Home Energy Management System and Internet of Things: Current Trends and Way Forward

Managing energy in the residential areas has becoming essential with the aim of cost saving, to realize a practical approach of home energy management system (HEMS) in the area of heterogeneous Internet-of-Thing (IoT) devices. The devices are currently developed in different standards and protocols. Integration of these devices in the same HEMS is an issue, and many systems were proposed to integrate them efficiently. However, implementing new systems will incur high capital cost. This work aims to conduct a review on recent HEMS studies towards achieving the same objectives: energy efficiency, energy saving, reduce energy cost, reduce peak to average ratio, and maximizing user's comfort. Potential research directions and discussion on current issues and challenges in HEMS implementation are also provided

Penuras terbitan Gaussian berorientasi untuk peruasan imej paru-paru radiograf mesin pegun dan mudah alih

Kaedah peruasan paru-paru tanpa seliaan adalah proses mandatori bagi membangunkan Sistem Dapatan Semula Imej Perubatan Berdasarkan Kandungan (CBMIRS) untuk imej sinar-x dada (CXR). Setakat ini, kajian berkenaan CXR bagi mesin mudah alih sangat terhad walhal ianya penting kerana pesakit yang tenat akan didiagnos menggunakan mesin mudah alih. Kajian ini membentangkan penyelesaian yang kukuh untuk peruasan paru-paru CXR bagi mesin pegun dan mudah alih, dengan kaedah automatik berasaskan penuras terbitan Gaussian dengan tujuh orientasi, digabungkan dengan teknik pengklusteran Fuzzy C-Means dan pengambangan untuk memperincikan kerangka paru-paru. Algoritma baru untuk menghasilkan nilai ambang secara automatik bagi setiap tindak balas Gaussian juga diperkenalkan. Algoritma ini digunakan untuk kedua-dua CXR PA dan AP daripada set data awam (JSRT) dan persendirian yang diperolehi daripada hospital kolaboratif. Dua blok pra-pemprosesan diperkenalkan untuk menyeragamkan imej dari mesin yang berbeza. Perbandingan dengan kajian terdahulu yang menggunakan set data JSRT menunjukkan kaedah kami menghasilkan keputusan yang memberangsangkan. Penilaian prestasi (ketepatan, F-skor, kepersisan, kepekaan dan kekhususan) bagi peruasan dari set data JSRT adalah lebih daripada 0.90, kecuali skor-bertindih (0.87). Nilai median skor-bertindih bagipangkalan data imej persendirian adalah 0.83 (mesin pegun) dan 0.75 (dari dua jenis mesin mudah alih). Algoritma ini juga pantas, dengan purata masa pelaksanaan 12.5s. Kaedah ini berupaya beroperasi tanpa penyeliaan, latihan atau pembelajaran untuk peruasan paru-paru bagi radiograf yang diambil dari mesin yang mempunyai piawaian berbeza, serta berupaya untuk digunakan dalam aplikasi CBMIRS

Sistem dapatan semula imej untuk aplikasi perubatan

Dapatan semula imej (DSI) adalah sistem pencarian imej yang menggunakan ciri-ciri tertentu atau konteks khusus dalam sesuatu imej. Dalam bidang perubatan, sistem DSI digunakan untuk menyediakan imej yang diperlukan secara tepat dan pantas kepada pakar perubatan. Proses itu biasanya berlaku pada dan ketika diagnosis dan rawatan penyakit dilakukan. Sistem dapatan semula yang awal dan masih digunakan dengan meluas dalam bidang perubatan adalah sistem DSI berdasarkan teks (TBIRS). TBIRS menggunakan kata kunci dalam konteks sesuatu imej dan ia memerlukan anotasi teks secara manual. Proses anotasi teks adalah tugas yang memerihkan lebih-lebih lagi jika melibatkan pangkalan data yang besar. Ini memungkinkan kebarangkalian berlakunya kesilapan manusia adalah tinggi. Untuk mengatasi masalah yang dinyatakan, sistem DSI berdasarkan kandungan (CBIRS) dengan pengindeksan automatik adalah dicadangkan. Kaedah ini melibatkan pemprosesan imej perubatan berdasarkan komputer yang menggunakan fitur visual imej seperti warna, bentuk dan tesktur. Namun begitu, umum mengetahui bahawa suatu algoritma tertentu dalam CBIRS adalah khusus untuk satu modaliti sahaja dan melibatkan bahagian yang tertentu. Ini ditambahkan pula bahawa CBIRS telah mengabaikan persepsi manusia dalam tugas menakrif sesuatu imej dan akibatnya, menyebabkan wujudnya masalah jurang semantik. Oleh itu, sistem DSI hibrid (HBIRS) yang menggabungkan kekuatan kedua-dua TBIRS dan CBIRS telah diperkenalkan bagi menangani masalah jurang semantik khususnya dan sekaligus memantapkan sistem DSI amnya. Satu kerangka sistem DSI yang cekap iaitu HBIRS juga telah dicadangkan. Walau bagaimanapun, kajian ini hanya melibatkan TBIRS dan CBIRS bagi aplikasi perubatan, dan prototaip TBIRS yang dikaji menggunakan imej X-Ray turut dicadangkan

