Annie anak Joseph

Online feature extraction based on accelerated kernel principal component analysis for data stream

Abstract Kernel principal component analysis (KPCA) is known as a nonlinear feature extraction method. Takeuchi et al. have proposed an incremental type of KPCA (IKPCA) that can update an eigen-space incrementally for a sequence of data. However, in IKPCA, the eigenvalue decomposition should be carried out for every single data, even though a chunk of data is given at one time. To reduce the computational costs in learning chunk data, this paper proposes an extended IKPCA called Chunk IKPCA (CIKPCA) where a chunk of multiple data is learned with single eigenvalue decomposition. For a large data chunk, to reduce further computation time and memory usage, it is first divided into several smaller chunks, and only useful data are selected based on the accumulation ratio. In the proposed CIKPCA, a small set of independent data are first selected from a reduced set of data so that eigenvectors in a high-dimensional feature space can be represented as a linear combination of such independent data. Then, the eigenvectors are incrementally updated by keeping only an eigenspace model that consists of the sextuplet such as independent data, coefficients, eigenvalues, and mean information. The proposed CIKPCA can augment an eigen-feature space based on the accumulation ratio that can also be updated without keeping all the past data, and the eigen-feature space is rotated by solving an eigenvalue problem once for each data chunk. The experiment results show that the learning time of the proposed CIKPCA is greatly reduced as compared with KPCA and IKPCA without sacrificing recognition accuracy.

Electromagnetic radiation towards adult human head from 900MHz handheld mobile phone

Electromagnetic radiation produce by mobile phone and the relationship with the humans health is not a new issue nowadays. Since the used of mobile phone had increased rapidly over the past few years, people are becoming more concern with their health when dealing with the so-called electromagnetic radiation. This type of radiation would leads to heating of body tissue at specific rate called the thermal radiation. Thermal radiation depends on the frequency of the energy, the power density of the radio frequency field that strikes the body and the polarization of wave. This paper will discuss on the result collected from the thermal radiation generated by handheld mobile phone with frequency of 900 MHz towards adult human head. The analysis is conducted in a laboratory with average of 45 minutes talking hour with two different types of mobile phone, internal and external antenna. The results show an increased of heat especially at the place near the ear skull after 45 minutes of operation. When comparing both different types of mobile phone, mobile phone with external antenna produce more heat compared to mobile phone with internal antenna.

Visualization and Analytical Measurement of Electromagnetic Radiation from Handheld Mobile Phones

Mobile communication is where signal is transferred via electromagnetic wave through radio frequency and microwave signals. This signal produced electromagnetic radiation in the form of thermal radiation that consists of harmful ionizing radiation and harmless non-ionizing radiation. When using mobile phone, electromagnetic wave is transferred to the body in the form of thermal radiation that might cause health problems especially at the place near ear skull region for a certain period of time (talking time or operation time). In this analysis, the simulation and experimental analysis are carried out to examine the effect of electromagnetic radiation towards design phantom (dry phantom), water molecules and human volunteer when using the mobile phone for 45 minutes talking hour. This analysis is conducted in an anechoic chamber. Simulation is done using MATLAB’s software to show the radiation produced by the mobile phone. In the experimental analysis, thermal imaging technique is used to monitor and capture temperature distribution towards design phantom and human head. Two different design of mobile phone are used with internal (built-in) and external monopole antenna serving two different GSM frequencies GSM 900 and GSM 1800. The results show that mobile phones produced electromagnetic radiation (thermal) towards human head with high radiation produced by mobile phone with external antenna serving for GSM 900 MHz.

A fast Incremental Kernel Principal Component Analysis for data streams

Kernel Principal Component Analysis (KPCA) is widely used feature extraction as it have been proven that KPCA is powerful in many areas in pattern recognition. Considering that the conventional KPCA should decompose a kernel matrix of all training data, this would be an unrealistic assumption for data streams in real-world applications. Therefore, in this paper, we propose an online feature extraction called Chunk Incremental Kernel Principal Component Analysis (CIKPCA) that can handle data streams in an incremental mode. In the proposed method, the training data are assumed to be given in a chunk of multiple data at one time. In CIKPCA, an eigen-feature space is updated by solving the eigenvalue decomposition once whenever a chunk of data is given. However, if a chunk size is large, a kernel matrix to be decomposed is also large, resulting in high computational time. Considering that not all the data are useful for the eigen-feature space learning, the data in a chunk are first selected based on the importance. Several benchmark data sets in the UCI Machine Learning Repository are used to evaluate the performance of the proposed method. The experimental results show that our proposed method can accelerate the learning of the eigen-feature space compared to Takeuchi et al.s IKPCA without reducing the recognition accuracy.

A Review on the Extraction of Quantitative Retinal Microvascular Image Feature

Digital image processing is one of the most widely used computer vision technologies in biomedical engineering. In the present modern ophthalmological practice, biomarkers analysis through digital fundus image processing analysis greatly contributes to vision science. This further facilitates developments in medical imaging, enabling this robust technology to attain extensive scopes in biomedical engineering platform. Various diagnostic techniques are used to analyze retinal microvasculature image to enable geometric features measurements such as vessel tortuosity, branching angles, branching coefficient, vessel diameter, and fractal dimension. These extracted markers or characterized fundus digital image features provide insights and relates quantitative retinal vascular topography abnormalities to various pathologies such as diabetic retinopathy, macular degeneration, hypertensive retinopathy, transient ischemic attack, neovascular glaucoma, and cardiovascular diseases. Apart from that, this noninvasive research tool is automated, allowing it to be used in large-scale screening programs, and all are described in this present review paper. This paper will also review recent research on the image processing-based extraction techniques of the quantitative retinal microvascular feature. It mainly focuses on features associated with the early symptom of transient ischemic attack or sharp stroke.