4 research outputs found
Charred grains (448): free-threshing wheat from Yarnton
No full textdorsal view of charred cereal grains (Triticum free-threshing) from archaeological excavations at Yarnton, England (2mm scalebar
Growing algorithmic thinking through interactive problems to encourage learning programming
No full textAttracting pupils from secondary schools (12–18 years old) to learn programming is not easy. It is especially the case in Belgium where there is no or very few programming and algorithm design courses in secondary schools. Another issue is that teachers who are in charge of computer science courses are afraid of teaching a matter they do not feel comfortable with, especially when they are not informatics teachers. This paper presents ILPADS, interactive learning of programming and algorithm design skills, an interactive website which aims at gradually growing algorithmic thinking skills to lead pupils towards the learning of the Python programming language. That website aims to serve as working material to support teachers for their computer science courses in secondary schools. Pupils can also use the website at home to continue learning on their own. The paper presents the interactive website and mainly focuses on the design of the ILPADS activities. Future work includes testing the website in real classrooms and evaluating it
Convolutional Deep Neural Network and Full Connectivity for Speech Enhancement
No full textThe speech signal that is received in real-time has background noise and reverberations, which have an impact on the quality of speech. Therefore, it is crucial to reduce or eliminate the noise and increase the intel-ligibility and quality of speech signals. In this study, a proposed method that is the most effective and challenging in a low SNR environment for three types of noise are removed, including washing machine, traffic noise, and electric fan noise, and clean speech is recovered. with three samples of noise which are mixed and added to the clean speech signal with a lower level of SNR value fixed at (-5, 0, 5) dBs, that noise source takes equal weights. The enhancement of the corrupted speech signal is done by applying a fully connected and convo-lutional neural network-based denoising algorithm and comparing their perfor-mance. The proposed network shows that a fully connected network (FCN) has less elapsed time than a convolutional network (CNN) while still achieving better performance, demonstrating its applicability for an embedded system. Also, the results obtained show that, overall, the CNN is better than the FCN regarding maximum coloration, PSNR, MES, and STOI
Road Equipment Procurement and Utilization Study, October 2002
No full textFinal report of road equipment procurement and utilization study
