EEG Signal Analysis for Effective Classification of Brain States

EEG (Electroencephalogram) is a non-stationary signal that has been well established to be used for studying various states of the brain, in general, and several disorders, in particular. This work presents efficient signal processing and classification of the EEG signal. The digital filters used during decomposition of the input EEG signal have transfer functions which are simple and easily realizable on digital signal processors (DSP) and embedded systems. The features selected in this study; energy, entropy and variance; are among the most efficient and informative to analyze the EEG signal strength and distribution for detecting brain disorders such as seizure. Training and testing of the extracted features are performed using linear kernel (Support Vector Machine) SVM and thresholding in DSP algorithms and hardware, respectively. The experimental results for the digital signal processing algorithms show a high classification accuracy of 95% in the occurrence of seizure in epileptic patients. The techniques in this work are also under investigation for classifying other brain states/disorders such as sleep stages, sleep apnea and multiple sclerosis

Effect of oxidative stress on afferent nerve activity from small intestine and colon in young and aged mouse

This thesis addressed the sensory functions of the gastrointestinal (GI) tract with a focus on the effect of oxidative stress on afferent nerve activity from small intestine and colon in young and aged mouse. Oxidative stress appears to be involved in the pathogenesis of many gastrointestinal conditions, such as inflammatory bowel disease (IBD), colon cancer, and may contribute to the gut dysfunction in ageing. How diverse regions of the gut react to, and handle, elevated levels of ROS in young and aged is unclear. Here, I investigated the effect of oxidative stress on afferent nerve activity in young and aged mice, and if it contributes to age associated changes. The study used in vitro afferent nerve recordings from jejunum and colon of male mice and concluded that colonic sensory neurons are more sensitive to oxidative stress than jejunum sensory neurons. In the aged group, decreased afferent mechanosensitivity associated with a greater oxidative status was observed only in the aged colonic mucosa. Findings obtained by RNA microarray analysis suggested that the difference between the mouse jejunum and colon in ROS production genes and secondary antioxidant genes may have contributed to the colonic afferent being more sensitive to oxidative stress. In addition, upregulation of inflammatory related genes associated with long-life exposure to high endogenous ROS level are possible factors for colon being more inclined to develop diseases or decreased function as a result of normal ageing

Safe Credential-Based Trust Protocols: A Framework

Trust in semantic Web is established by either credentials or reputation. Credential-based trust protocols assume the possession of credentials and transfer them between parties in order to establish trust. Since credentials can be private data, the act of providing private credentials implies poor privacy management even if transferred through secure (encrypted) channels. Exchanging private credentials is a major risk in critical applications since it eases unauthorized usage of private data. This paper presents a framework for safe trust protocols that interactively proves the possession of credentials without the need of exchanging the

Safe Credential-Based Trust Protocols: A Framework

Trust in semantic Web is established by either credentials or reputation. Credential-based trust protocols assume the possession of credentials and transfer them between parties in order to establish trust. Since credentials can be private data, the act of providing private credentials implies poor privacy management even if transferred through secure (encrypted) channels. Exchanging private credentials is a major risk in critical applications since it eases unauthorized usage of private data. This paper presents a framework for safe trust protocols that interactively proves the possession of credentials without the need of exchanging the

Physiological Study of Effect of Smoking Cigarette and Hookah on Non Enzymatic Antioxidant Level in Blood Serum

Smoking cigarette and hookah are one of the common serious problem in the world , where it is especially popular among young people . Many of people think that hookah smoking to be less hazardous than cigarette smoking. We studied and comparative effect of smoking hookah and cigarette on levels of non enzymatic antioxidan

A novel transition protocol to post-quantum cryptocurrency blockchains

Blockchain-based public ledgers, known as cryptocurrencies, are used to build peer-to-peer digital payment systems. Cryptocurrency transactions are secured by digital signatures. However, today's public-key cryptography, which is the basis of digital signatures, is vulnerable to quantum attacks. Therefore, there is a significant risk to the 2.7 trillion dollar market capitalization of the cryptocurrency sector in the Quantum Era. In this paper, we review the current risk of quantum attacks on the blockchains of cryptocurrencies. We also discuss the migration of existing cryptocurrencies from classical to quantum-resistant blockchains and review some of the existing transition protocol algorithms. The main contribution of this work is to propose a new transition protocol algorithm that allows smooth and safe migration to post-quantum blockchains without delay. The proposed algorithm requires a soft fork of the original blockchain, which makes it more desirable than other hard-fork solutions. We also prove the soundness and completeness properties of the proposed algorithm and discuss its advantages compared to the existing ones. We conclude by highlighting our recommendations based on this study

Security and Privacy Using One-Round Zero-Knowledge Proofs

A zero-knowledge proof (ZKP) is an interactive proof that allows a prover to prove the knowledge of a secret to a verifier without revealing it. ZKPs are powerful tools to deal with critical applications in security e-commerce. Existing ZKPs are iterative in nature; their protocols require multiple communication rounds. The cost of iteration makes ZKPs unsuitable in practice. We propose a new protocol that meets all the requirements of ZKPs, yet runs in one round. The new approach substantially reduces computation and communications costs. It makes ZKPs more suitable for practical cryptographic systems for both govern-ment and commercial applications

Better Privacy and Security in E-Commerce: Using Elliptic Curve-Based Zero-Knowledge Proofs

We propose an approach using elliptic curve-based zero-knowledge proofs in e-commerce applications. We demonstrate that using elliptic curved-based zero-knowledge proofs provide privacy and more security than other existing techniques. The improvement of security is due to the complexity of solving the discrete logarithm problem over elliptic curves

Harnessing Extrinsic Dissipation to Enhance the Toughness of Composites and Composite Joints: A State-of-the-Art Review of Recent Advances

Interfaces play a critical role in modern structures, where integrating multiple materials and components is essential to achieve specific functions. Enhancing the mechanical performance of these interfaces, particularly their resistance to delamination, is essential to enable extremely lightweight designs and improve energy efficiency. Improving toughness (or increasing energy dissipation during delamination) has traditionally involved modifying materials to navigate the well-known strength-toughness trade-off. However, a more effective strategy involves promoting non-local or extrinsic energy dissipation. This approach encompasses complex degradation phenomena that extend beyond the crack tip, such as long-range bridging, crack fragmentation, and ligament formation. This work explores this innovative strategy within the arena of laminated structures, with a particular focus on fiber-reinforced polymers. This review highlights the substantial potential for improvement by presenting various strategies, from basic principles to proof-of-concept applications. This approach represents a significant design direction for integrating materials and structures, especially relevant in the emerging era of additive manufacturing. However, it also comes with new challenges in predictive modeling of such mechanisms at the structural scale, and here the latest development in this direction is highlighted. Through this perspective, greater durability and performance in advanced structural applications can be achieved

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.https://doi.org/10.3390/e1809027