Traditional computed tomography (CT) reconstruction methods, such as Filtered Backprojection Techniques (FBPT), haveM long been valued for their speed. However, advancements in modern imaging present new challenges: achieving high-quality reconstructions with limited data, managing noise, and enhancing computational efficiency. For full projection sets, challenges include addressing projection data inconsistencies like noise. In sparse data scenarios, challenges shift to managing unobservable object patterns and mitigating the evolving estimation errors caused by uncontrolled prediction inaccuracies. We present a novel MENT-based reconstruction approach that delivers exceptional speed and accuracy, addressing image quality, noise resilience, and sparse data processing challenges. Our method, the Extended High Efficiency Computed Tomography (eHECTOR), incorporates MENT within an Extended Kalman Filter-Type (eKFT) feedback loop, resolving lack of observability and effectively managing data inconsistencies. This approach enables faster, more precise reconstructions under sparse and noisy data conditions, marking a significant advancement over traditional methods and paving the way for next-generation diagnostic imaging.

We introduce Hermes, a general-purpose networking architecture built on an overlay of reconfigurable proxies. Hermes delegates networking responsibilities from applications and services to the overlay proxies. It employs a range of proxying and tunneling techniques, utilizes HTTP as its core component, and incorporates assisting components to facilitate service delivery, enhance communication, and improve end-users' experience. To substantiate these benefits, we prototyped Hermes and demonstrated its ability to efficiently address service and communication challenges. We showed that Hermes enables end-to-end solutions for compatibility with legacy applications and protocols and reliable delivery in highly disadvantaged networking conditions. Furthermore, Hermes demonstrated its ability to provide end-to-end, business-logic-driven handling of general IP traffic and to serve as a communication pipeline for Named Data Networking, facilitating the development and adoption of future networking architectures.

The growing sophistication of cyber threats has intensified the challenges faced by security systems, particularly in detecting malicious activities that exploit advanced encryption and behavioral evasion tactics. Dynamic Crypto-Behavioral Profiling (DCBP) offers a novel framework that combines realtime cryptographic monitoring with adaptive machine learning to address the limitations of traditional and static approaches. Through the continuous analysis of execution patterns, cryptographic operations, and network behaviors, the proposed system demonstrates exceptional accuracy in identifying both known and novel ransomware variants. The system's ability to adapt dynamically to evolving threats while maintaining high performance across diverse environments underscores its potential to redefine automated cybersecurity defenses. Comprehensive experimental evaluations illustrate its scalability, efficiency, and robustness, establishing a transformative direction in the field of malware detection and prevention.

The escalating frequency and sophistication of malicious encryption attacks have required the development of more effective detection mechanisms. The Dynamic Encryption Pattern Analysis (DEPA) framework introduces a novel approach that focuses on real-time monitoring and analysis of encryption behaviors, enabling the identification of ransomware activities with high accuracy and minimal false positives. Through a modular architecture, DEPA seamlessly integrates into existing cybersecurity infrastructures, offering scalability and adaptability across various organizational environments. Empirical evaluations demonstrate DEPA's efficacy in detecting a wide range of ransomware variants, including those employing advanced obfuscation techniques, while maintaining efficient resource utilization. The findings demonstrate the potential of behavior-based detection methodologies in enhancing defenses against evolving ransomware threats.

The mechanical high-performance resistance has attracted intensive scientific interest in Hardmasks (HMs) films applicable via the Bosch etching process for Silicon Micro-Channels (SiMCs) fabrication. This manuscript compares different film deposition methods and metal film behavior as HMs during the dry etching for the Bosch process in the Inductively Coupled Plasma and Reactive Ion Etching system. The HMs used were thermally evaporated Aluminum (Alev), Sputtering direct current DC diode Aluminum (Alspu), and bath chemical process Nickel-phosphorus (Ni-P). The 500nm thickness for Alspu and 1µm thickness for Alev and NiP films were deposited on the Si substrate (p-type (100) orientation). The Four Point Probe Resistivity Measurements and Atomic Force Microscopy AFM methods are used for resistivity measurements. In addition, the Al and NiP films were used to assess resistivity and grain size dimensions. For this work, the HM patterns consist of parallel metallic lines ranging from 175 to 220 µm in width with spacing between 230 and 750 µm. The pattern transfer technique was done via lithography and wet etching. The Bosch process was applied with time variations between 40sec to 60sec per cycle, using SF6+Ar and C4F8+Ar, to obtain the SiMCs, with anisotropic corrosion and depth values between 66 µm to 104 µm. The depth values were measured using Scanning Electron Microscopy and Scan Profile surface analyses in the Al and NiP resistivity and grain size related to the HM performance during the Bosch process.

