Engineering

—  Pemanfaatan metode komputasi bertingkat (nested computation) Metris pada bidang pemrograman aritmetika khususnya perkalian masih sangat baru. Dalam metode komputasi bertingkat Metris ini akan diperkenalkan suatu fungsi baru yaitu fungsi pagar Metris. Basis bilangan yang bekerja pada perhitungan menggunakan Metris ini tidak hanya bilangan desimal namun juga dapat bilangan biner, oktadesimal maupun hexadesimal. Sehingga nantinya dalam penelitian selanjutnya dapat dimanfaatkan sebagai metode pemrograman pada sistem mikroprosesor.

There has been a growing research interest in brain tumor classification based on proton magnetic resonance spectroscopy (Math EqH MRS) signals. Four research centers within the EU funded INTERPRET project have acquired a significant number of long echo Math EqH MRS signals for brain tumor classification. In this paper, we present an objective comparison of several classification techniques applied to the discrimination of four types of brain tumors: meningiomas, glioblastomas, astrocytomas grade II and metastases. Linear and non-linear classifiers are compared: linear discriminant analysis (LDA), support vector machines (SVM) and least squares SVM (LS-SVM) with a linear kernel as linear techniques and LS-SVM with a radial basis function (RBF) kernel as a non-linear technique. Kernel-based methods can perform well in processing high dimensional data. This motivates the inclusion of SVM and LS-SVM in this study. The analysis includes optimal input variable selection, (hyper-) parameter estimation, followed by performance evaluation. The classification performance is evaluated over 200 stratified random samplings of the dataset into training and test sets. Receiver operating characteristic (ROC) curve analysis measures the performance of binary classification, while for multiclass classification, we consider the accuracy as performance measure. Based on the complete magnitude spectra, automated binary classifiers are able to reach an area under the ROC curve (AUC) of more than 0.9 except for the hard case glioblastomas versus metastases. Although, based on the available long echo Math EqH MRS data, we did not find any statistically significant difference between the performances of LDA and the kernel-based methods, the latter have the strength that no dimensionality reduction is required to obtain such a high performance.

ABSTRACT For the prediction of nonlinear time series, weighted least squares support vector machine (WLS-SVM) local region method is proposed in this paper. The method has the following two advantages. First, the WLS-SVM can obtain robust estimates for regression through the limited observation, and in the WLS-SVM framework, there is a simple and efficient approach to model parameters selection based on leave-one-out cross-validation. Second, considering the estimate of the given point, using all samples is unnecessary. Training a segment of samples, which are familiar with the given point, can achieve high quality precise. Our method has been tried for prediction on two synthetic and the neuronal data sets. The results show that the method has more superior performance than other methods like LS-SVM.

In least squares support vector machines (LS-SVM's) for function estimation Vapnik's €-insensitive loss func-tion has been replaced by a cost function which corre-sponds to a form of ridge regression. In this way nonlin-ear function estimation is done by solving a linear ...

Research supported by Research Council KULeuven: GOA-Mefisto 666, IDO (IOTA oncology, genetic networks), several PhD/postdoc & fellow grants; Flemish Govern-ment: FWO: PhD/postdoc grants, G.0407.02 (support vector machines), projects G.0115.01 ( ...

Classification problems have arisen in many applications, attracting many researches to develop advanced classifier techniques. A method called Support Vector Machines (SVM) for pattern recognition and function estimation has been introduced by Vapnik (1995) in the ...

Time-series prediction has been a very well researched topic in
recent studies. Some popular approaches to this problem are the traditional statistical methods e.g. multiple linear regression and moving average, and neural network with the Multi Layer Perceptron which has shown its supremacy in time-series prediction. In this study, we used a different approach based on
evolving clustering algorithm with polynomial regressions to find repeating local patterns in a time-series data. To illustrate chaotic time-series data we have taken into account the use of stock price data from Indonesian stock exchange market and currency exchange rate data. In addition, we have also
conducted a benchmark test using the Mackey Glass data set. Results showed that the algorithm offers a considerably high accuracy in time-series prediction and could also reveal repeating patterns of movement from the past.

The needs of monitoring the water level of troughs is increasing. This is parallel with the growing of Wireless Sensor
Network and Internet of Thing. By combining both approach,
cattlemen can monitoring their troughs ubiquitous using their
own personal device. This paper develop such system by using
LoRaTM as the media between sensor hub and nodes, while
Raspberry Pi is used as the gateway to push data into server. We have successfully tested the functionality of node to hub join mechanism, where a node should join to the closest hub, which marked by the highest RSSI of beacon signal. The designed system is able to wirelessly sense trough water level up to 50 simulated nodes.