A system was developed that will serve as a learning tool for starters in sign language that involves hand detection. This system is based on a skin-color modeling technique, i.e., explicit skin-color space thresholding. The skin-color range is predetermined that will extract pixels (hand) from non-pixels (background). The images were fed into the model called the Convolutional Neural Network (CNN) for classification of images. Keras was used for training of images. Provided with proper lighting condition and a uniform background, the system acquired an average testing accuracy of 93.67%, of which 90.04% was attributed to ASL alphabet recognition, 93.44% for number recognition and 97.52% for static word recognition, thus surpassing that of other related studies. The approach is used for fast computation and is done in real time.
To ensure an error-free transmission in packet switching, additional check bits (either header or a payload) are typically appended to the input data of a message for error detection especially in a string of binary code. Normally, it comes from the input message and as a result of a deterministic algorithm after these data have been processed. The receiver system implements the said algorithm, while the transmitter used it to match the reliability of the sent information and detects whether an error bit has occurred or not. The corrupted bits will be corrected, recovered, and matched with the original message. To further improve the detection and correction of the corrupted transmitted bits, an enhanced error detection correction code implementation was proposed and developed in this paper. This will improve the limitations of using cyclic redundancy checking (CRC) code and Hamming code, by reducing the number of the redundancy bits ‘r’ in CRC due to the needed polynomial generator, and the overhead of interspersing of the r in conventional Hamming code, respectively. Xilinx Spartan 6 (XC7Z020-2CLG4841) FPGA was used to synthesize the proposed enhanced error detection code (EEDC) method. Based on the results, the transmission rate is faster, and an increase in detection of random errors compared with using CRC and Hamming codes.
Infrared thermography is the science of measuring the infrared energy emitted by an object, translating it to apparent temperature variance, and displaying the result as an infrared image. Significantly, acquiring thermal images delivers distinctive levels of temperature differences in solar panels that correspond to their health status, which is beneficial for the early detection of defects. The proposed algorithm aims to analyze the thermal solar panel images. The acquired thermal solar panel images were segmented into solar cell sizes to provide more detailed information by region or cell area instead of the entire solar panel. This paper uses both the image histogram information and its corresponding cumulative distribution function (CDF), useful for image analysis. The acquired thermal solar panel images are enhanced using grayscale, histogram equalization, and adaptive histogram equalization to represent a domain that is easier to analyze. The experimental results reveal that the extraction results of thermal images provide better histogram and CDF features. Furthermore, the proposed scheme includes the convolutional neural network (CNN) for classifying the enhanced images, which shows that a 97% accuracy of classification was achieved. The proposed scheme could promote different thermal image applications—for example, non-physical visual recognition and fault detection analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.