Estimation of Terrestrial Water Storage Variations in Sichuan-Yunnan Region from GPS Observations Using Independent Component Analysis

GPS can be used to measure land motions induced by mass loading variations on the Earth’s surface. This paper presents an independent component analysis (ICA)-based inversion method that uses vertical GPS coordinate time series to estimate the change of terrestrial water storage (TWS) in the Sichuan-Yunnan region in China. The ICA method was applied to extract the hydrological deformation signals from the vertical coordinate time series of GPS stations in the Sichuan-Yunnan region from the Crustal Movement Observation Network of China (CMONC). These vertical deformation signals were then inverted to TWS variations. Comparative experiments were conducted based on Gravity Recovery and Climate Experiment (GRACE) data and a hydrological model for validation. The results demonstrate that the TWS changes estimated from GPS(ICA) deformations are highly correlated with the water variations derived from the GRACE data and hydrological model in Sichuan-Yunnan region. The TWS variations are overestimated by the vertical GPS observations the northwestern Sichuan-Yunnan region. The anomalies are likely caused by inaccurate atmospheric loading correction models or residual tropospheric errors in the region with high topographic variability and can be reduced by ICA preprocessing.

Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the quantitative EEG (qEEG) features can be used as new predictors for the prediction of recovery in moderate traumatic brain injury (TBI). However, the brain’s recorded electrical activity has always been contaminated with artifacts, which in turn further impede the subsequent processing steps. As a result, it is crucial to devise a strategy for meticulously flagging and extracting clean EEG data to retrieve high-quality discriminative features for successful model development. This work proposed the use of multiple artifact rejection algorithms (MARA), which is an independent component analysis (ICA)-based algorithm, to eliminate artifacts automatically, and explored their effects on the predictive performance of the random undersampling boosting (RUSBoost) model. Continuous EEG were acquired using 64 electrodes from 27 moderate TBI patients at four weeks to one-year post-accident. The MARA incorporates an artifact removal stage based on ICA prior to RUSBoost, SVM, DT, and k-NN classification. The area under the curve (AUC) of RUSBoost was higher in absolute power spectral density (PSD) in AUCδ = 0.75, AUC α = 0.73 and AUCθ = 0.71 bands than SVM, DT, and k-NN. The MARA has provided a good generalization performance of the RUSBoost prediction model.

BOTNET DETECTION USING INDEPENDENT COMPONENT ANALYSIS

Botnet is a significant cyber threat that continues to evolve. Botmasters continue to improve the security framework strategy for botnets to go undetected. Newer botnet source code runs attack detection every second, and each attack demonstrates the difficulty and robustness of monitoring the botnet. In the conventional network botnet detection model that uses signature-analysis, the patterns of a botnet concealment strategy such as encryption & polymorphic and the shift in structure from centralized to decentralized peer-to-peer structure, generate challenges. Behavior analysis seems to be a promising approach for solving these problems because it does not rely on analyzing the network traffic payload. Other than that, to predict novel types of botnet, a detection model should be developed. This study focuses on using flow-based behavior analysis to detect novel botnets, necessary due to the difficulties of detecting existing patterns in a botnet that continues to modify the signature in concealment strategy. This study also recommends introducing Independent Component Analysis (ICA) and data pre-processing standardization to increase data quality before classification. With and without ICA implementation, we compared the percentage of significant features. Through the experiment, we found that the results produced from ICA show significant improvements. The highest F-score was 83% for Neris bot. The average F-score for a novel botnet sample was 74%. Through the feature importance test, the feature importance increased from 22% to 27%, and the training model false positive rate also decreased from 1.8% to 1.7%. ABSTRAK: Botnet merupakan ancaman siber yang sentiasa berevolusi. Pemilik bot sentiasa memperbaharui strategi keselamatan bagi botnet agar tidak dapat dikesan. Setiap saat, kod-kod sumber baru botnet telah dikesan dan setiap serangan dilihat menunjukkan tahap kesukaran dan ketahanan dalam mengesan bot. Model pengesanan rangkaian botnet konvensional telah menggunakan analisis berdasarkan tanda pengenalan bagi mengatasi halangan besar dalam mengesan corak botnet tersembunyi seperti teknik penyulitan dan teknik polimorfik. Masalah ini lebih bertumpu pada perubahan struktur berpusat kepada struktur bukan berpusat seperti rangkaian rakan ke rakan (P2P). Analisis tingkah laku ini seperti sesuai bagi menyelesaikan masalah-masalah tersebut kerana ianya tidak bergantung kepada analisis rangkaian beban muatan trafik. Selain itu, bagi menjangka botnet baru, model pengesanan harus dibangunkan. Kajian ini bertumpu kepada penggunaan analisa tingkah-laku berdasarkan aliran bagi mengesan botnet baru yang sukar dikesan pada corak pengenalan botnet sedia-ada yang sentiasa berubah dan menggunakan strategi tersembunyi. Kajian ini juga mencadangkan penggunakan Analisis Komponen Bebas (ICA) dan pra-pemprosesan data yang standard bagi meningkatkan kualiti data sebelum pengelasan. Peratusan ciri-ciri penting telah dibandingkan dengan dan tanpa menggunakan ICA. Dapatan kajian melalui eksperimen menunjukkan dengan penggunaan ICA, keputusan adalah jauh lebih baik. Skor F tertinggi ialah 83% bagi bot Neris. Purata skor F bagi sampel botnet baru adalah 74%. Melalui ujian kepentingan ciri, kepentingan ciri meningkat dari 22% kepada 27%, dan kadar positif model latihan palsu juga berkurangan dari 1.8% kepada 1.7%.

Doppler flow phantom failure detection by combining empirical mode decomposition and independent component analysis with short time Fourier transform

<p class="Abstract">Nowadays, objective protocols and criteria for the monitoring of phantoms failures are still lacking in literature, despite their technical limitations. In such a context, the present work aims at providing an improvement of a previously proposed method for the Doppler flow phantom failures detection. Such failures were classified as low frequency oscillations, high velocity pulses and velocity drifts. The novel objective method, named EMoDICA-STFT, is based on the combined application of the Empirical Mode Decomposition (EMD), Independent Component Analysis (ICA) and Short Time Fourier Transform (STFT) techniques on Pulsed Wave (PW) Doppler spectrograms. After a first series of simulations and the determination of adaptive thresholds, phantom failures were detected on real PW spectrograms through the EMoDICA-STFT method. Data were acquired from two flow phantom models set at five flow regimes, through a single ultrasound (US) diagnostic system equipped with a linear, a convex and a phased array probe, as well as with two configuration settings. Despite the promising outcomes, further studies should be carried out on a greater number of Doppler phantoms and US systems as well as including an in-depth investigation of the proposed method uncertainty.</p>