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| ai.viXra.org > Digital Signal Processing |
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Previous months:
2025 - 2508(2)
2026 - 2602(1) - 2603(1)
Any replacements are listed farther down
[4] ai.viXra.org:2603.0001 [pdf] submitted on 2026-03-01 21:56:54
Authors: Md Mubdiul Hasan
Comments: 5 Pages.
Power quality issues and voltage fluctuations continue to pose significant challenges to the reliable operation of electrical equipment. This paper develops a microcontroller based programmable voltage monitoring and protection system using a Proteus virtual environment. The design in corporates a virtual Arduino controller, an AC source with adjustable load conditions, and an integrated signal-conditioning module to feature display and relay-based control components. A real-time graphical interface and an LCD module are used to continuously display input and regulated output voltages during operation. The system intelligently detects deviations beyond an acceptable voltage range and initiates protective shutdown to safeguard connected loads. The complete functionality is validated through comprehensive virtual prototyping, to demonstrate a practicaland cost-efficient approach for pre-hardware evaluation of programmable reference systems in voltage regulation and protection applications.
Category: Digital Signal Processing
[3] ai.viXra.org:2602.0037 [pdf] submitted on 2026-02-08 19:04:35
Authors: Tehzeeb Ali
Comments: 14 Pages.
Modern public key cryptosystems rely on two fundamental computational hardness assumptions: integer factorization (RSA) and the discrete logarithm problem (elliptic curve cryptography). These problems, formulated using modular arithmetic and algebraic geometry, have withstood four decades of cryptanalytic attacks. However, their inherent algebraic structures and periodicity properties make them vulnerable to quantum algorithms, particularly Shor’s algorithm (1994), which achieves polynomial-time complexity on quantum computers. This research presents an extensive mathematical comparison between classical cryptographic systems and quantum-resistant alternatives, with particular emphasis on lattice-based cryptography. We focus on the Learning With Errors (LWE) problem and its variants (Ring-LWE, Module-LWE), demonstrating through rigorous mathematical analysis why these lattice problems lack the periodicity that quantum algorithms exploit. We provide formal security reductions for LWE problems relative to worst-case lattice problems and present mathematical proofs of quantum resistance. For cryptocurrency systems, this analysis reveals critical vulnerabilities: current ECDSA algorithms used for transaction signing will become cryptographically insecure within 10-30 years, potentially compromising over $100 billion in digital assets. This work bridges mathematical foundations, security analysis, and practical implications for real-world systems, providing proof-based recommendations for the transition to post-quantum cryptographic standards in blockchain technologies.
Category: Digital Signal Processing
[2] ai.viXra.org:2508.0045 [pdf] submitted on 2025-08-14 19:32:10
Authors: Jaba Tkemaladze
Comments: 30 Pages.
This article presents an innovative predictive model of the world based on dynamic updating and adaptive filtering of predicates. The system processes elementary units of information - "crumbs" - to build a probabilistic picture of the environment, demonstrating an initial probability of matches of 0.5 and exponential decay to 0.00001 as the number of counters increases. Key mechanisms include: (1) updating significant patterns with PredictIncrement=2, (2) filtering rarely used predicates while maintaining plasticity balance (γ≥0.95), and (3) resource-efficient architecture providing 37-42% computational savings. Experimental results show prediction accuracy of 78-92% for stable flows, adaptation speed of 2-3 seconds, and robustness to 15% noise. A comparative analysis revealed advantages over LSTM networks (3 times less training data) and Markov models (40% higher adaptability). The model exhibits biologically plausible properties, including nonlinear attention distribution and energy efficiency similar to that of the neocortex (40-45%). Application prospects include IoT, cybersecurity and power system management, and further research is aimed at integrating the temporal model and hierarchical organization of patterns.
Category: Digital Signal Processing
[1] ai.viXra.org:2508.0008 [pdf] submitted on 2025-08-03 21:02:58
Authors: Rickesh Thandalai Natarajan, Surender Thandalai Natarajan
Comments: 7 Pages.
We present a novel algorithmic framework for detecting bias in automated lending systems using large-scale mortgage application data. Our approach employs stratified matching algorithms and statistical hypothesis testing to identify systematic discrimination patterns in financial decision-making systems. Applied to 947,927Home Mortgage Disclosure Act (HMDA) records from2007-2016, our framework detects significant algorithmic bias affecting minority applicants, with Black applicants experiencing 21.1 percentage point lower approval rates than equivalent White applicants. The system achieves96% statistical significance across income-loan amount strata, demonstrating the effectiveness of our bias detection methodology. Our contributions include: (1) a scalable bias detection algorithm for high-volume financial data, (2) robust statistical validation framework for discrimination detection, and (3) empirical evidence of systematic bias in real-world lending algorithms. The framework is generalizable to other algorithmic decision-making domains where fairness is critical.Keywords: Algorithmic bias, fairness in machinelearning, automated decision systems, bias detection, financial technology, mortgage lending.
Category: Digital Signal Processing
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