Abstract: Convolutional neural networks (CNNs) have attracted much attention in change detection (CD) for their superior feature learning ability. However, most of the existing CNN-based CD methods ...

Abstract: Although the vision transformer-based methods (ViTs) exhibit an excellent performance than convolutional neural networks (CNNs) for image recognition tasks, their pixel-level semantic ...

Abstract: Sequential recommender systems seek to capture information about user affinities and behaviors considering their sequential series of interactions. While former models based on Markov Chains ...

Abstract: This paper proposes a method to improve the accuracy of an absolute magnetic encoder by using harmonic rejection (HR) and a dual-phase-locked loop (DPLL). The encoder consists of two ...

Abstract: This brief presents a power and memory-optimized hardware implementation for the open forward error correction (oFEC) encoder proposed for high-speed fiber ...

Abstract: The human motor system has often been studied as a feedback control problem, but the integration of binary neuronal encoding in these formulations has been largely overlooked. Neuronal ...

Abstract: In this work, alternatives to implement 8b/10b encoders in FPGAs for serializer-deserializer links are evaluated. Custom implementations based on decoders and look-up tables are benchmarked, ...

Abstract: Transformers are widely used in natural language processing and computer vision, and Bidirectional Encoder Representations from Transformers (BERT) is one of the most popular pre-trained ...

Abstract: Light detection and ranging (LiDAR) point cloud denoising is critical for reliable environmental perception in autonomous driving and robotics. To overcome the lack of real-noise datasets ...

Abstract: This paper presents a design of a reversible priority encoder that is an important element for encoding and decoding addresses. Promising nanotechnology, Quantum-dot Cellular Automata (QCA), ...