For efficient operation of nanoscale optoelectronic components, realization of nanophotonic waveguides confining high mode powers is imperative. In most cases, this requirement is further compounded by the requirement of fabricating nanostructures supporting the fundamental mode only. We carried out an extensive numerical analysis based on rigorous full-vectorial finite difference method with perfectly matched layer boundaries to identify the single mode operation region of sub-micrometer silicon-on-insulator rib waveguides in terms of the waveguide geometric parameters. We particularly emphasized on achieving high mode confinements while designing a single mode waveguide and provided a way to engineer the confinement factor. Contrary to what has been established in the case of large cross-section rib waveguides, we demonstrated that determination of theoretical single mode conditions is possible for sub-micrometer waveguides with accuracy approaching 100%. To the best of our knowledge, this is the first such analysis which covers both, the deeply and the shallowly etched rib waveguides (0.1≤(slab height/rib height)≤0.8) and reports the single mode condition for entire sub-micrometer range (0.2µm≤rib height, rib width≤1.0µm) while adhering to design specific mode confinement requirements.

High-density surface EMG (HD-sEMG) is gaining attention because of its non-invasive nature and high spatial resolution. However, wearable HD-sEMG measurements with over 128 channels face challenges in system integration and reliable network-free inter-device synchronization. This article presents a wireless distributed wearable HD-sEMG acquisition system named Oct-HD, which supports up to eight acquisition modules (512 channels) with microsecond-level synchronization without requiring a network. The full-channel impedance detection ensures reliable electrode-skin contact and signal acquisition. The system also includes a self-locking base station that stores, charges, and configures the modules. Synchronization validation shows a longterm inter-module offline error within 2 ms (0.139 ppm) under vibrations and extreme temperatures, demonstrating reliability for network-free outdoor and open-space monitoring. Comparative experiments with a commercial system (Sessantaquattro) involving ten hand postures and 17 subjects were conducted, as hand gesture recognition is one of the most common applications in the field of electromyography. Results showed a significant performance improvement (p < 10 −5) of Oct-HD over the state-of-theart system in signal quality and anti-interference capacity. Gesture classification accuracy increased significantly for both the dynamic task (p = 0.008) and the maintenance task (p = 0.003). This highlights the effectiveness of Oct-HD in enhancing applications in prosthetic control and human-computer interaction. The Oct-HD system offers a notable advancement in wireless HD-sEMG acquisition, offering superior channel capacity, synchronization precision, signal quality, and anti-interference capacity compared to existing systems. The network-free microsecond-level synchronization and enhanced signal performance provide greater monitoring flexibility across various muscle groups, paving the way for broader applications in human-machine interaction.

Differentiating between a Relaxed State and a Physical Stress Stage is a useful technology for wearable devices. Currently, an accurate physical stress detection system utilizing the Electrodermal Activity Signal is not available. Using the Electrodermal Activity physiological signal, we develop a novel approach to this problem. Assessing physical changes with Electrodermal Activity/physiological signals is a field that has seen significant progress. The Electrodermal Activity signal tries to observe the changes in the electrical output of the eccrine sweat glands. These changes are performed by the sudomotor nerves, part of the nervous system, and by assessing the changes in these nerves (through Electrodermal Activity), it would be possible to develop a technology like that described above. We have used a dataset from a previous experiment, which has 5-minute stages, to provide the data for our experiment. The data for this experiment was taken with a wearable device. The data were pre-processed using a bandpass butterworth filter and a minmax normalizer. After this, feature extraction and selection were performed. For the model training, multiple models were trained for the stratified and non-stratified dataset. Multiple of them had excellent performance, but the Random Forest Classifier especially stood out for the non-stratified dataset, with a macroaverage cross-validated F-1 score of 0.80. After establishing that the best model was the Random Forest Classifier, Shapley values were used to understand and explain the feature importance of the model.

The wireless channel response contains rich multipath information, but accurately extracting this information remains challenging in complex indoor environments. In this paper, we propose a deep learning-based sparse recovery method for super-resolution multipath time delay estimation (TDE) and dictionary mismatch mitigation in sparse modeling. The proposed DenoisingCNN framework recovers the sparse delay spectrum from the channel response with highly overlapping multipath components (MPCs) with three specialized modules to addressing dictionary mismatch. Numerical experiments demonstrate that the proposed method outperforms current state-of-the-art approaches in the estimation of both the number of paths and the multipath time delays, offering robustness and strong generalization capabilities.

