Detecting gravitational waves from highly eccentric compact binaries

In dense stellar regions, highly eccentric binaries of black holes and neutron stars can form through various n-body interactions. Such a binary could emit a significant fraction of its binding energy in a sequence of largely isolated gravitational wave bursts prior to merger. Given expected black hole and neutron star masses, many such systems will emit these repeated bursts at frequencies within the sensitive band of contemporary ground-based gravitational wave detectors. Unfortunately, existing gravitational wave searches are ill-suited to detect these signals. In this work, we adapt a "power stacking" method to the detection of gravitational wave signals from highly eccentric binaries. We implement this method as an extension of the Q-transform, a projection onto a multiresolution basis of windowed complex exponentials that has previously been used to analyze data from the network of LIGO/Virgo detectors. Our method searches for excess power over an ensemble of time-frequency tiles. We characterize the performance of our method using Monte Carlo experiments with signals injected in simulated detector noise. Our results indicate that the power stacking method achieves substantially better sensitivity to eccentric binary signals than existing localized burst searches.Comment: 17 pages, 20 figure

Extraction of black hole coalescence waveforms from noisy data

We describe an independent analysis of LIGO data for black hole coalescence events. Gravitational wave strain waveforms are extracted directly from the data using a filtering method that exploits the observed or expected time-dependent frequency content. Statistical analysis of residual noise, after filtering out spectral peaks (and considering finite bandwidth), shows no evidence of non-Gaussian behaviour. There is also no evidence of anomalous causal correlation between noise signals at the Hanford and Livingston sites. The extracted waveforms are consistent with black hole coalescence template waveforms provided by LIGO. Simulated events, with known signals injected into real noise, are used to determine uncertainties due to residual noise and demonstrate that our results are unbiased. Conceptual and numerical differences between our RMS signal-to-noise ratios (SNRs) and the published matched-filter detection SNRs are discussed.Comment: 15 pages, 11 figures. Version accepted for publicatio

Wavelet transform - artificial neural network receiver with adaptive equalisation for a diffuse indoor optical wireless OOK link

This paper presents an alternative approach for signal detection and equalization using the continuous wavelet transform (CWT) and the artificial neural network (ANN) in diffuse indoor optical wireless links (OWL). The wavelet analysis is used for signal preprocessing (feature extraction) and the ANN for signal detection. Traditional receiver architectures based on matched filter (MF) experience significant performance degradation in the presence of artificial light interference (ALI) and multipath induced intersymbol interference (ISI). The proposed receiver structure reduces the effect of ALI and ISI by selecting a particular scale of CWT that corresponds to the desired signal and classifying the signal into binary 1 and 0 based on an observation vector. By selecting particular scales corresponding to the signal, the effect of ALI is reduced. We show that there is little variation when using 30 and 5 neurons in the first layer, with one layer ANN model showing a consistently worse BER performance than other models, whilst the 15 neuron model show some behaviour anomalies from a BER of approximately 10-3. The simulation results show that the Wavelet-ANN architecture outperforms the traditional MF based receiver even with the filter is matched to the ISI affected pulse shape. The Wavelet-ANN receiver is also capable of providing a bit error rate (BER) performance comparable to the equalized forms of traditional receiver structure

Multiple bottlenecks sorting criterion at initial sequence in solving permutation flow shop scheduling problem

This paper proposes a heuristic that introduces the application of bottleneck-based concept at the beginning of an initial sequence determination with the objective of makespan minimization. Earlier studies found that the scheduling activity become complicated when dealing with machine, m greater than 2, known as non-deterministic polynomial-time hardness (NP-hard). To date, the Nawaz-Enscore-Ham (NEH) algorithm is still recognized as the best heuristic in solving makespan problem in scheduling environment. Thus, this study treated the NEH heuristic as the highest ranking and most suitable heuristic for evaluation purpose since it is the best performing heuristic in makespan minimization. This study used the bottleneck-based approach to identify the critical processing machine which led to high completion time. In this study, an experiment involving machines (m =4) and n-job (n = 6, 10, 15, 20) was simulated in Microsoft Excel Simple Programming to solve the permutation flowshop scheduling problem. The overall computational results demonstrated that the bottleneck machine M4 performed the best in minimizing the makespan for all data set of problems

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Analysis of variations in diesel engine idle vibration

The variations in diesel engine idle vibration caused by fuels of different composition and their contributions to the variations in steering wheel vibrations were assessed. The time-varying covariance method (TV-AutoCov) and time-frequency continuous wavelet transform (CWT) techniques were used to obtain the cyclic and instantaneous characteristics of the vibration data acquired from two turbocharged four-cylinder, four-stroke diesel engine vehicles at idle under 12 different fuel conditions. The analysis revealed that TV-AutoCov analysis was the most effective for detecting changes in cycle-to-cycle combustion energy (22.61 per cent), whereas changes in the instantaneous Values of the combustion peaks were best measured using the CWT method (2.47 per cent). On the other hand, both methods showed that diesel idle vibration was more affected by amplitude modulation ( 12.54 per cent) than frequency modulation (4.46 per cent). The results of this work suggest the use of amplitude modulated signals for studying the human subjective response to diesel idle vibration at the steering wheel in passenger cars