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Integration of Home Automation and Security System Controller with FPGA Implementation Ngieng Bryna Sing Yii, Nabihah Ahmad, Mohd Helmy Abd Wahab, Warsuzarina Mat Jubadi, Chessda Uttraphan and Syed Zulkarnain Syed Idrus Abstract: A home automation system is essential for promoting a safe and comfortable living environment and notable energy conservation for the user. However, the system’s favour had been obstructed by cost, power usage, inadequate security, complexity, and no emergency backup power. Current home automation systems with controllers were limited by their number of ports, fixed architecture, non-durable and non-parallel executions. Keeping this in view, integration of home comfort system, security system, and the automatic load transfer switch features are proposed using the base of Cyclone IV E: EP4CE115F29C7 FPGA Board (DE2-115). The top-level module is developed via Verilog Hardware Descriptive Language (HDL) with the bottom-up technique and used test bench for functional verification via ModelSim-Altera. The PWM method was applied to the lighting system to control the dimming of light through its digital signals via a maximum 500000 counter to improve energy efficiency for the proposed design. In this project, 200Hz pulses are successfully simulated to prevent visible flickering of lights in duty cycle generation. The light intensity of 40% and 100% are verified and successfully generated according to the inputs provided by the status of the LDR sensor and IR sensor. The proposed controller gives correct corresponding outputs to the 13 actuators based on the detected input stimuli. The proposed design utilized a total of 162 (<1%) logic elements, 32 registers, and total pins of 74 (14%). The proposed design successfully integrated the three-sub module and provided control on comfort and security system operations to prevent service failure during power blackout conditions at the top-level and utilized a low ratio of the FPGA. Keywords: FPGA; Home Automation; PWM; Security. |
The Application of Computer-Aided Under-Resourced Language Translation for Malay into Kadazandusun Mohd Shamrie Sainin, Minah Sintian, Suraya Alias and Asni Tahir Abstract: A computer-aided language translation using a Machine translation (MT) is an application performed by computers (machines) that translates one natural language to another. There are many online language translation tools, but thus far none offers a sequence of text translations for the under-resourced Kadazandusun language. Although there are web-based and mobile applications of Kadazandusun dictionaries available, the systems do not translate more than one word. Hence, this paper aims to present the discussion of the preliminary translation of Malay to Kadazandusun. The basic word-to-word with dictionary alignment translation based on Direct Machine Translation (DMT) is selected to begin the exploration of the translation domain where DMT is one of the earliest translation methods which relies on the word-to-word approach (sequence-to-sequence model). This paper aims to investigate the under-resourced language and the task of translating from the Malay language to the Kadazandusun language or vice versa. This paper presents the application and the process as well as the results of the system according to the basic Kadazandusun word arrangement (Verb-Subject-Object) and its translation quality using the Bilingual Evaluation Understudy (BLEU) score. Several phases are involved during the process, including data collection (word pair translation), preprocessing, text selection, translation procedures, and performance evaluation. The preliminary language translation approach is proven to be capable of producing up to 0.5 BLEU scores which indicate that the translation is readable, however, requires post-editing for better comprehension. The findings are significant for the quality of the under-resourced language translation and as a starting point for other machine translation methodologies such as statistical or deep learning-based translation. Keywords: Computer-aided; Kadazandusun; Language Translation; Machine Translation; Malay; Under-resourced. |
