Electronic Concepts and Engineering (United States)

In cryptography and communication system applications, Linear Feedback Shift Registers (LFSRs) are commonly used for pseudorandom number generation due to their simplicity and capability of generating long periodic sequences. But the linear characteristics of basic LFSRs weaken their resistance to cryptanalytic attacks. This work presents a new interleaved LFSR system design that promotes randomness and security through output combination from various LFSRs, each governed by distinct primitive polynomials of equal degree, using a dynamic interleaving process. Control-based sequence determines interleaving order, substituting the fixed cyclic approach with a programmable strategy. Register Transfer Level (RTL) implementation is designed using Verilog and verified for correctness through Cadence tools, supplemented with static timing analysis conducted on the synthesized gate-level netlist. The results confirm functional correctness and demonstrate that the design meets setup and hold timing constraints, with a setup slack of 4.758 ns and a hold slack of 0.164 ns, thereby validating the effectiveness and reliability of the proposed interleaved LFSR approach.

Brucellosis is a widespread zoonotic disease and a major contributor to febrile illness, often underdiagnosed. This study investigated the seroprevalence of brucellosis and the associated exposure factors within South African farming and animal health communities. A cross-sectional survey was conducted across 40,000 km2 in the Free State and Northern Cape provinces from 2015 to 2016. Interviews and serum samples were collected from 847 volunteers on randomly selected farms and veterinary professionals listed in a regional register. Samples were tested using a commercial Brucella IgG ELISA. Risk factors were assessed using logistic regression, accounting for within-farm clustering. The seroprevalence was higher among veterinary professionals (11.6%; 16/138) than farm-based participants (7.0%; 50/711); p = 0.095. Multivariable analysis identified several exposure factors within the farm-based population: age over 40 years (aOR = 5.35; 95% CI: 1.68–17.02), White population group (aOR = 4.60; 95% CI: 1.64–12.91), contact with diseased animals (aOR = 2.01; 95% CI: 1.05–3.84), and working 4–8 h daily with ungulates (aOR = 2.90; 95% CI: 1.25–6.76). Among veterinary professionals, odds of exposure were higher with more than 5 years of work (OR = 1.82; 95% CI: 1.21–2.72) and extended ungulate contact (OR = 4.85; 95% CI: 1.27–18.52). Occupational exposure highlights the need for improved brucellosis prevention strategies.

The paper serves all the model components of social justice in the vision of air quality management of the American urban environment and racial injustice against the poor community. We then may resort to the facts regarding the environmental consideration, state regulation, and the census results and discuss to what extent the tradition of redlining and other similar practices will reach before making the people realize what exposure to air pollution may be like. The analysis to be performed consists of a spatial analysis of freeze frames of PM2.5 concentration in 50 large cities in the United States from 2010 to 2023, and comparing the pollutants to the socioeconomic factors. Analyzing the data, one can notice that communities of color are at risk of PM 2.5 28 times in comparison to cubic-white communities. The paper is proposing policies to address these historical injustices of the environment, such as equitable expansion of surveillance networks, participative style, community orientation, and revisionary decisions

We present a simple but effective training-free approach for text-driven image-to-image translation based on a pre-trained text-to-image diffusion model. Our goal is to gener-ate an image that aligns with the target task while preserving the structure and background of a source image. To this end, we derive the representation guidance with a combination of two objectives: maximizing the similarity to the target prompt based on the CLIP score and minimizing the structural distance to the source latent variable. This guidance improves the fidelity of the generated target image to the given target prompt while maintaining the structure integrity of the source image. To incorporate the representation guidance component, we optimize the target latent variable of diffusion model's reverse process with the guidance. Experimental results demonstrate that our method achieves outstanding image-to-image translation performance on various tasks when combined with the pretrained Stable Diffusion model.

We present an information-theoretic regularization technique for few-shot novel view synthesis based on neural im-plicit representation. The proposed approach minimizes potential reconstruction inconsistency that happens due to in-sufficient viewpoints by imposing the entropy constraint of the density in each ray. In addition, to alleviate the poten-tial degenerate issue when all training images are acquired from almost redundant viewpoints, we further incorporate the spatial smoothness constraint into the estimated images by restricting information gains from additional rays with slightly different viewpoints. The main idea of our algorithm is to make reconstructed scenes compact along indi-vidual rays and consistent across rays in the neighborhood. The proposed regularizers can be plugged into most of existing neural volume rendering techniques based on NeRF in a straightforward way. Despite its simplicity, we achieve con-sistently improved performance compared to existing neural view synthesis methods by large margins on multiple stan-dard benchmarks. Our codes and models are available in the project website <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> http://cvlab.snu.ac.kr/research/InfoNeRF.

This short paper is an extension of a family of embedding schemes called Natural Steganography, which embeds a message by mimicking heteroscedastic sensor noise in the JPEG domain. Under the assumption that the development from RAW uses linear de- mosaicking, we derive a closed-form for the covariance matrix of DCT coefficients from 3 × 3 JPEG blocks. This computation relies on a matrix formulation of all steps involved in the development pipeline, which includes demosaicking, conversion to luminance, DCT transform, and reordering. This matrix is then used for pseudo-embedding in the JPEG domain on four lattices of 8 × 8 DCT blocks. The results obtained with the computed covariance matrix are contrasted with the results previously obtained with the covariance matrix estimated using Monte Carlo sampling and scaling. The empirical security using DCTR features at JPEG quality 100 increased from PE = 14% using covariance estimation and scaling to PE = 43% using the newly derived analytic form.

The thixotropic behaviors of ferrofluid samples of different particle concentration were studied using different measurement methods, including the three interval thixotropic test and the hysteresis loop test. The experimental results demonstrated that ferrofluids exhibit significant thixotropic behaviors and the microstructural evolution in ferrofluids behind the macroscopic rheological mechanics is discussed. The influence of magnetic field strength, particle concentration and temperature on the thixotropy of ferrofluids was also analyzed. Microscopic ferrofluid theory was adopted to study the thixotropic behaviors of ferrofluids under different shearing conditions, indicating that different thixotropic behaviors of ferrofluids can be induced by the presence and evolution of different kinds of microstructures, such as linear chain-like and dense drop-like structures. Furthermore, a phenomenal thixotropic model was employed to analyze the experimental results, indicating that a more specific model for ferrofluids is needed. These findings contribute to a better understanding of the microscopic mechanism of the complex rheological behaviors of ferrofluids.

We address the problem of object tracking in an underwater acoustic sensor network in which distributed nodes measure the strength of field generated by moving objects, encode the measurements into digital data packets, and transmit the packets to a fusion center in a random access manner. We allow for imperfect communication links, where information packets may be lost due to noise and collisions. The packets that are received correctly are used to estimate the objects' trajectories by employing an extended Kalman Filter, where provisions are made to accommodate a randomly changing number of obseravtions in each iteration. An adaptive rate control scheme is additionally applied to instruct the sensor nodes on how to adjust their transmission rate so as to improve the location estimation accuracy and the energy efficiency of the system. By focusing explicitly on the objects' locations, rather than working with a pre-specified grid of potential locations, we resolve the spatial quantization issues associated with sparse identification methods. Finally, we extend the method to address the possibility of objects entering and departing the observation area, thus improving the scalability of the system and relaxing the requirement for accurate knowledge of the objects' initial locations. Performance is analyzed in terms of the mean-squared localization error and the trade-offs imposed by the limited communication bandwidth.