Hang-and-Go: A Smart Laundry Hanging System

Washing clothes, drying up the clothes are the routine to the majority of the people. However, most of the people claimed that the process of drying up the clothes is the most challenging part due to the unpredictable weather in Malaysia. This project introduces Hang –and –Go: A Smart Laundry Hanging System that can automatically detect the presence of rain and sunlight and intelligently provide shelter for the clothes to protect them from the rain. This project is began with the objectives of studying people’s experience in doing the laundry process at home and small business scale, investigate existing laundry hanging system, develop a low cost laundry hanging system for household usage and lastly evaluate the performance of the developed system. In this project, a prototype is constructed using the combination of several tools which include Lego Mindstroms EV3, Tetrix, and also Arduino. On top of that, the prototype is targeted for the household usage and also for small business scale use. In order to collect the users’ experiences, problems faced and their view on the suggested solution, the research methodology uses in this project are interview and survey. Survey result shows that majority of the people agreed that the Hang-and-Go is an efficient approach that would help busy people to dry their clothes without human supervision. A series of experiment were conducted to test the functionality of the system. As a conclusion, Hang-and-Go is an unmanned robotic approach to automatically improve human on their laundry proces

Efficient Processing of k Nearest Neighbor Joins using MapReduce

k nearest neighbor join (kNN join), designed to find k nearest neighbors from a dataset S for every object in another dataset R, is a primitive operation widely adopted by many data mining applications. As a combination of the k nearest neighbor query and the join operation, kNN join is an expensive operation. Given the increasing volume of data, it is difficult to perform a kNN join on a centralized machine efficiently. In this paper, we investigate how to perform kNN join using MapReduce which is a well-accepted framework for data-intensive applications over clusters of computers. In brief, the mappers cluster objects into groups; the reducers perform the kNN join on each group of objects separately. We design an effective mapping mechanism that exploits pruning rules for distance filtering, and hence reduces both the shuffling and computational costs. To reduce the shuffling cost, we propose two approximate algorithms to minimize the number of replicas. Extensive experiments on our in-house cluster demonstrate that our proposed methods are efficient, robust and scalable.Comment: VLDB201

Optimization of Computer generated holography rendering and optical design for a compact and large eyebox Augmented Reality glass

Thesis (Master of Science in Informatics)--University of Tsukuba, no. 41288, 2019.3.2

Cancer Care System (CCS): Web-Based Social Interaction System for Cancer Community and Their Caregivers

The purpose of this study is to ensure communication among cancer community is well-supported. Thus, a model for a social interaction system was designed and developed for cancer community namely Cancer Care System (CCS). This study starts off with defining requirements to determine the components of the CCS. Three fact finding techniques were used in this phase namely interview, document study, and comparative analysis. The discovered components were then used to develop a prototype. This study followed by the design and development of CCS where prototyping approach was used. There are four steps involved in prototyping; identify basic requirements, develop initial prototype, use the prototype, and evaluate as operation prototype. User testing was carried out after the development of CCS was completed and the usability evaluation was then conducted through questionnaire. Computer System Usability Questionnaire (CSUQ) was adopted and utilized to measure users' satisfaction in this study. The subjects of this study are cancer patients, parents, and medical practitioners. The findings have shown that most of the users are satisfied with using the CCS in term of system usefulness, information quality, and interface quality. However, a few of suggestions from users should be taken into consideration for future improvement. Finally, this study was concluded by summarizing the overall results and achievement. The recommendations for future study also included

Empirijske metode za konverziju razdiobe intenziteta oborine s nekoliko dužih intervala na 1-minutni intenzitet u Maleziji

The rapid development of the radio communications system, especially in developed countries, has drawn the attention of telecommunication systems engineers to explore the frequency band above the Ku band. Because radio communication systems operating in the frequency band above the Ku band (10 G Hz) suffer from rain attenuation during rainy conditions, prediction of rain attenuation using a 1-min rainfall rate distribution is indeed vital. However, a 1-min rainfall rate distribution is not widely available compared to rainfall rate distributions with longer integration times. Therefore, a suitable conversion method is required to predict 1-min rainfall rate distributions of distinct integration times. This paper presents several conversion methods such as Segal, Burgueno et al., Chebil and Rahman, Joo et al., EXCELL RSC and LG . The Segal method provides an overall Root Mean Square (RMS ) error below 5% at different integration times and is suitable to be used in Malaysia.Brzi razvoj sustava radio komunikacija, a što je naročito izraženo u razvijenim zemljama, potaknuo je inženjere na istraživanje frekvencijskog pojasa iznad tzv. Ku pojasa. Naime, radiokomunikacijski sustavi koji rade u frekvencijskom pojasu iznad Ku-pojasa (10 GHz) podložni su prigušenju u oborinskim uvjetima. Stoga je predviđanje atenuacije radio signala korištenjem 1-min intenziteta oborine od velike važnosti. Međutim, za razliku od razdioba intenziteta oborine za duža kumulacijska vremena, razdiobe 1-min intenziteta nisu široko dostupne. Stoga je neophodna metoda konverzije za predviđanje distribucije 1-min intenziteta oborine za različita kumulacijska vremena. U ovom radu je prikazano nekoliko metoda konverzije kao što su metode Segala, Burguena i suradnika, Chebila i Rahmana, Jooa i suradnika, te EXCELL RSC i LG metoda. Metoda Segala daje ukupnu srednju kvadratnu pogrešku (Root Mean Square Error – RMS ) ispod 5% za različita kumulacijska vremena i pokazuje se prikladnom za upotrebu u Maleziji