Adaptive System Identification using Markov Chain Monte Carlo

One of the major problems in adaptive filtering is the problem of system identification. It has been studied extensively due to its immense practical importance in a variety of fields. The underlying goal is to identify the impulse response of an unknown system. This is accomplished by placing a known system in parallel and feeding both systems with the same input. Due to initial disparity in their impulse responses, an error is generated between their outputs. This error is set to tune the impulse response of known system in a way that every change in impulse response reduces the magnitude of prospective error. This process is repeated until the error becomes negligible and the responses of both systems match. To specifically minimize the error, numerous adaptive algorithms are available. They are noteworthy either for their low computational complexity or high convergence speed. Recently, a method, known as Markov Chain Monte Carlo (MCMC), has gained much attention due to its remarkably low computational complexity. But despite this colossal advantage, properties of MCMC method have not been investigated for adaptive system identification problem. This article bridges this gap by providing a complete treatment of MCMC method in the aforementioned context

Learning style preference and critical thinking perception among engineering students

Engineering education plays a vital role towards modernization of world. Therefore, engineering students need to be nurture with multiple skills like learning preferences and critical thinking skills. This study has been conducted to identify the learning style preferences and critical thinking perception of the engineering students from three programs electrical engineering, mechanical engineering and civil engineering at Universiti Tun Hussein Onn Malaysia (UTHM), Johor. Survey research design was applied in this study. The quantitative data was collected by two questionnaires Index of Learning Styles (ILS) that is based on Felder-Silverman Learning Style Model (FSLSM) and Critical Thinking Skills (CTS) questionnaire which consists of analysis, evaluation, induction and deduction in terms of problem solving and decision making. A total of 315 final year engineering students were participated in this study. Data was analyzed in descriptive and inferential statistics involving tests Analysis of Variance (ANOVA), Pearson Correlation and linear regression. The study discovered that engineering students are preferred to be visual learners (83.80%). Visual learning style denotes FSLSM input dimension and visual learners learn best by diagrams, charts, maps and graphical presentations. This study also found that engineering students possess critical thinking perception in all dimensions. However, there is no statistical significant difference of learning style found among engineering programs as “p” value found 0.357. Whereas, there is statistical significant critical thinking difference found among engineering programs as “p” value found 0.006. Lastly, findings revealed that there is no significant relationship found between learning styles and critical thinking skills. The study findings suggested that providing preferred learning style (visual learning style) in classroom will enhance students’ academic achievement and increase their cognitive level. This study might serve as a guideline for educators to facilitate learners to enhance their learning and thinking for better outcomes in academia as well as in workplace

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

A New Approach to Linear Estimation Problem in Multi-user Massive MIMO Systems

A novel approach for solving linear estimation problem in multi-user massive MIMO systems is proposed. In this approach, the difficulty of matrix inversion is attributed to the incomplete definition of the dot product. The general definition of dot product implies that the columns of channel matrix are always orthogonal whereas, in practice, they may be not. If the latter information can be incorporated into dot product, then the unknowns can be directly computed from projections without inverting the channel matrix. By doing so, the proposed method is able to achieve an exact solution with a 25% reduction in computational complexity as compared to the QR method. Proposed method is stable, offers an extra flexibility of computing any single unknown, and can be implemented in just twelve lines of code

Hypotensive, vaso-relaxant, cardio-depressant and diuretic effect of crude extract of Crotalaria burhia (Fabaceae)

Purpose: To examine 70 % aqueous-methanol crude extract of Crotalaria burhia (Cb.Cr) for its hypotensive and diuretic effects. Methods: The effect of intravenous administration of Cb.Cr on blood pressure (BP) of normotensive anesthetized rats was studied. In vitro experiments on rabbit isolated aortic and atrial preparations were performed to elucidate the mechanism of action. The diuretic effect was assessed following oral administration of Cb.Cr in rats. Results: Intravenous administration of Cb.Cr produced 14.70 ± 1.21, 22.00 ± 2.24 and 36.21 ± 2.65 % reduction in mean arterial blood pressure of the rats at doses of 1, 3 and 10 mg/kg, respectively. It was more potent in relaxing potassium (80 mM)- than phenylephrine (1 μM)-induced contractions in isolated aorta of rabbit with half-maximal effective concentration (EC50) values of 0.58 ± 0.03 and 1.58 ± 0.16 mg/mL, respectively, which are similar to verapamil. The extract showed depressant effects on spontaneously beating atrial preparations of rabbit in a dose-dependent manner. Moreover, Cb.Cr also increased urine volume and urinary electrolyte excretion in rats. Conclusion: Crotalaria burhia crude extract exhibits hypotensive and diuretic effects in rats. The hypotensive activity of the extract possibly involves vasodilator, cardio-depressant, calcium channel blocking and diuretic actions. Keywords: Khip, Crotalaria burhia, Calcium channel blocker, Antihypertensive, Diureti

Profitability as the Determinant of Soft Environmental Disclosures

The profitability has been studied as the determinant of soft environmental disclosures (SED) first time in Pakistan. The Cement, Steel and Power generation sectors of Pakistan have been destroying the environment more as compared to other industries. This study finds the results of the sample of three years from 2015-2017. The Quantile regression is applied to find the impact of SED. Now the policymakers can look into the results at different quantiles. The quantile regression has been applied on 0.25 percentile, 0.50 percentile, and 0.75 percentile. The empirical results show that at every quantile the profitability is the significant positive determinant of SED