Localized Electroconvection at Ion-Exchange Membranes with Heterogeneous Surface Charge

Operating electrochemical membrane processes beyond the limiting current density bears the potential to decrease the investment cost of desalination plants significantly. However, while there are strategies for successfully reducing energy demand by shortening the plateau region, their influence on the formation of electroconvection is still unknown. This study demonstrates control over the electroconvective vortices' rotational direction and position using a surface patterning method. We compare the development of electroconvection at two membranes modified with patterns of different surface charges. We analyze the electroconvective vortex field's build-up, the vortices' rotational direction, and structural stability in the steady-state. Finally, we showcase the control possibilities by enforcing a specific structure along an asymmetric letter pattern. Such tailor-made patterns have the potential to diminish the plateau region's energy loss completely. Furthermore, the scale-up of these membranes to industrial processes will allow the economic operation in the overlimiting regime, significantly increasing their space-time yield.

Cointegration of single-transistor neurons and synapses by nanoscale CMOS fabrication for highly scalable neuromorphic hardware

Cointegration of multistate single-transistor neurons and synapses was demonstrated for highly scalable neuromorphic hardware, using nanoscale complementary metal-oxide semiconductor (CMOS) fabrication. The neurons and synapses were integrated on the same plane with the same process because they have the same structure of a metal-oxide semiconductor field-effect transistor with different functions such as homotype. By virtue of 100% CMOS compatibility, it was also realized to cointegrate the neurons and synapses with additional CMOS circuits. Such cointegration can enhance packing density, reduce chip cost, and simplify fabrication procedures. The multistate single-transistor neuron that can control neuronal inhibition and the firing threshold voltage was achieved for an energy-efficient and reliable neural network. Spatiotemporal neuronal functionalities are demonstrated with fabricated single-transistor neurons and synapses. Image processing for letter pattern recognition and face image recognition is performed using experimental-based neuromorphic simulation.

Pengenalan Citra Huruf Hijaiah Menggunakan Metode Gray Level Co-Occurrence Matrices (Glcm) Dengan 4 Sudut Orientasi Dan Jaringan Syaraf Tiruan Backpropagation

Arabic is one of the international languages according to the United Nations (UN) which was adopted by General Council resolution 3190 (XXVIII) as the official language and working language of the General Council and Main Committees on 18 December 1973. Arabic can be found in the holy book Al - Qur'an. For a Muslim, it is obligatory to learn and master Arabic in order to read and understand the contents of the Al-Qur'an. job applicant from Indonesia is also have to learn Arabic. The Hijaiiyah letter has the same role as the alphabet, which is to compose every word and sentence. Humans have a natural intelligence to be able to recognize each Hijaiiyah letter based on the special characteristics or patterns contained in each letter. However, natural intelligence has deficiencies such as inconsistencies in assessing the similarity of each handwritten Hijaiiyah letter from different people. Therefore this research will develop a system for identifying or recognizing Hijaiiyah handwritten patterns using the Gray Level Co-occurrence Matrices (GLCM) method with 4 orientation angles and Backpropagation Artificial Neural Network (ANN). Data was collected using the Autodesk Sketchbook application so that can reduce the noise. The purpose of this research is to know the level of accuracy and precision of the classification of the Hijaiiyah letter pattern. In this research, the amount of data used was 1500 images of Hijaiiyah letters. The highest accuracy is 45.1111% with a precision 45.1111%.

Pierce Similarity Algorithm In Detection Of Jar Letter In Al-Quran As Basic Media For Learning Arabic Language Structure

Al-Quran is the basic reason someone should understand the rules of Arabic. One of the basic rules is knowing the jar type which we generally often encounter in Arabic, but we do not understand that this letter has its own duties and functions. In this study, samples of Jar letters were used as many as 7 jar letter patterns which are generally often encountered in the Al-Quran. The purpose of this research is to build a system that can recognize Jar letters using the Pierce Similarity method and performs the performance on the algorithm. The research method used is the theory of pattern recognition in image processing with 2 processes, namely the Training Process and the Testing Process. The value of each letter pattern obtained in the Training Process will be the weight benchmark for the Testing Process, so that we can measure the performance level of Algortima Pierce Similarity in detecting the Jar letter pattern. The results can vary for each letter pattern ranging from 60% to 80%.

Optimization of Pattern Recognition of Hijaiyah Letters using Normalized Cross Correlation Tchniques

The development of analysis in digital image increasingly developed with various methods, one of which is in recognition of letter patterns. Each letter written using handwriting must have different writing patterns, such as the thickness and shape of the letter pattern. This research will be doing on the pattern recognition of hijaiyah letters of handwriting by applying the Normalized Cross Correlation (NCC) technique. NCC is a technique used to match two images. Before the NCC process, it should be done with the preprocessing using convolution and without convolution using the binary image. The convolution technique used was the Sobel and Prewitt edge detection with the aimed to get the edge of an object and compared the number of matching letters between using edge detection and without edge detection. The tests were done by using the different sized image of 32x32 pixels, 64x64 pixels and then match it against a similar sample data, a different sample data, a different objects font sample data and a different sample data of original image size. The results show that the matching of the letter pattern depends on the size of the image that is more matches to the image of 32x32 pixels. The binary image had better matching numbers than the convolution techniques. While in convolution techniques, Prewitt edge detection had the higher accuracy and matching results compared to the image using Sobel edge detection.

APLIKASI JARINGAN SYARAF TIRUAN DALAM PENGENALAN POLA HURUF PEGON JAWA

Artificial Neural Network (ANN) Technology) can help humans in processing data into information with design resembling the performance of the human brain. ANN adopts 5 aspects of human capability: Memorization, Generalization, Efficiency, Accuracy, and Tolerance in its application. ANN proves to be effective in pattern recognition. Researchers developed an application implementing ANN to recognize Java Pegon Letter pattern. The research uses 160 image data, divided into 100 training data (consisting of 5 normal images for each character) and 60 test data (consisting of 1 normal data, 1 data is not complete/corrupt, and 1 data with noise) for each character. The data obtained from the processed captures, so all of data have the same dimensions and size: 100x100 pixels. All data is processed through preprocessing and extraction stages. Furthermore, the data result is used in training stage to recognize the pattern of Java Pegon by applying the Learning Vector Quantization method. The application can recognize Java pegon pattern very well. The application can recognize 100% of training data and test data. This application also has the ability to recognize abnormal data very well, such as data with noise or corrupted data.

