Analisa tekstur adalah satu sifat penting untuk mengenal pasti permukaan dan objek daripada imej perubatan dan pelbagai imej lain. Penyelidikan ini telah membangunkan sebuah algoritma untuk menganalisa tekstur dengan menggunakan imej perubatan dari echocardiography untuk mengenal pasti jantung yang disyaki mengalami myocardial infarction. Di sini penggabungan daripada teknik wavelet extension transform dan teknik gray level co–occurrence matrix adalah dicadangkan. Di dalam penyelidikan ini wavelet extension transform digunakan untuk menghasilkan sebuah imej hampiran yang mempunyai resolusi yang lebih besar. Gray level co–occurrence matrix yang dihitung untuk setiap sub–band digunakan untuk mencirikan empat sifat vektor: entropy, contrast, energy (angular second moment) dan homogeneity (invers difference moment). Pengklasifikasian yang digunakan di dalam penyelidikan ini adalah pengklasifikasian Mahalanobis distance. Kaedah yang telah dicadangkan diuji dengan data klinikal dari imej echocardiography untuk 17 orang pesakit. Untuk setiap pesakit, contoh tisu diambil daripada kawasan yang disyaki infarcted dan kawasan non–infarcted (normal). Untuk setiap pesakit, 8 bingkai imej yang dipisahkan oleh sela waktu tertentu di mana 5 kawasan normal dan 5 kawasan disyaki myocardial infarction berukuran 16×16 piksel akan dianalisa. Hasil pengklasifikasian telah dicapai dengan ketepatan 91.32%. Kata kunci: Analisa tekstur, wavelet extension, co–occurrence matrix, myocardial infarction, sifat vektor Texture analysis is an important characteristic for surface and object identification from medical images and many other types of images. This research has developed an algorithm for texture analysis using medical images do trained from echocardiography in identifying heart with suspected myocardial infarction problem. A set of combination of wavelet extension transform with gray level co–occurrence matrix is proposed. In this work, wavelet extension transform is used to form an image approximation with higher resolution. The gray level co–occurrence matrices computed for each subband are used to extract four feature vectors: entropy, contrast, energy (angular second moment) and homogeneity (inverse difference moment). The classifier used in this work is the Mahalanobis distance classifier. The method is tested with clinical data from echocardiography images of 17 patients. For each patient, tissue samples are taken from suspected infarcted area as well as from non–infarcted (normal) area. For each patient, 8 frames separated by some time interval are used and for each frame, 5 normal regions and 5 suspected myocardial infarction regions of 16×16 pixel size are analyzed. The classification performance achieved 91.32% accuracy. Key words: Texture analysis, wavelet extension, co–occurrence matrix, myocardial infarction, feature vector

Recently the number of accidents and crimes are arising in many big cities. Mobile applications with location aware systems can be utilized in providing location information of the crime. The problem is that location information provided by mobile phones has not been fully utilized. Mostly mobile phones used for its main functions which are for calling and messaging. This paper argues that location information can be used for reporting accident and crime, so that a quick and fast response can be achieved. This study proposes a framework of mobile crime information assistance to help the users (victim) from locus delicti (location where accident or crime happened) with location aware capabilities. Using this framework, mobile app can send and receive location of crimes including the scenes (images) to the nearest police station or central police station. The system can offer a facility to track the location of the nearest police station by accessing a built-in database with a combination of Google map APIs and send the scene/image of the crime location as one complete set of report to the police station. This will allow the police to find the location of the accident right away and increase the safety of the resident in big cities.

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Pengklasifikasian embrio telur menjadi sangat penting dalam proses penetasan untuk membedakan antara yang layak; berdasarkan adanya perkembangan embrio yang dapat dilanjutkan dalam proses inkubasi atau tidak layak (fertil atau infertil). Penelitian ini menyajikan deteksi dengan teknik pengolahan citra digital dan menggunakan komputer kecil yaitu Raspberry Pi sebagai pemroses gambar dan menampilkan hasil klasifikasi. Dengan metode dan penggunaan Raspberry Pi akurasi pendeteksian mencapai 90% dan 95%.