Development of a Wearable Sensor Glove for Real-Time Sign Language Translation Radzi Ambar, Safyzan Salim, Mohd Helmy Abd Wahab, Muhammad Mahadi Abdul Jamil and Tan Ching Phing Abstract: This article describes the development of a wearable sensor glove for sign language translation and an Android-based application that can display words and produce speech of the translated gestures in real-time. The objective of this project is to enable a conversation between a deaf person and another person who does not know sign language. The glove is composed of five (5) flexible sensors and an inertial sensor. This article also elaborates the development of an Android-based application using the MIT App Inventor software that produces words and speech of the translated gestures in real-time. The sign language gestures were measured by sensors and transmitted to an Arduino Nano microcontroller to be translated into words. Then, the processed data was transmitted to the Android application via Bluetooth. The application displayed the words and produced the sound of the gesture. Furthermore, preliminary experimental results demonstrated that the glove successfully displayed words and produced the sound of thirteen (13) translated sign languages via the developed application. In the future, it is hoped that further upgrades can produce a device to assist a deaf person communicates with normal people without over-reliance on sign language interpreters. Keywords: Android application; Sign Language Translator; Smart Glove; Speech; Wearable Technology; Words. |
Automated Platelet Counter with Detection Using K-Means Clustering Shafaf Ibrahim, Muhammad Faris Afiq Fauzi, Nur Nabilah Abu Mangshor, Raihah Aminuddin and Budi Sunarko Abstract: Platelet is a blood cell type that is stored and circulated in the human body. It acts as a blood thickening agent and prevents blood from overflowing whenever bleeding occurs. An excessive or inadequate number of platelets could lead to platelet-related diseases. The current practice of platelet counting involves the manual counting process using a haemocytometer, Wright’s Stain which uses the dyes to facilitate the differentiation of blood cell types, and a tally counter. Yet, this process can be time-consuming, demanding, and exhausting for haematologists, and likely to be prone to errors. Thus, this paper presents a study on automated platelet counter and detection using image processing techniques. The K-Means Clustering was employed to count and detect the presence of platelets in microscopic blood smear images. Several processes were performed prior to the K-means clustering, including image enhancement and YCbCr image formatting. Subsequently, image masking, as well as area thresholding were applied to eliminate every unwanted entity and highlight the visibility of the platelets before the number of platelets could be detected and counted. A comparative experiment was designed in which the K-Means Clustering platelet count and detection were compared with the actual number of platelets reported by haematologists. The platelet counts and detection were categorized into three detection categories which are Less Detection (LD), Accurate Detection (AD), and Over Detection (OD). The proposed study was evaluated to 90 testing platelet images. Out of the 90 testing images, 75 platelet images were perfectly counted and detected which returned 91.67% of accuracy. This signifies that the K-Means Clustering algorithm was discovered to be efficient and dependable for automated platelet counter and detection. Keywords: Detection; Image Processing; K-Means Clustering; Platelet Counts. |
Prediction of MUET Results Based on K-Nearest Neighbour Algorithm Norlina Mohd Sabri and Siti Fatimah Azzahra Hamrizan Abstract: The machine learning based prediction has been applied in various fields to solve different kind of problems. In education, the research on the predictions of examination results is gaining more attentions among the researchers. The adaptation of machine learning for the prediction of students’ achievement enables the educational institutions to identify the high failure rate, learning problems, and reasons for low student performance. This research is proposing the prediction of the Malaysian University English Test (MUET) results based on the K-Nearest Neighbour Algorithm (KNN). KNN is a powerful algorithm that has been applied in various prediction problems. The prediction of the MUET results would help the students and lecturers to be more well prepared and could improve the required English language skills accordingly before the actual examination. The MUET result prediction is based on the student’s English courses grades and there are 516 data of students’ results that have been collected from Universiti Teknologi MARA (UiTM) Dungun campus. The performance measurement that has been used are the mean accuracy, percentage error and mean squared error (MSE). In this research, the KNN prediction model has generated an acceptable performance with 65.29% accuracy. For future work, KNN could be modified or hybridized to further improve its performance. Furthermore, other algorithms could also be explored into this problem to further validate the best predictive model for the prediction of the MUET results. Keywords: English Language Examination; K-Nearest Neighbour; MUET; Prediction. |
