-dimensional (range–azimuth) point-target simulation further confirms that these improvements translate into superior SAR focusing, producing a cleaner and more isolated mainlobe with only slight broadening. This enhanced 2-D response increases the contrast between weak subsurface targets and background clutter, directly improving the detectability of tunnel features. The optimized PWL-NLFM waveform is integrated into a low-power SDR-based SAR transceiver, demonstrating its suitability for long-duration airborne sensing missions requiring deep penetration and high-contrast imaging.

As the demand for high-bandwidth Internet connections continues to surge, industries are exploring innovative ways to harness this connectivity, and smart agriculture stands at the forefront of this evolution. In this paper, we delve into the challenges faced by Internet Service Providers (ISPs) in efficiently managing bandwidth and traffic within their networks. We propose a synergy between two pivotal technologies, Multi-Protocol Label Switching—Traffic Engineering (MPLS-TE) and Diffserv Quality of Service (Diffserv-QoS), which have implications beyond traditional networks and resonate strongly with the realm of smart agriculture. The increasing adoption of technology in agriculture relies heavily on real-time data, remote monitoring, and automated processes. This dynamic nature requires robust and reliable high-bandwidth connections to facilitate data flow between sensors, devices, and central management systems. By optimizing bandwidth utilization through MPLS-TE and implementing traffic control mechanisms with Diffserv-QoS, ISPs can create a resilient network foundation for smart agriculture applications. The integration of MPLS-TE and Diffserv-QoS has resulted in significant enhancements in throughput and a considerable reduction in Jitter. Employment of the IPv4 header has demonstrated impressive outcomes, achieving a throughput of 5.83 Mbps and reducing Jitter to 3 msec.

Lysine 2-hydroxyisobutyrylation (Khib) has emerged as a crucial Post-Translational Modification (PTM) with significant roles in diverse biological processes ranging from gene expression to metabolic regulation. Despite its importance, computational approaches for accurately predicting Khib sites remain limited. This study introduces BLOS-Khib, a deep-learning framework that utilizes evolutionary information encoded in the BLOSUM62 matrix within a Convolutional Neural Network (CNN) architecture for cross-species Khib site prediction. Through systematic optimization, we found that a 43-amino acid peptide length captures the optimal sequence context for prediction across six taxonomically diverse organisms. Comprehensive comparative analyses demonstrated BLOS-Khib competitive performance compared to existing methods, achieving notable Area Under the ROC Curve (AUC) values on independent test sets: human (0.913), wheat (0.892), T. gondii (0.893), rice (0.887), Candida albicans (0.885), and Botrytis cinerea (0.903). Our framework showed improved performance compared to state-of-the-art approaches, including traditional machine learning classifiers and alternative deep learning architectures. Sequence signature analysis revealed both conserved lysine-rich regions preceding modification sites and species-specific amino acid preferences at positions immediately flanking the target residue. Notably, our cross-species applicability experiments identified high transferability between evolutionarily distant organisms, ensuring the potential convergent evolution of Khib determinants. BLOS-Khib demonstrates competitive performance for PTM prediction, while providing evolutionary insights into the sequence determinants governing this emerging regulatory mechanism across diverse species.

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support.

This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice.