Quantum correlations and measurements in tri-partite quantum systems.

Correlations and entanglement in a chain of three oscillators A,B,C with nearest neighbour coupling is studied. Oscillators A,B and B,C are coupled but there is no direct coupling between oscillators A,C. Examples with initial factorizable states are considered, and the time evolution is calculated. It is shown that the dynamics of the tri-partite system creates correlations and entanglement among the three oscillators and in particular, between oscillators A,C which are not coupled directly. We have performed photon number selective and non-selective measurements on oscillator A and we investigated their effects on the correlations and entanglement. It is shown that, before the measurement, the correlations between oscillators A,C can be stronger than the correlations of oscillators A,B. Moreover, some entanglement witness shows that oscillators A,C are entangled but the oscillators A,B might or might not be entangled. By using quantum discord, which measures the quantumness of correlations, it is shown that there are quantum correlations between oscillators A,B and after the measurements in both cases of selective and non-selective measurements, oscillators A,B and A,C become classically correlated.Ministry of Higher Education, Malaysia and Universiti Kebangsaan, Malaysia

Framework based on Mobile Augmented Reality for Translating Food Menu in Thai Language to Malay Language

Augmented reality (AR) technology is a technique that combines the real world and the virtual world digitally using mobile devices. Mobile AR technology is expected to help Malaysian tourists who have difficulties to understand the Thai language when visiting the country. Hence, a prototype called ARThaiMalay  translator was developed to translate printed Thai food menu to Malay language. The objective of this study is to design a food menu translation framework from Thai to Malay language based on mobile AR, develop a translator application and to test the effectiveness of the translator application. The prototype consists of three main components which are translation based on optical character recognition (OCR) technology, dictionary development using SQLite database  and display data from the local database. Evaluation of the developed application shows its effectiveness to perform translation of Thai text with certain features to Malay language

Parallel strategy of implementing composite Newton-Cotes rules using message passing on parallel computing systems

The paper describes the parallel implementation of composite Newton-Cotes rules (Trapezoidal and Simpson’s ⅓ rules) under PVM-based environment for approximating one-dimensional definite integral on parallel and distributed computing systems.The parallelism is realized by master-slave relationship where the master process decomposes the interval of integration into n subintervals, then distribute to the slave processes.Thereby initiating work pool technique to ensure perfect workload balanced state to avoid unnecessary communication overheads among the various contending processors.The effectiveness of the approach used in connection with the novel workload management scheme is demonstrated in the good quality results and the global load optimization for the tested applied application problem

A linear model based on Kalman filter for improving neural network classification performance

Neural network has been applied in several classification problems such as in medical diagnosis, handwriting recognition, and product inspection, with a good classification performance. The performance of a neural network is characterized by the neural network's structure, transfer function, and learning algorithm. However, a neural network classifier tends to be weak if it uses an inappropriate structure. The neural network's structure depends on the complexity of the relationship between the input and the output. There are no exact rules that can be used to determine the neural network's structure. Therefore, studies in improving neural network classification performance without changing the neural network's structure is a challenging issue. This paper proposes a method to improve neural network classification performance by constructing a linear model based on the Kalman filter as a post processing. The linear model transforms the predicted output of the neural network to a value close to the desired output by using the linear combination of the object features and the predicted output. This simple transformation will reduce the error of neural network and improve classification performance. The Kalman filter iteration is used to estimate the parameters of the linear model. Five datasets from various domains with various characteristics, such as attribute types, the number of attributes, the number of samples, and the number of classes, were used for empirical validation. The validation results show that the linear model based on the Kalman filter can improve the performance of the original neural network

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Mobile Application for Medicinal Plants Recognition from Leaf Image Using Convolutional Neural Network

Indonesia is a country that has thousands of plant types that can be used as traditional medicine. However, some people have not utilized this potential optimally due to the lack of knowledge about medicinal plants' types, benefits, and substances. Therefore, there is a need to develop an application that can identify medicinal plants that grow in Indonesia and provide information about the benefits and content of the substances contained in them. In this study, medicinal plants will be recognized using a mobile application from leaf images based on a pre-trained convolutional neural network (CNN) with a transfer learning technique. Three pre-trained CNN architectures, namely VGG-16, MobileNetV2, and DenseNet-121, are explored for medicinal plant recognition. Hyperparameter tuning is performed at the fully connected layer of all architectures with 20 possible modifications to find the best model. The experimental results on 24 types of medicinal plants show that the model based on MobileNetV2 achieves the best classification accuracy of 97.74%. The best model is obtained by modifying the fully connected layer of MobileNetV2 into three dense layers with the number of neurons 736, 448, and 928, respectively. After the application recognizes the types of medicinal plants, information about the benefits and substances contained in them is displayed to the user

Rantai Pasok Usaha Penggilingan Padi Studi Kasus : Ud. Putra Tunggal Kabupaten Kolaka Timur

This study aims to identify and describe the supply chain system in the rice milling business. This study is an explanatory research. The case study was conducted at UD. Only Son in East Kolaka. The results of this study are, the supply chain system in the rice milling business consists of 2 groups, namely the raw material supply channel (grain) and the product distribution channel (rice). The channel for supplying raw materials (grain) is dominated by the Farmer - Grain Collector - Company channel which has a 50% share of the total annual purchase volume. Meanwhile, the product distribution channel (rice) is dominated by the Company - Wholesaler channel which has a 55% share of the total annual sales volume. The complexity of the supply chain creates the possibility of potential financial risks. Most of the supply chain in terms of volume and percentage (>50%) is traversed by products, namely from farmers, grain traders, UD Putra Tunggal, and wholesalers.Penelitian ini bertujuan untuk  mengetahui dan menggambarkan sistem rantai pasok pada usaha penggilingan padi. Studi ini merupakan penelitian eksplanatori. Studi kasus dilakukan pada UD. Putra Tunggal di Kolaka Timur. Hasil penelitian ini adalah, Sistem rantai pasok pada usaha penggilingan padi terdiri atas 2 kelompok yaitu Saluran pemasok bahan baku (gabah) dan Saluran distribusi produk (beras). Saluran pemasok bahan baku (gabah) didominasi oleh saluran  Petani - Pedagang Pengumpul Gabah - Perusahaan yang mempunyai pangsa 50% dari  total volume pembelian pertahun. Sedangkan saluran distribusi produk (beras) didominasi oleh saluran Perusahaan - Pedagang Besar yang mempunyai pangsa 55% dari  total volume penjualan pertahun. Kompleksnya rantai pasok mengakibatkan adanya kemungkinan potensi risiko keuangan. Rantai pasokan terbanyak dalam volume dan prosentase (>50%) dilalui produk yaitu dari petani, pedagang pengumpul gabah, UD Putra Tunggal, dan pedagang besar

Mobile Application for Medicinal Plants Recognition from Leaf Image Using Convolutional Neural Network

Indonesia is a country that has thousands of plant types that can be used as traditional medicine. However, some people have not utilized this potential optimally due to the lack of knowledge about medicinal plants' types, benefits, and substances. Therefore, there is a need to develop an application that can identify medicinal plants that grow in Indonesia and provide information about the benefits and content of the substances contained in them. In this study, medicinal plants will be recognized using a mobile application from leaf images based on a pre-trained convolutional neural network (CNN) with a transfer learning technique. Three pre-trained CNN architectures, namely VGG-16, MobileNetV2, and DenseNet-121, are explored for medicinal plant recognition. Hyperparameter tuning is performed at the fully connected layer of all architectures with 20 possible modifications to find the best model. The experimental results on 24 types of medicinal plants show that the model based on MobileNetV2 achieves the best classification accuracy of 97.74%. The best model is obtained by modifying the fully connected layer of MobileNetV2 into three dense layers with the number of neurons 736, 448, and 928, respectively. After the application recognizes the types of medicinal plants, information about the benefits and substances contained in them is displayed to the user

Automatic Image Segmentation using Sobel Operator and k-Means Clustering: A Case Study in Volume Measurement System for Food Products

Image segmentation plays an important role in automatic visual inspection of food product using computer vision system. However, segmentation of food product image is not easily performed if the image has low contrast with its background or the background in acquired image is not homogeneous. This paper proposes a method for automatic food product image segmentation using Sobel operator and k-means clustering. Sobel operator was used to determine region of interest (ROI). k-means clustering was then used to separate object and background in ROI. The area outside ROI was considered as background. The proposed method has been validated using 100 images of food product from ten different types. The experimental results show that the proposed method achieves good segmentation result

Procedural Database Normalization Level Validation for Electronic Certificate Issuance

The Ministry of Communication and IT (MCIT) conducts computer training course in seven provinces of Afghanistan. These six months training courses taught computer packages for the employees of the Ministry and others sectors. At the end of this course, each participant receives a computer certificate called ECP (Electronic Certificate Printing). The current ECP issuance process faces severe problems due to the lack of procedures to validate database normalization levels in databases sent by training centers. Importation of data from non-verified database into the ECP Database has produced a mass of redundant data in an active system that cannot be redesigned from scratch. The primary consequence of this problem is that the officials in the administration office cannot find the relevant data about each student quickly, because the proper field and tables from the unverified database could