| ai.viXra.org > Digital Signal Processing |
|
 |
| ai.viXra.org > Digital Signal Processing |
|
|
Previous months:
2025 - 2508(2)
2026 - 2602(1) - 2603(2) - 2608(2)
Any replacements are listed farther down
[7] ai.viXra.org:2608.0064 [pdf] submitted on 2026-08-18 19:26:57
Authors: Dinuo Chen
Comments: 16 Pages.
Near-ultrasonic frequency bands provide an underutilized portion of the acoustic spectrum with relatively low perceptual salience[1, 2] while remaining potentially usable with conventional audio hardware. This paper investigates the feasibility of transmitting analog audio over near-ultrasonic frequency bands using consumer-grade loudspeakers, microphones, and sound cards. A singlesideband (SSB) modulation scheme is employed to shift baseband audio into the near-ultrasonicrange, where it is transmitted through an acoustic channel and subsequently demodulated at the receiver. The system is evaluated under various transmission conditions using commonly available audio equipment. Signal quality is assessed through spectral analysis and objective similarity metrics between the original and recovered audio signals. Experimental results demonstrate that consumer audio devices are capable of supporting near-ultrasonic analog audio transmission within the limits imposed by their frequency response. Although attenuation and distortion increase toward the upper end of the transmission band, the original audio can still be recovered with acceptable quality under suitable conditions. These findings suggest that near-ultrasonic acoustic channels couldprovide a low-cost communication medium for specialized applications without requiring dedicated ultrasonic transducers.
Category: Digital Signal Processing
[6] ai.viXra.org:2608.0029 [pdf] submitted on 2026-08-10 02:01:56
Authors: Lluis Eriksson
Comments: 18 pages; Exact-arithmetic, numerical, and Lean/mathlib reproducibility artifacts are linked in the paper.
Scalar Chebyshev filtering treats every vector in a block Krylov iterate with the same polynomial, while simultaneously diagonalizable matrix coefficients amount to independent scalar filters after a fixed channel rotation. We study the larger class of Hermitian matrix-polynomial filters under tangential pass constraints. Already at fixed channel dimension d=3, we construct rationally generated signatures with quantitative full-spark margin for which an irreducible symmetric filter has stopband leakage O(exp(-cN)). In contrast, every exactly calibrated symmetric coefficient family with any common nontrivial invariant channel subspace has leakage at least one; this comparator strictly contains the pairwise-commuting class and permits a fully noncommutative 2 by 2 block. A quantitative theorem covers approximately reducible filters by charging their sampled off-block coupling together with calibration error. The signature margin and the admissible combined error are both exp(-O(N)), not exp(-N^3). This exponential scale is unavoidable: for arbitrary pass nodes and signatures in the same separated bands, an explicit scalar binomial-tail polynomial has both pass error and stopband leakage at most exp(-2N/81). Thus constant-error separation is impossible, while the irreducible construction achieves the correct exponential scale class. An affine cosine substitution gives the same robust law for fixed-latency reciprocal linear-phase MIMO FIR filters. We also give an exact rational five-tap certificate with stopband norm at most 25/32 and prove that its advantage survives reducible calibration errors delta < 7/1920. A second exact-arithmetic certificate is calibrated from a public triaxial pump-vibration data set: it gives leakage below 0.96 versus one for every exactly calibrated reducible symmetric class, while its maximum directional error on six held-out records is 0.00385. In a frequency-domain-decomposition test on the frozen held-out cospectra, filtering rotates the leading modal direction by at most 0.222 degrees and changes its leading spectral ordinate by at most 2.4 × 10^-5 relatively. Numerical programs test the mechanism and expose an ordinary graph-denoising setting in which scalar Chebyshev filtering is instead preferable. Tangential matrix interpolation, MIMO filtering, scalar two-band approximation and convex FIR design are not claimed as new; the contribution is the fixed-order reducible/irreducible separation together with its two-sided calibration scale.
Category: Digital Signal Processing
[5] ai.viXra.org:2603.0051 [pdf] submitted on 2026-03-12 17:43:27
Authors: Md Mubdiul Hasan
Comments: 6 Pages.
Temperature control is a critical requirement inmany industrial heating applications where accurate sensing and reliable power regulation are necessary for system stability and safety. This paper presents the design and implementation of a microcontroller-based temperature monitoring and control system utilizing a thermocouple sensor and dual-heater regulation. The proposed system employs a thermocouple temperature sensor interfaced through a thermocouple-to-digital converter toobtain accurate temperature measurements. A microcontroller processes the temperature data and compares it with a user-defined setpoint to perform closed-loop temperature control.The system integrates a user interface consisting of key switches for parameter adjustment and a four-digit seven-segment display for real-time visualization of temperature values and system status. Heater control is achieved through optoisolated triac driver circuits that enable safe switching of ACpower to the heating elements. Additional peripheral modules including LED indicators, a buzzer alarm circuit, and a cooling fan control mechanism are incorporated to enhance operational feedback, safety, and thermal stability. The designed architecture ensures electrical isolation between the low-voltage control circuitry and high-voltage heater loads,improving system reliability and user safety. Experimental evaluation demonstrates that the system provides stable temperature regulation, responsive user interaction, and reliable heaterswitching performance. The proposed design offers a cost-effective and scalable solution suitable for industrial heating systems, laboratory equipment, and temperature-sensitive process control applications.
Category: Digital Signal Processing
[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
None