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Quad-motor drive (QMD) systems enable fully independent torque control at each wheel and are increasingly adopted in high-performance electric vehicles (EVs). However, compared with single-motor drives, current measurement errors (CMEs) in QMD architecture accumulate across multi-motor subsystems, leading to amplified torque and speed ripple as well as asymmetric current sharing.  This paper proposes a correlative correction strategy for QMDs based on a distributed calibration approach (DCA).

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This repository contains MATLAB code and simulation data implementing the Density-Driven Optimal Control (D2OC) framework for efficient and collaborative multiagent nonuniform coverage, as presented in IEEE TSMC-S, 2025, 10.1109/TSMC.2025.3622075
The code allows reproducing the simulation results from the paper, including multiagent trajectory generation and density matching.
Users can run the scripts to test D2OC in custom scenarios. Citation of the original paper is requested if this code is used in research.

This dataset presents a large-scale collection of VVC (H.266)–compressed multiview dynamic volumetric video content designed for objective and subjective quality assessment in immersive media applications. The dataset is derived from the 8i Voxelized Full Bodies v2 dataset and focuses exclusively on dynamic human performance sequences captured from four viewpoints, enabling realistic evaluation of motion, view dependency, and compression artefacts in volumetric video.

Computing high-rank elliptic curves, a fundamentally difficult problem, infiltrates many fields of mathematics and cryptography. One of the most notable examples is in Elliptic Curve Cryptography, a stronger counterpart compared to Rivest–Shamir–Adleman (RSA), which requires the in-depth study of the properties of these algebraic curves. As such, it is necessary to find an algorithm that can generate high-rank curves given a certain lower bound, avoiding the consequences of computing the rank by hand.

The data sheet comprises twenty-nine columns for items dedicated to seven different latent constructs in the proposed study model. The items are addressing issues related to construction supply chain management in the three Coastal Karnataka districts of India across various taluks. The region selected for the study is environmentally vulnerable to natural disasters, subject to rapid urbanization, and facing logistical and transportation challenges. 

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The third-generation, neurologically-inspired Spiking Neural Networks (SNNs) possess many attributes
which make them attractive to the often restrictive operating environment found within Unmanned Aerial
Vehicles (UAVs). These benefits include, but are not limited to, high power efficiency, low latency, data
sparsity, event-driven processing, synaptic plasticity and neuromorphic hardware compatibility. In this
survey of 34 studies spanning the period 2019-2025 which implemented spiking networks onboard a real or

Conventional hierarchical architectures for Unmanned Underwater Vehicles (UUVs) trajectory tracking separate motion control from thrust allocation, which leads to cumulative mapping errors, inefficient constraint handling, and suboptimal performance. Furthermore, centralized nonlinear optimization faces the curse of dimensionality and limited robustness in complex environments. To address these challenges, this paper proposes a Distributed Direct Thruster Speed-Optimized Tube Based Model Predictive Control (Tube-MPC) scheme for UUVs.

This study was designed to establish reliable methods for identifying Streptocaulon griffithii (SG) and distinguishing it from its common adulterant, Cryptolepis buchnaii (CB), with the ultimate goal of developing quality criteria for the standard decoction of SG. Fifteen SG samples and five CB samples, collected from diverse geographical origins, served as the research materials. Qualitative identification of SG was conducted using thin-layer chromatography (TLC), whereas differentiation between SG and CB was accomplished through a polymerase chain reaction (PCR) specificity test.