Silicon photonics is the most viable and cost-effective platform for next generation optical interconnects. One of the key optical devices in these interconnects is an optical modulator which, along with photodetectors, effectively defines performance of the whole optical link. However, due to its centrosymmetric structure, silicon does not offer linear electro-optic effects, leaving plasma dispersion effect as the only feasible modulation mechanism for high speed applications. The performance of plasma dispersion-based modulators is affected by several design variables, affecting various modulator figures of merit (FOMs) where performance trade-off exists. This necessitates design optimization, requiring reasonably accurate mathematical predictive models for all the FOMs. This paper presents a rigorous full 3D model for the junction capacitance of silicon-on-insulator interleaved junction optical phase modulators with submicron dimensions. Junction capacitance is instrumental in calculating power consumption per bit, modulation speed and efficiency of optical modulators. Solution of Drift-Diffusion and Poisson's equations were carried out on 3D finite-element-mesh and Maxwell's equations were solved using Finite-Difference-Frequency-Domain (FDFD) method on non-uniform rectangular grid to calculate confinement factor. Whole of the modeling process has been detailed and all the coefficients required in the model are presented. Model validation suggests < 10 % RMS error.

The increasing integration of renewable energy in smart-grids, usage of modern power-electronic devices having nonlinear behavior and environmental factors have raised power quality disturbance (PQDs) issues. PQDs result in malfunctioning, damaging or shutdown of sensitive equipment and disturb the smart grid networks' safe and economical operations. Different approaches have been used to detect and classify the PQDs along with Convolutional Neural Networks (CNNs). Recently a machine learning model, Vision Transformer (ViT) has gained much recognition for outshining the CNNs if trained on a sufficient amount of data. This work proposes a method of two-step, combining Smoothed-Pseudo Wigner-Ville Distribution with Vision Transformer (ViT-SPWVD). This is the firstever application of ViT-SPWVD for PQDs classification. Firstly, a set of synthetic PQDs have been generated through MATLAB model. Secondly, SPWVD technique is developed to convert 1-D signals into a 2-D images, then ViT model is developed for image classification. The features from image are automatically extracted with the help of self-attention mechanism. The proposed technique has proved its capability as additional valuable tool in the field of PQDs detection and classification. At culmination, the paper presents a comparison of ViT and CNN based approaches to PQD classification.

The ever increasing sophistication of ransomware attacks demands the development of advanced detection methodologies capable of identifying and mitigating such threats in real-time. The Adaptive Sequence Mapping Analysis (ASMA) framework introduces a novel approach that leverages sequential pattern recognition and entropy-based metrics to detect anomalous behaviors indicative of ransomware activity. Through comprehensive empirical evaluation, ASMA has demonstrated high detection accuracy across a diverse array of ransomware families, effectively distinguishing malicious actions from benign processes with minimal false positives. The framework's adaptability to zero-day attacks and scalability in high-throughput environments underline its potential for integration into existing cybersecurity infrastructures, providing a proactive defense mechanism against the evolving landscape of ransomware threats. The modular architecture of ASMA facilitates seamless integration with current security systems, enabling organizations to enhance their protective measures without necessitating extensive overhauls of existing infrastructures. The computational efficiency of the framework ensures real-time detection capabilities, maintaining system performance even under substantial network loads. These findings position ASMA as a valuable tool in the ongoing efforts to safeguard digital assets from malicious encryption-based attacks.

The rapid expansion of machine-generated text applications has highlighted critical limitations in adaptability and context awareness, particularly when models are exposed to diverse or unfamiliar scenarios. Contextual Hyper-Modulation introduces a groundbreaking framework that dynamically adjusts neural pathways to enhance contextual adaptability, offering significant advancements in accuracy, coherence, and real-time responsiveness. Through the integration of modulation layers that reconfigure internal representations, the framework addresses inherent deficiencies in static pre-trained architectures, achieving superior performance without extensive computational overhead. Empirical evaluations across a range of linguistic tasks demonstrate substantial improvements in context sensitivity and robustness, establishing the framework's efficacy in diverse applications. The findings reveal a transformative approach to augmenting language models with adaptive mechanisms, redefining the boundaries of contextual understanding and model scalability.

