Classification of colon biopsy samples by spatial analysis of a single spectral band from its hyperspectral cube

The histopathological analysis of colon biopsy samples is a very important part of screening for colorectal cancer. There is, however, significant inter-observer and even intra-observer variability in the results of such analysis due to its very subjective nature. Therefore, quantitative methods are required for the analysis of histopathological images to aid the histopatholgists in their diagnosis. In this paper, we exploit the shape and structure of the gland nuclei cells for the classification of colon biopsy samples using two-dimensional principal component analysis (2DPCA) and Support Vector Machine (SVM). We conclude that the use of textural features extracted from non-overlapping blocks of the histopathological images results in a non-linear decision boundary which can be efficiently exploited using a SVM with appropriate choice of parameters for its Gaussian kernel. The SVM classifier outperforms all the remaining methods by a clear margin

Wavelet based segmentation of hyperspectral colon tissue imagery

Segmentation is an early stage for the automated classification of tissue cells between normal and malignant types. We present an algorithm for unsupervised segmentation of images of hyperspectral human colon tissue cells into their constituent parts by exploiting the spatial relationship between these constituent parts. This is done by employing a modification of the conventional wavelet based texture analysis, on the projection of hyperspectral image data in the first principal component direction. Results show that our algorithm is comparable to other more computationally intensive methods which exploit the spectral characteristics of the hyperspectral imagery data

Model based optimal bit allocation

Modeling of the operational rate-distortion characteristics of a signal can significantly reduce the computational complexity of an optimal bit allocation algorithm. In this report, such models are studied

Comparative analysis of spatial and transform domain methods for meningioma subtype classification

Pattern recognition in histopathological image analysis requires new techniques and methods. Various techniques have been presented and some state of the art techniques have been applied to complex textural data in histological images. In this paper, we compare the novel Adaptive Discriminant Wavelet Packet Transform (ADWPT) with a few prominent techniques in texture analysis namely Local Binary Patterns (LBP), Grey Level Co-occurrence Matrices (GLCMs) and Gabor Transforms. We show that ADWPT is a better technique for Meningioma subtype classification and produces classification accuracies of as high as 90%

PENGARUH KEGIATAN EKSTRAKURIKULER DAN MOTIVASI BELAJAR SISWA TERHADAP PRESTASI BELAJAR SISWA PADA PROGRAM KOMPETENSI KEAHLIAN TEKNIK INSTALASI TENAGA LISTRIK DI SMK NEGERI 2 WONOSARI

Faktor yang mempengaruhi prestasi belajar siswa antara lain kegiatan ekstrakurikuler dan motivasi belajar. Penelitian ini bertujuan untuk mengetahui pengaruh kegiatan ekstrakurikuler siswa dan motivasi belajar siswa terhadap prestasi belajar siswa pada Program Kompetensi Keahlian Teknik Instalasi Tenaga Listrik di SMK Negeri 2 Wonosari. Penelitian ini merupakan penelitian ex post facto dan populasi, dengan pendekatan kuantitatif. Penelitian dilakukan di SMK Negeri 2 Wonosari dan respondennya adalah siswa kelas X dan XI Program Kompetensi Keahlian Teknik Instalasi Tenaga Listrik di SMK Negeri 2 Wonosari Tahun Ajaran 2011/2012 yang berjumlah 127 siswa. Variabel dalam penelitian ini yaitu kegiatan ekstrakurikuler (X1) dan motivasi belajar (X2) sebagai variabel bebas dan prestasi belajar (Y) sebagai variabel terikatnya. Pengumpulan data untuk variabel kegiatan ekstrakurikuler dan motivasi belajar menggunakan metode angket dengan skala Likert, sedangkan untuk variabel prestasi belajar dengan metode dokumentasi yang dikuantitatifkan. Pengujian persyaratan analisis meliputi uji normalitas, uji heteroskedastisitas dan uji multikolinieritas. Teknik analisis data yang digunakan adalah analisis deskriptif untuk menghitung harga mean, median, modus, dan simpangan baku. Uji hipotesis menggunakan analisis regresi linier sederhana dan berganda. Hasil penelitian menunjukkan bahwa: (1) antara kegiatan ekstrakurikuler terhadap prestasi belajar tidak terdapat pengaruh yang signifikan, dengan kontribusi 0,6% dan sisanya 99,4% ditentukan oleh variabel lain, (2) antara motivasi belajar terhadap prestasi belajar terdapat pengaruh yang signifikan, dengan kontribusi 5,2% dan sisanya 94,8% ditentukan oleh variabel lain, (3) antara kegiatan ekstrakurikuler dan motivasi belajar terhadap prestasi belajar terdapat pengaruh yang signifikan, dengan kontribusi 5,5% dan sisanya 94,5% ditentukan oleh variabel lain

Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Intelligent modelling and control with fatigue reduction for fes induced knee joint of hemiplegic for rehabilitation

Functional electrical stimulation (FES) is one of the treatment for the people with stroke such as hemiplegic body (half body paralysed) by applying small charges of electricity to the muscle to induce the movement. FES can be applied during rehabilitation stage to enhance the healing process. The development of the intelligent hemiplegic model of the knee joint and control strategies with fatigue reduction for the FES control application are the main concern of this thesis. Modelling the musculoskeletal is significantly challenging due to the complexity of the system. Development of the knee joint model that is capable of relating FES parameters is the first aim of this study. The knee joint model comprising of equations of motion to represent the segmental dynamics and PSO optimised Neural Network - ARX to represent quadriceps muscle properties was formulated. The results show that the muscle model developed gives an accurate dynamic characterisation. Development of the FES-induced extension and flexion motions control is the second aim of this study. To control the motor function of muscle by using external devices such as FES is one of the crucial issues. High nonlinearity and rapid change of muscle properties due to fatigue are the major problems of the FES control system. PSO optimised Fuzzy Logic Control (FLC) has been proposed to handle this complex nonlinear system. A natural trajectory control strategy by using the proposed control system has been assessed. There are two control strategies; knee movement control with and without minimised electrical stimulation were developed. The control problem was to design a FLC such that the knee joint track the desired trajectory as closely as possible. Then, both control strategies were investigated in terms of muscle fatigue. Multi objective PSO optimised FLC was used to minimise the amount of electrical stimulation in order to reduce the muscle fatigue. This control strategy has shown up to 32.6% minimisation of the electrical stimulation in the simulation studies and 35.89 % reduction the muscle fatigue in the experimental work. Therefore, this control strategy can be applied as FES control system for the treatment in rehabilitation to enhance the healing process for the stroke subjects such as hemiplegic patients

HUBUNGAN ANTARA DUKUNGAN KELUARGA TERHADAP MOTIVASI LANSIA MENGHADIRI POSYANDU LANSIA

Latar belakang; Posyandu lansia merupakan bentuk peran serta masyarakat lansia dalam upaya bidang kesehatan untuk mencapai derajat kesehatan yang optimal serta kondisi menua yang sehat dan mandiri. Sehingga dalam pemanfaatannya diperlukan suatu motivasi yang mampu untuk menggerakkan diri lansia menghadiri posyandu lansia. Melalui dukungan keluarga yang baik diharapkan akan memunculkan motivasi lansia yang tinggi pula dalam menghadiri posyandu lansia. Tujuan; Penelitian ini bertujuan untuk mengetahui adakah hubungan antara dukungan keluarga terhadap motivasi lansia menghadiri posyandu lansia. Metode penelitian; Penelitian ini merupakan penelitian kuantitatif non experimental dengan studi korelasional dan pendekatan yang digunakan adalah desain cross sectional. Instrumen berupa kuesioner dukungan keluarga dan motivasi. Subyek penelitian adalah semua lansia usia 60-74 tahun di RW 3 Kelurahan Bulusan Kecamatan Banyumanik Semarang dengan sample sebanyak 40 orang. Untuk pengolahan dan analisa data uji Fisher Exact

Vortices and Flat Connections

At Bradlow's limit, the moduli space of Bogomol'nyi vortices on a compact Riemann surface of genus $g$ is determined. The K\"{a}hler form, and the volume of the moduli space is then computed. These results are compared with the corresponding results previously obtained for a general vortex moduli space.Comment: LaTex file, 6 page