Frequency and time profiles of metric wave isolated Type I solar noise storm bursts at high spectral and temporal resolution

Type I noise storms constitute a sizeable faction of the active-Sun radio emission component. Observations of isolated instances of such bursts, in the swept-frequency-mode at metric wavelengths, have remained sparse, with several unfilled regions in the frequency coverage. Dynamic spectra of the burst radiation, in the 30 - 130 MHz band, obtained from the recently commissioned digital High Resolution Spectrograph (HRS) at the Gauribidanur Radio Observatory, on account of the superior frequency and time resolution, have unravelled in explicit detail the temporal and spectral profiles of isolated bursts. Apart from presenting details on their fundamental emission features, the time and frequency profile symmetry, with reference to custom-specific Gaussian distributions, has been chosen as the nodal criterion to statistically explain the state of the source regions in the vicinity of magnetic reconnections, the latent excitation agent that contributes to plasma wave energetics, and the quenching phenomenon that causes damping of the burst emission.Comment: 9 pages 7 black and white / grey-scale figures (inclusive of 3 composite). MNRAS - accepte

Vowel recognition using Kohonen\u27s self-organizing feature maps

An important organizing principle observed in the sensory pathways in the brain is the orderly placement of neurons. Although the neurons are structurally identical, the specialized role played by each unit is determined by its internal parameters that are made to change during early learning processes. In the human auditory system, the nerve cells and fibres are arranged in a manner that would elicit maximum response from the neurons when they are activated. Although most of this organization is genetically determined, some of the high level organization is created due to algorithms that promote self-organization. Kohonen\u27s self-organizing feature map is a neural net model that produces feature maps similar to the ones produced in the brain. These maps are capable of describing topological relationships of input signals using a one or two dimensional representation. This technique uses unlabeled data and requires no training as in supervised learning algorithms. It is hence immensely useful in speech and vision applications. This neutral net has been implemented for the recognition of vowels in the American English language. The net has been trained and tested with vowel data. The formation of internal clusters or categories has been observed and closely reflects the tonotopic relationships between the vowels. An analysis of the results has been carried out and the performance has been compared to other classification techniques. A graphical user interface has also been developed using Xview to help visualize the formation of the maps during the training and testing processes

Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval

Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm.  This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-art-works

A Simple Model for Pricing Securities with Equity, Interest-Rate, and Default Risk

We develop a model for pricing derivative and hybrid securities whose value may depend on different sources of risk, namely, equity, interest-rate, and default risks. In addition to valuing such securities the framework is also useful for extracting probabilities of default (PD) functions from market data. Our model is not based on the stochastic process for the value of the firm [which is unobservable], but on the stochastic process for interest rates and the equity price, which are observable. The model comprises a risk-neutral setting in which the joint process of interest rates and equity are modeled together with the default conditions for security payoffs. The model is embedded on a recombining lattice which makes implementation of the pricing scheme feasible with polynomial complexity. We present a simple approach to calibration of the model to market observable data. The framework is shown to nest many familiar models as special cases. The model is extensible to handling correlated default risk and may be used to value distressed convertible bonds, debt-equity swaps, and credit portfolio products such as CDOs. We present several numerical and calibration examples to demonstrate the applicability and implementation of our approach

Convolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter

Owing to the use of nonlinear loads in the distribution side, there are power quality issues such as voltage swell/sag, harmonics, flickers, voltage imbalance, and outage. The harmonics in power system affect the quality of power and hence a suitable methodology is vital to mitigate the harmonics and compensation of reactive power. In this paper, CNN (Convolutional Neural Network)-based harmonic mitigation is performed. A 5-level cascaded H-bridge inverter is employed as a shunt active filter in which the reference current is generated by the SRF theory, incorporating CNN for harmonic extraction. The DC-link potential across capacitor is retained by means of ANN (Artificial Neural Network) controller whose behaviour is compared with a proportional controller as well as FLC. The gating pulse for the cascaded inverter is generated by means of PWM generator incorporated with Hysteresis Current Controller (HCC). By this control strategy, the harmonics in the current and voltage get mitigated; subsequently, the reactive power compensation is achieved with unity power factor. By implementing the five-level inverter, the THD and the settling time are minimized. The performance of the system is analysed using MATLAB for nonlinear load and the hardware is implemented with FPGA Spartan 6E. The THD of 0.93% is accomplished in simulation and 1.4% in the hardware execution

A Direct Approach to Arbitrage-Free Pricing of Credit Derivatives

This paper develops a model for the pricing of credit derivatives using observables. The model (i) is arbitrage-free, (ii) accommodates path-dependence, and (iii) handles a range of securities, even with American features. The computer implementation uses a recursive scheme that is convenient and seamlessly processes forward induction and backward recursion, needed to compute more complicated derivative securities.

Development of algorithms for determining heart failure with reduced and preserved ejection fraction using nationwide electronic healthcare records in the UK

Background: Determining heart failure (HF) phenotypes in routine electronic health records (EHR) is challenging. We aimed to develop and validate EHR algorithms for identification of specific HF phenotypes, using Read codes in combination with selected patient characteristics. Methods: We used The Healthcare Improvement Network (THIN). The study population included a random sample of individuals with HF diagnostic codes (HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF) and non-specific HF) selected from all participants registered in the THIN database between 1 January 2015 and 30 September 2017. Confirmed diagnoses were determined in a randomly selected subgroup of 500 patients via GP questionnaires including a review of all available cardiovascular investigations. Confirmed diagnoses of HFrEF and HFpEF were based on four criteria. Based on these data, we calculated a positive predictive value (PPV) of predefined algorithms which consisted of a combination of Read codes and additional information such as echocardiogram results and HF medication records. Results: The final cohort from which we drew the 500 patient random sample consisted of 10 275 patients. Response rate to the questionnaire was 77.2%. A small proportion (18%) of the overall HF patient population were coded with specific HF phenotype Read codes. For HFrEF, algorithms achieving over 80% PPV included definite, possible or non-specific HF HFrEF codes when combined with at least two of the drugs used to treat HFrEF. Only in non-specific HF coding did the use of three drugs (rather than two) contribute to an improvement of the PPV for HFrEF. HFpEF was only accurately defined with specific codes. In the absence of specific coding for HFpEF, the PPV was consistently below 50%. Conclusions: Prescription for HF medication can reliably be used to find HFrEF patients in the UK, even in the absence of a specific Read code for HFrEF. Algorithms using non-specific coding could not reliably find HFpEF patients

Unusual heavy landings of Tachysurus dussumieri by dol net at Arnala

Arnala is one of the major landing centres of Maharashtra for dol netters comprising catch of Bombay duck, Coilia and nonpenaeid prawns.The operation is restricted to a depth of about 40 m and the catch is brought ashore after everyone or two hauls. The fishermen had actually set out for pomfret fishing and an unusual catch of the catfish(Tachysurus dussumieri) which when examined were found to be migratory