An Improved Evolutionary Algorithm in Formulating a Diet for Grouper Cai-Juan Soong, Rosshairy Abd Rahman and Razamin Ramli Abstract: This paper reveals the high demand of fish products in many countries, which subsequently highlighted the high demand of grouper fish species for human consumption. This high demand leads to the insufficient supply of wild ocean grouper fish in the market, thus justifying the need for farmed or cultured grouper fish. Basically, in grouper fish farming, large amounts of trash fish are needed as the feed for grouper fish, which is the carnivorous type of fish. However, since the cost of trash fish is too high, searching for alternative ingredients for the feed through modelling of feed formulation is an option for reducing or minimizing the farming cost. This led to the search for methods in giving the best combination of feedstuff ingredients with appropriate nutrients in formulating the feed. One prospective method is the Evolutionary Algorithm (EA) that has been applied in solving similar problems of diet formulation for several types of animals including livestock, poultry and shrimp. Hence, in this paper, an improved EA method known as the SR-SD-EA is proposed highlighting three important EA operators, which are initialization, selection and mutation. A semi random initialization operator is introduced to filter some important constraints thus increase the chances of obtaining feasible formulations or solutions. Subsequently, the novel selection operator embeds the concept of standard deviation in the SR-SD-EA as part of the function in minimizing the total cost of the formulated grouper fish feed. Eventually, the enhanced boundary-based mutation is also introduced in the algorithm to ensure the crucial constraint of the ingredients’ total weight must be met. The overall structure of the SR-SD-EA is presented as a framework, where the three methodological contributions are embedded. The preliminary findings of SR-SD-EA show that the obtained cost computed based on the Best-So-Far feed formulation as the solution is comparable, while all the crucial constraints are fulfilled.. Keywords: Binary-Standard Deviation Tournament Selection; Boundary-based Mutation; Evolutionary Algorithm; Feed formulation; Grouper fish. |
Chest X-Ray Image Annotation based on Spatial Relationship Feature Extraction Mohd Nizam Saad, Mohamad Farhan Mohamad Mohsin, Hamzaini Abdul Hamid and Zurina Muda Abstract: Digital imaging has become an essential element in every medical institution. Therefore, medical image retrieval such as chest X-ray (CXR) must be improved via novel feature extraction and annotation activities before they are stored into image databases. To date, many methods have been introduced to annotate medical images using spatial relationships after these features are extracted. However, the annotation performance for each method is inconsistent and does not show promising achievement to retrieve images. It is noticed that each method is still struggling with at least two big problems. Firstly, the recommended annotation model is weak because the method does not consider the object shape and rely on gross object shape estimation. Secondly, the suggested annotation model can only be functional for simple object placement. As a result, it is difficult to determine the spatial relationship feature after they are extracted to annotate images accurately. Hence, this study aims to propose a new model to annotate nodule location within lung zone for CXR image with extracted spatial relationship feature to improve image retrieval. In order to achieve the aim, a methodology that consists of six phases of CXR image annotation using the extracted spatial relationship features is introduced. This comprehensive methodology covers all cycles for image annotation tasks starting from image pre-processing until determination of spatial relationship features for the lung zone in the CXR. The outcome from applying the methodology also enables us to produce a new semi-automatic annotation system named CHEXRIARS which acts as a tool to annotate the extracted spatial relationship features in CXR images. The CHEXRIARS performance is tested using a retrieval test with two common tests namely the precision and recall (PNR). Apart from CHEXRIARS, three other annotation methods that are object slope, object projection and comparison of region boundaries are also included in the retrieval performance test. Overall, the CHEXRIARS interpolated PNR curve shows the best shape because it is the closest curve approaching the value of 1 on the X-axis and Y-axis. Meanwhile the value of area under curve for CHEXRIARS also revealed that this system attained the highest score at 0.856 as compared to the other three annotation methods. The outcome from the retrieval performance test indicated that the proposed annotation model has produced outstanding outcome and improved the image retrieval. Keywords: English Language Examination; K-Nearest Neighbour; MUET; Prediction. |
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