Aviation fundamentally depends on well structured and rigorous record keeping frameworks. The demand for accurate data is continuously increasing for both operational and security processes, particularly in civil aviation. The principal challenge for civil aviation is protecting and sharing sensitive information, while guarding itself against the identity of illegitimate users. The emergence of blockchain and biometrics has given rise to a new direction of technological innovation, which has potential to solve the challenges fundamentally affecting the aviation sector. The proposed algorithm is designed to extract multimodal physiological biometrics, fusion with cryptographic hash to create 1024-bit (256 hex) hash in blockchain vector subspace. The primary challenge of this research is to incorporate biometrics attributes within blockchain hash function, making data available to all authorized aviation entities, and protecting sensitive PII Information against privacy, security, and unlinkability attacks. Blockchain and biometric systems provide security measures on their own. In conjunction with asymmetric encryption, the data becomes more secure when live human physiological attributes are required to unlock a secret key, as it will be incorporated with every transaction of civil aviation. The stochastic model will provide variabilities in the estimation of unique cipher key. Biometrics is the only part of the process of encrypting data which ensures unique digital identity with the presence of live users. On the other side, the time and difficulty of guessing a secret key along with global accessibility of universal data set in real time, are what makes the proposed model the most robust and cryptographically secure, integrated cybersecurity platform.
The main challenge of facial biometrics is its robustness and ability to adapt to changes in position orientation, facial expression, and illumination effects. This research addresses the predominant deficiencies in this regard and systematically investigates a facial authentication system in the Euclidean domain. In the proposed method, Euclidean geometry in 2D vector space is being constructed for features extraction and the authentication method. In particular, each assigned point of the candidates’ biometric features is considered to be a 2D geometrical coordinate in the Euclidean vector space. Algebraic shapes of the extracted candidate features are also computed and compared. The proposed authentication method is being tested on images from the public “Put Face Database”. The performance of the proposed method is evaluated based on Correct Recognition (CRR), False Acceptance (FAR), and False Rejection (FRR) rates. The theoretical foundation of the proposed method along with the experimental results are also presented in this paper. The experimental results demonstrate the effectiveness of the proposed method.
Space Time Adaptive Processing (STAP) is a multi-dimensional adaptive signal processing technique,
which processes the signal in spatial and Doppler domains for which a target detection hypothesis
is to be formed. It is a sample based technique and based on the assumption of adequate number
of Independent and Identically Distributed (i.i.d.) training data set in the surrounding environment.
The principal challenge of the radar processing lies when it violates these underlying assumptions due
to severe dynamic heterogeneous clutter (hot clutter) and jammer effects. This in turn degrades the
Signal to Interference-plus-Noise Ratio (SINR), hence signal detection performance. Classical Wiener
filtering theory is inadequate to deal with nonlinear and nonstationary interferences, however Wiener
filtering approach is optimal for stationary and linear systems. But, these challenges can be overcome
by Adaptive Sequential State Estimation (ASSE) filtering technique.
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