Implementation of a number recognition algorithm built using a neural network on the BASYS3 FPGA panel

In recent years, some field programmable valve arrays (FPGAs) based on CNN release phase accelerators have been introduced. FPGA is widely used in portable devices. They can be programmed to achieve higher concurrency and provide better performance. The power consumption of the FPGA is lower than that of GPUs with the same workload. These reasons make the FPGA suitable for implementing the CNN release phase. They can provide relative output performance for GPUs and achieve low power consumption, which is very important for portable devices. To effectively implement the CNN output phase on the FPGA, the design should have high parallelism, and the hardware resources used should be minimized to reduce the area and power consumption. In the process of working with the help of a neural network, an algorithm for recognizing handwritten numbers is implemented. A special architecture is being created to implement a neural network at the appatent level. The performance during operation and power consumption is comparable to the performance of the processor and the GPU.

Number Evaluation for Auditorily and Verbally Challenged Children

The objective of this paper is to develop a system designed on MATLAB 2021a to serve as a holistic assessment tool for Auditorily and Verbally challenged children studying in grades 1 and 2. This system captures and recognizes specific hand gestures indicated by these children as answers to mathematical questions displayed on the system’s screen. It then proceeds to evaluate the answers as right or wrong and accordingly displays the scores obtained. This project addresses the steps that must be undertaken to develop a viable, functioning number recognition assessment tool. Algorithms have been chosen taking into consideration obstacles that may arise out of poor illumination, complicated backgrounds or incorrect rotation of the images captured. We have utilized: Image pre-processing, Feature extraction, Feature Classification and the MATLAB App Designer to achieve our objective.

Brief Interventions Influence the Quantity and Quality of Caregiver-Child Conversations in an Everyday Context

Reading and arithmetic are difficult cognitive feats for children to master and youth from low-income communities are often less “school ready” in terms of letter and number recognition skills (Lee and Burkam, 2002). One way to prepare children for school is by encouraging caregivers to engage children in conversations about academically-relevant concepts by using numbers, recognizing shapes, and naming colors (Levine et al., 2010; Fisher et al., 2013). Previous research shows that caregiver-child conversations about these topics rarely take place in everyday contexts (Hassinger-Das et al., 2018), but interventions designed to encourage such conversations, like displaying signs in a grocery store, have resulted in significant increases in caregiver-child conversations (Ridge et al., 2015; Hanner et al., 2019). We investigated whether a similar brief intervention could change caregiver-child conversations in an everyday context. We observed 212 families in a volunteer-run facility where people who are food-insecure can select food from available donations. Volunteers greet all the clients as they pass through the aisles, offer food, and restock the shelves as needed. About 25% of the clients have children with them and our data consist of observations of the caregiver-child conversations with 2- to 10-year-old children. Half of the observation days consisted of a baseline condition in which the quantity and quality of caregiver-child conversation was observed as the client went through aisles where no signs were displayed, and volunteers merely greeted the clients. The other half of the observation days consisted of a brief intervention where signs were displayed (signs-up condition), where, volunteers greeted the clients and pointed out that there were signs displayed to entertain the children if they were interested. In addition, there was a within-subject manipulation for the intervention condition where each family interacted with two different categories of signs. Half of the signs had academically-relevant content and the other half had non-academically-relevant content. The results demonstrate that the brief intervention used in the signs-up condition increases the quantity of conversation between a caregiver and child. In addition, signs with academically-relevant content increases the quality of the conversation. These findings provide further evidence that brief interventions in an everyday context can change the caregiver-child conversation. Specifically, signs with academically-relevant content may promote school readiness.