The evolution of cyber threats targeting sensitive data and systems has necessitated the development of increasingly advanced detection techniques to counteract their sophistication and adaptability. Dynamic Encrypted Payload Analysis introduces a groundbreaking methodology, addressing limitations in existing approaches through real-time monitoring and pattern recognition of malicious encryption behaviors. Through leveraging entropy analysis and machine learning models, the framework achieves superior accuracy and scalability while reducing false positives, making it highly effective against adaptive ransomware strategies. Comprehensive experimental evaluations have validated its performance across diverse environments, showcasing significant improvements in detection speed, resource efficiency, and adaptability to emerging threats. The findings demonstrate the transformative potential of the proposed framework, providing a resilient, scalable, and efficient solution for securing digital infrastructures against evolving ransomware attacks.

This study contributes to the ongoing discourse about the resilience of financial institutions by assessing whether actions related to capital policy in Europe contribute to systemic risk. Using a versatile hierarchical framework, I estimate systemic risk and evaluate the multi-level effects of capital policy measures across Europe and over time. This probabilistic approach yields a hierarchy of implications for systemic risk due to capital policy measures for each of the 21 European countries over 83 quarters. I specifically examine tightening, loosening, and unclear policy measures in the dataset. My findings uncover that the accumulation of tightening policy actions inadvertently leads to an increase in system risk by 1 to 10 quarterly percentage points at the bank level. When considering the intra-national probability of 'random effects,' institutions in Greece, Ireland, and the United Kingdom seem to contribute to heightened systemic risk. My results indicate that capital adequacy regulation could inadvertently heighten systemic risk. This unintentional correlation may arise from various smaller institutions collectively amplifying systemic risk dynamics by investing in similar asset classes to adhere to capital rules.

Contemporary academic research covers a wide range of topics, illustrating the complex and dynamic challenges and opportunities faced by societies worldwide. This paper examines the influence of literature in forming cultural identities and the significant effects of technology on conventional frameworks. Present academic works provide indepth perspectives on the integration of humanistic considerations and technological progress, highlighting their joint importance in addressing modern societal problems.

This paper presents a modified design for a Carry Save Multiplier (CSM) that aims to reduce power consumption compared to conventional approaches. The key modifications include replacing the conventional full adder (FA) with three different adder designs-Improved Full Adder (IFA), Hybrid-Pass logic with Static CMOS (HPSC), and Transmission Gate Adder (TGA)-and replacing the conventional vector-merging adder (CVMA) with a delay and energy-efficient Modified Square Root Carry Select Adder (MSCA) for the final addition step. The power consumption is reduced by approximately 84.66% when using the Transmission Gate Adder (TGA) instead of the Improved Full Adder (IFA), and by approximately 78.22% when using the Hybrid-Pass logic with Static CMOS (HPSC) adder instead of Improved Full Adder (IFA). The Transmission Gate Adder (TGA) when used in the multiplier circuit achieved the lowest power consumption with a reduction of approximately 72.04% compared to the IFA's power consumption. The Hybrid-Pass Logic with Static CMOS (HPSC) adder when used in the multiplier circuit has a reduction of 50.81% in comparison to the IFA. The modified CSM architecture, demonstrates significant power savings compared to traditional designs while maintaining multiplication performance, making it an attractive choice for high-speed, low-power digital systems.

The broad adaptation of cloud computing in healthcare has deeply aroused serious concerns about privacy and security, particularly in managing sensitive patient data. This paper proposes a state-ofthe-art architecture that integrates cloud infrastructure with edge computing for secure healthcare monitoring using Wireless Body Area Networks. The proposed architecture based on an edge node ensures the preservation of privacy through anonymization techniques, which is achieved through the integrated ARX toolkit with advanced techniques including l-diversity, t-closeness, and δ presence. In our framework, a matrix-based access control system was introduced to manage permissions precisely. It also employed the Attribute-Based Signcryption for secure data transmission. The system will process the vital medical parameters through specialized functions at the edge nodes. It adopts a computation approach that balances efficiency in processing with privacy requirements in a very effective way. It integrates fuzzy logic into the architecture for diagnosis in medicine while considering compliance to healthcare regulations. These include a multi-layered mechanism for the preservation of privacy, a lean edge-based processing system for the parameters of medicine, and an integrated access control strategy. This work has contributed to the field of secure healthcare systems by showing how edge computing can improve both privacy and efficiency in the processing of medical data while imposing strict security standards at the cloud.