Content Based Retrieval of Images with Consolidation from Chest X-Ray Databases

There are large amounts of digitized radiographs available with related patient pathology and medical history. Retrieval of archived images are useful for aiding diagnosis and to provide relevant evidence from previous cases, as well as a training mechanism for junior radiologists. Most diseases and abnormalities tend to appear at specific regions of the image; hence a retrieval system with local features becomes necessary. Medical image segmentation also plays an important role by automating the delineation of anatomical structures. Thus, the main motivation of this thesis is to develop a fully automated lung segmentation approach together with consolidation detection and classification, to be used in a content-based medical image retrieval (CBMIR) system to identify infection and fluid regions in CXR images. Developing a fully automated segmentation approach for a CBMIR system is a challenging task as chest radiography images from different machines produce different contrast and intensity levels, and are also subject to different patient positioning and image projection

Hybrid Medical Image Retrieval System For CT Brain Images

This work covers a combination of text- and content-based image retrieval techniques for medical applications. By combining THIR with CBIR, the images can also be indexed by their visual content and would be retrieved based on visual similarity. All medical images are associated with textual patient's metadata that stores valuable information and can be used to get specific results. For this reason, traditional text-based retrieval is still helpful and a combination of both the accuracy of the retrieved results. Hence, a system that integrates both methods is expected to be more efficient in retrieving those desired medical images

Efficient Block-based Matching for Content-based Retrieval of CT Head Images

An efficient image retrieval system for use in medical applications is proposed. The system addresses both global and local features and is able to perform similarity retrieval on the whole image as well as on local regions within the image. The retrieval algorithm is based on a simple grey level histogram (GLH) algorithm to capture the intensity distribution, and a novel block-based retrieval technique to localize the image. The algorithm is tested on a database of computed tomography (CT) images of head provided by a local hospital. Promising results are reported which pave the way for the integration of the block-based method with a more advanced feature extraction method

Comparison of Different Feature Extraction Techniques in Content-Based Image Retrieval for CT Brain Images

Content-based image retrieval (CBIR) system helps users retrieve relevant images based on their contents. A reliable content-based feature extraction technique is therefore required to effectively extract most of the information from the images. These important elements include texture, colour, intensity or shape of the object inside an image. CBIR, when used in medical applications, can help medical experts in their diagnosis such as retrieving similar kind of disease and patient's progress monitoring. In this paper, several feature extraction techniques are explored to see their effectiveness in retrieving medical images. The techniques are Gabor Transform, Discrete Wavelet Frame, Hu Moment Invariants, Fourier Descriptor, Gray Level Histogram and Gray Level Coherence Vector. Experiments are conducted on 3,032 CT images of human brain and promising results are reporte

Abnormality detection for infection and fluid cases in chest radiograph

This paper presents an automated abnormality detection system for infection and fluid cases in the lung field for chest radiograph. The abnormality features represented as abnormality scores are investigated based on the sharpness of costophrenic angle (Scoreθn), symmetrical lung area (ScoreLp), area of the lung (Scorearea), as well as the lung level (ScoreLlevel). The radiograph will be detected as abnormal if any of the score is `1'. Total numbers of classified normal and with disease radiographs are 177 and 35 respectively. From the results at the image level, 78% and 100% of the infection and fluid images are correctly detected as abnormal

Content-based medical image retrieval system for infections and fluids in chest radiographs

This paper presents a retrieval system based on the image’s content for the application in medical domain. This system is aimed to assist the radiologists in healthcare by providing pertinent supporting evidence from previous cases. It is also useful for the junior radiologists and medical students as teaching aid and training mechanism. The system is tested to retrieve the infections and fluid cases in chest radiographs. We explored several feature extraction techniques to see their effectiveness in describing the low-level property of the radiographs in our dataset. These features are Gabor transform, Discrete Wavelet Frame and Grey Level Histogram. The retrieval of these cases was also experimented with a number of distance metrics to observe their performances. Promising measures based on recognition rate are reported