In this brief, a hybrid look-up table (LUT)-coordinate rotation digital computer (CORDIC) architecture for rectangular-to-polar conversion is proposed. The architecture performs domain folding and a small number of CORDIC iterations to transform the input vector into a reduced angular range, followed by a compact LUT that stores precomputed reference values used to derive the final magnitude and phase. Taylor series approximation and dynamic truncation are employed to minimize LUT storage while maintaining numerical precision. FPGA implementation on a Xilinx XCZU11EG device shows that the design can achieve the lowest latency among competing designs while maintaining competitive performance in power consumption and accuracy, demonstrating its efficiency and suitability for high-performance signal processing systems.

A key challenge in decentralized optimization is determining the optimal convergence rate and designing algorithms to achieve it. While this problem has been extensively addressed for doubly-stochastic and column-stochastic mixing matrices, the row-stochastic scenario remains unexplored. This paper bridges this gap by introducing effective metrics to capture the influence of row-stochastic mixing matrices and establishing the first convergence lower bound for decentralized learning over row-stochastic networks. However, existing algorithms fail to attain this lower bound due to two key issues: deviation in the descent direction caused by the adapted gradient tracking (GT) and instability introduced by the Pull-Diag protocol. To address descent deviation, we propose a novel analysis framework demonstrating that Pull-Diag-GT achieves linear speedup—the first such result for row-stochastic decentralized optimization. Moreover, by incorporating a multi-step gossip (MG) protocol, we resolve the instability issue and attain the lower bound, achieving near-optimal complexity for decentralized optimization over row-stochastic networks.

A ubiquitous phenomenon observed along the ventral stream of the primate hierarchical visual system is the suppression of neural responses to familiar stimuli at the population level. The observation of the suppression of the neural response in the early visual cortex (V1 and V2) to familiar stimuli that are multiple times larger in size than the receptive fields of individual neurons implicates the plausible development of recurrent circuits for encoding these global stimuli. In this work, we investigated the neural mechanisms of familiarity suppression and showed that a recurrent neural circuit based on Hebbian learning, consisting of neurons with small and local receptive fields, can develop to encode specific global familiar stimuli robustly as a result of familiarity training. We proposed that the learned recurrent circuit implements a manifold transform. The recurrent circuit compresses the dimensions of nuisance variations of a familiar image in the neural response manifold relative to the dimensions for discriminating different familiar stimuli, resulting in increased robustness of the global stimulus representation against noise and other irrelevant perturbations. We demonstrate that a recurrent circuit implements the manifold transform using a mixed strategy of locally linear and globally nonlinear computations, where the local linear computation selectively redistributes recurrent gain to enhance concept discrimination. These results provide testable predictions for neurophysiological experiments.

The most recent Thrifty Food Plan developed by the USDA in 2021 serves as the basis for the maximum Supplemental Nutrition Assistance Program benefit allotments. The underlying mathematical optimization model produces the recommended food plan for the lowest income population in the country that balances nutrition, cost, and minimal deviation from people’s current consumption patterns. To address food-induced carbon emissions, we add carbon emissions analysis to the optimization model for the Thrifty Food Plan 2021. Synthesizing food-induced carbon footprint data from DataFRIENDS, we use machine learning, specifically the DBSCAN algorithm, to detect footprint clusters in food categories, and select them for higher and lower carbon subdivisions. With carbon footprint subdivision of selected food categories, our model produces a weekly food plan for a family of four that reduces carbon footprint by 11.3% (from 106.2 kg CO2equivalents to 94.2 kg CO2equivalents) while meeting all the requirements on nutrition, budget, and resemblance to individuals’ observed diets. Our work shows promise in applying machine learning to the increasingly complex and interdisciplinary data in food and environmental sciences. Our model demonstrates a way to develop a sustainable, affordable, nutrition-sufficient, and culturally acceptable model for the country's lowest-income population. Given that the Supplemental Nutrition Assistance Program feeds one out of eight Americans, our work represents a meaningful contribution to help fight climate change and dieted-related chronic diseases.

We present a design of an inductively coupled radio frequency (ICRF) marker for magnetic resonance (MR)-based positional tracking, enabling the robust increase of tracking signal at all scanning orientations in quadrature-excited closed MR imaging (MRI). The marker employs three curved resonant circuits fully covering a cylindrical surface that encloses the signal source. Each resonant circuit is a planar spiral inductor with parallel plate capacitors fabricated monolithically on flexible printed circuit board (FPC) and bent to achieve the curved structure. Size of the constructed marker is Ø3-mm ×5 -mm with quality factor > 22, and its tracking performance was validated with 1.5 T MRI scanner. As result, the marker remains as a high positive contrast spot under 360° rotations in 3 axes. The marker can be accurately localized with a maximum error of 0.56 mm under a displacement of 56 mm from the isocenter, along with an inherent standard deviation of 0.1-mm. Accrediting to the high image contrast, the presented marker enables automatic and real-time tracking in 3D without dependency on its orientation with respect to the MRI scanner receive coil. In combination with its small form-factor, the presented marker would facilitate robust and wireless MR-based tracking for intervention and clinical diagnosis. This method targets applications that can involve rotational changes in all axes (X-Y-Z).

Cardiac electrophysiology is an effective treatment for atrial fibrillation, in which a long, steerable catheter is inserted into the heart chamber to conduct radio frequency ablation. Magnetic resonance imaging (MRI) can provide enhanced intraoperative monitoring of the ablation progress as well as the localization of catheter position. However, accurate and real-time tracking of the catheter shape and its efficient manipulation under MRI remains challenging. In this article, we designed a shape tracking system that integrates a multicore fiber Bragg grating (FBG) fiber and tracking coils with a standard cardiac catheter. Both the shape and positional tracking of the bendable section could be achieved. A learning-based modeling method is developed for cardiac catheters, which uses FBG-reconstructed three-dimensional curvatures for model initialization. The proposed modeling method was implemented on an MRI-guided robotic platform to achieve feedback control of a cardiac catheter. The shape tracking performance was experimentally verified, demonstrating 2.33° average error for each sensing segment and 1.53 mm positional accuracy at the catheter tip. The feedback control performance was tested by autonomous targeting and path following (average deviation of 0.62 mm) tasks. The overall performance of the integrated robotic system was validated by a pulmonary vein isolation simulator withex-vivotissue ablation, which employed a left atrial phantom with pulsatile liquid flow. Catheter tracking and feedback control tests were conducted in an MRI scanner, demonstrating the capability of the proposed system under MRI.