Edge detection is a common operation in image/video processing applications. Canny edge detection, which performs well in different conditions, is one of the most popular and widely used of these algorithms. Canny’s superior performance is due mainly to its provision of the ability to adjust the output quality by manipulating the edge detection parameters, Sigma and Threshold. Calculating values for these two parameters on-the-fly and based on the application’s circumstances requires additional preprocessing, which increases the algorithm’s computational complexity. To reduce the complexity, several proposed methods simply employ precalculated, fixed values for the Canny parameters (based on either the worst or typical conditions), which sacrifices the edge detection’s performance in favor of the computational complexity. In this paper, an adaptive parameter selection method is proposed that selects values for the Canny parameters from a configuration table (rather than calculating in run-time), based on the estimated noise intensity of the input image and the minimum output performance that can satisfy the application requirements. This adaptive implementation of the Canny algorithm ensures that, while the edge detection performance (noise robustness) is higher than state-of-the-art counterparts in different circumstances, the execution time of the proposed Canny remains lower than those of recent cutting-edge Canny realizations.

Two-dimensional convolution has been used as a subsystem for filtering and enhancement in a wide range of signal and image processing applications. It is not only a memory intensive operation but also a compute intensive process. The previous efforts to improve the performance of 2-D convolution have primarily focused on the memory access challenges. However, the convolver’s performance is highly dependent on the efficient design of the computation units which can be enhanced significantly by employing some techniques such as pipelining. In this paper, a pipelined with low pixel access rate architecture for implementation of 2-D convolution is presented. Compared to the conventional convolvers, the proposed design involves a fixed (independent to the problem/kernel size), and a significantly shorter critical path specifically for large kernel sizes where the proposed convolver works with 283 MHz clock frequency, on a Xilinx Virtex-7 (XC7V2000t) FPGA, for a 3×3 kernel. Additionally, the required pixel access rate of the new scheme is less than that of the state-of-the-art methods which is only 849 MBps for a 3×3 kernel and 8-bit pixels. The improvements in the critical path delay and the required pixel access rate are obtained without significant increase in the resource utilization.

Creativity is increasingly recognized as an essential 21st-century skill, critical for innovation, problem-solving, and personal growth. Educational systems have responded by prioritizing creative thinking, prompting researchers to explore the potential of Learning Analytics (LA) to support and enhance creativity. This systematic review synthesizes empirical studies, theoretical frameworks, and methodological innovations from databases such as Web of Science, Scopus, ERIC, ProQuest, and Google Scholar, examining how creativity is operationalized within LA contexts. The review identifies diverse assessment frameworks, encompassing divergent thinking tests, product-based evaluations, behavioral metrics, and process-oriented assessments, often underpinned by the “4 Ps of Creativity” framework (Person, Process, Product, Press). Tools such as automated scoring systems, multimodal analytics, and AI-enhanced assessments demonstrate the potential to objectively and reliably capture creative processes and outcomes. However, significant challenges remain, including definitional ambiguity, inconsistent metrics, scalability issues, and ethical concerns related to data privacy. This review underscores the transformative capacity of LA to foster creativity in education while highlighting the critical need for standardized, robust methodologies and inclusive frameworks. By addressing identified gaps, future research can advance innovative approaches to assess and cultivate creativity using LA.