This paper designs a hardware-implementable joint denoising and demosaicing acceleration system. Firstly, a lightweight network architecture with multi-scale feature extraction based on partial convolution is proposed at the algorithm level. The part...
This paper proposes an efficient dynamic reconfigurable CNN accelerator (EDRCA) for FPGAs to tackle the issues of limited hardware resources and low energy efficiency in the deployment of convolutional neural networks on embedded edge computing devic...
Convolution is the most computationally intensive task of the Convolutional Neural Network (CNN). It requires a lot of memory storage and computational power. There are different approaches to compute the solution of convolution and reduce its comput...
This paper investigates the kinematics and dynamics of multi-rigid-body systems in screw form. The Newton–Euler dynamics equations are established in screw coordinates. All forces and torques of the multi-rigid-body system can be solved straigh...
In many scenarios, edge devices perform computations for applications such as target detection and tracking, multimodal sensor fusion, low-light image enhancement, and image segmentation. There is an increasing trend of deploying and running multiple...
With the extensive application of big spatial data and the emergence of spatial computing, augmented reality (AR) map rendering has attracted significant attention. A common issue in existing solutions is that AR-GIS systems rely on different platfor...
The rising integration of renewable energy sources and power electronic converters in modern power grids imposes increasingly stringent requirements on electromagnetic transient (EMT) simulation in terms of both precision and computational speed. Con...
This article presents an area-aware unified hardware accelerator of Weierstrass, Edward, and Huff curves over
Passive bistatic radar is a novel radar technology that passively detects targets without actively emitting signals. Since passive bistatic radar entails larger data volumes and computations compared to traditional active radiation radar, the develop...
The Number Theoretic Transform (NTT) is a cornerstone for efficient polynomial multiplication, which is fundamental to lattice-based cryptographic algorithms such as CRYSTALS-Kyber—a leading candidate in post-quantum cryptography (PQC). However...
Robotic controllers have to execute various complex independent tasks repeatedly. Massive processing power is required by the motion controllers to compute the solution of these computationally intensive algorithms. General-purpose graphics processin...
Convolutional Neural Networks (CNNs) have found widespread applications in artificial intelligence fields such as computer vision and edge computing. However, as input data dimensionality and convolutional model depth continue to increase, deploying...
Convolutional neural networks (CNNs) have significantly advanced various fields; however, their computational demands and power consumption have escalated, posing challenges for deployment in low-power scenarios. To address this issue and facilitate...
Now, with the availability of 3-D ultrasound data, a lot of research efforts are being devoted to developing 3-D ultrasound strain elastography (USE) systems. Because 3-D motion tracking, a core component in any 3-D USE system, is computationally int...
To accelerate the large-scale cellular automaton (CA) simulation for grain growth, a parallel CA model for grain growth was developed. The model was implemented based on the compute unified device architecture (CUDA) parallel computing platform. The...
The simulation of massive multiple-input multiple-output (MIMO) channel models is becoming increasingly important for testing and validation of fifth-generation new radio (5G NR) wireless networks and beyond. However, simulation performance tends to...
Geophysical forward modeling serves as a fundamental theoretical approach for characterizing subsurface structures and material properties, essentially involving the computation of gravity responses at surface or spatial observation points based on a...
Sparse matrix-vector multiplication (SpMV) is central to many scientific, engineering, and other applications, including machine learning. Compressed Sparse Row (CSR) is a widely used sparse matrix storage format. SpMV using the CSR format on GPU com...
To improve the seismic connectivity reliability (SCR) analysis efficiency of water distribution systems (WDS) based on Monte Carlo (MC) simulation, the quasi-Monte Carlo (QMC) method sampled by a low-discrepancy sequence is applied. Furthermore, a pa...
The advent of next-generation sequencing has greatly accelerated the field of human microbiome studies. Currently, investigators are seeking, struggling and competing to find new ways to diagnose, treat and prevent human diseases through the human mi...
The electrification of automotive powertrains has accelerated research efforts in the modeling, control, and monitoring of electric drive systems, where reliability, safety, and efficiency are key enablers for mass adoption. Despite a large corpus of...
Gas-insulated transmission lines (GILs) are increasingly deployed in dense urban power networks, where complex construction activities may introduce external mechanical impacts and pose risks to pipeline structural integrity. However, existing GIL mo...
The rising demand for real-time perception in aerial platforms has intensified the need for lightweight, hardware-efficient object detectors capable of reliable onboard operation. This survey provides a focused examination of real-time aerial object...
The Monte Carlo (MC) simulation, due to its ability to accurately simulate the backscattered signal of lidar, plays a crucial role in the design, optimization, and interpretation of the backscattered signal in lidar systems. Despite the development o...
Local path tracking is a critical challenge for autonomous vehicles, requiring precise trajectory following under nonlinear dynamics, strict constraints, and real-time execution. While Nonlinear Model Predictive Control (NMPC) has emerged as a leadin...
Value investing remains grounded in intrinsic value estimation, margin-of-safety reasoning, and disciplined fundamental analysis, but its practical execution is increasingly constrained by the scale, heterogeneity, and velocity of modern financial in...