EVALUASI PEMANFAATAN MEDIA AUDIO “ABC” (AKU BACA DALAM CERITA) UNTUK MENGENALKAN HURUF PADA PAUD

Audio media “ABC” (Aku Baca dalam Cerita) a learning media that introduces letters through stories for Early Childhood Education aged 5-6 years. To find out the success of the media, it is necessary to evaluate: Is ABC audio media can help introduce alphabet on early childhood learners? How the level of development of the language aspects of students after learning with ABC audio media? Evaluation of the use of audio media ABC aims to know the audio media ABC in introducing letters to students in early childhood and to determine the development of language students with learning ABC audio media. This research was conducted for 10 days on 1-14 August 2017 in ABA Gamping TK, Sleman, Yogyakarta. The population of this research is all of ABA Gamping TK students with the sample of 40 PAUD students. The results of this study obtained data that the level of developmental achievement of students aged 5-6 years in recognizing the letter pattern was good. Thus it can be seen that ABC learning audio media can help in introducing letters to students in early childhood. At the level of achievement of language development, it is shown that most students can achieve the predetermined indicators. This shows that ABC audio media can improve language development in early childhood education. However, there is still a need for variations in the presentation format in ABC learning audio media programs to stimulate the imagination of students in early childhood and the need for supporting materials that can stimulate the students' creativity.ABSTRAKMedia Audio “ABC” (Aku Baca dalam Cerita) merupakan media audio pembelajaran yang memperkenalkan huruf melalui cerita untuk Pendidikan Anak Usia Dini usia 5-6 tahun. Untuk mengetahui keberhasilan media tersebut perlu dilakukan evaluasi tentang: Apakah media audio ABC dapat membantu mengenalkan huruf pada peserta didik PAUD? Bagaimana tingkat perkembangan aspek bahasa peserta didik setelah belajar dengan media audio ABC? Tujuan evaluasi pemanfaatan media audio ABC ini yaitu untuk mengetahui media audio ABC dalam mengenalkan huruf pada peserta didik PAUD dan untuk mengetahui perkembangan bahasa peserta didik dengan belajar media audio ABC. Penelitian ini dilaksanakan selama 10 hari pada tanggal 1-14 Agustus 2017 di TK ABA Gamping, Sleman, Yogyakarta. Populasi dari penelitian ini adalah seluruh siswa TK ABA Gamping dengan sampel 40 siswa PAUD. Hasil dari penelitian ini diperoleh data bahwa tingkat pencapaian perkembangan peserta didik usia 5-6 tahun dalam mengenal pola huruf sudah baik. Dengan demikian dapat diketahui bahwa media audio pembelajaran ABC dapat membantu dalam mengenalkan huruf pada peserta didik PAUD. Pada tingkat pencapaian perkembangan bahasa ditunjukkan bahwa peserta didik sebagian besar dapat mencapai indikator yang telah ditentukan. Hal ini menunjukkan bahwa media audio ABC dapat meningkatkan perkembangan bahasa pada pendidikan anak usia dini. Saran, perlunya variasi format sajian dalam program media audio pembelajaran ABC untuk merangsang imajinasi peserta didik pada PAUD dan perlunya bahan penunjang yang dapat merangsang keratifitas peserta didik.

PENERAPAN METODE BACKRPOPAGATION UNTUK IDENTIFIKASI HURUF HIJAIYAH TULISAN TANGAN

Hijaiyah letters are Arabic spelling letters that are the original language of the Qur'an. Just like other types of letters, the hijaiyah has certain shapes and characteristics that will form a certain pattern. By using the concept of artificial neural networks, can dibanguun a system that can recognize the pattern by doing the previous training. One of the most commonly used meotodes in artificial neural network paradigms is the crawling or backpropagation buffer. This hijaiyah letters identification system is built using the handwritten hijaiyah image data of 150 images. The feature or feature taken from the image is the binary value of the letter pattern and the number of objects contained in the letters. Prior to the feature extraction process, the image first passes the preprocessing stage consisting of color binerization, object widening, cropping, and resizing. The result obtained by backpropagation method is the system is able to recognize handwriting hijaiyah pattern well. All training data have been correctly identified, while as many as 150 test data can be identified as 77 letters with an accuracy of 51.33%. This accuracy value is obtained with the architectural arrangement of the number of hidden layer neurons = 60, minimum error = 0.001 and maximum iteration = 10000.keyword:backpropagation, biner, hijaiyah, , pattern, preprocessing

Capital Letter Pattern Recognition in Text to Speech by Way of Perceptron Algorithm

Computer vision is a data transformation retrieved or generated from webcam into another form in means of determining decision. All kinds of transformations are carried through to attain specific aims. One of the supporting techniques in implementing computer vision on a system is digital image processing as the objective of digital image processing is to transform digital-formatted picture so that it can be processed in computer. Computer vision and digital image processing can be implemented in a system of capital letter introduction and real-time handwriting reading on a whiteboard supported by artificial neural network mode “perceptron algorithm” used as a learning technique for the system to learn and recognize the letters. The way it works is captured in letter pattern using a webcam and generates a continuous image that is transformed into digital image form and processed using several techniques such as grayscale image, thresholding, and cropping image.