Joint complex diversity coding and channel coding over space, time and frequency

This study provides a general diversity analysis for joint complex diversity coding (CDC) and channel coding-based space-time-frequency codeing is provided. The mapping designs from channel coding to CDC are crucial for efficient exploitation of the diversity potential. This study provides and proves a sufficient condition of full diversity construction with joint three-dimensional CDC and channel coding, bit-interleaved coded complex diversity coding and symbol-interleaved coded complex diversity coding. Both non-iterative and iterative detections of joint channel code and CDC transmission are investigated. The proposed minimum mean-square error-based iterative soft decoding achieves the performance of the soft sphere decoding with reduced complexity.

A Wavelet-Prony Method for Modeling of Fixed-Speed Wind Farm Low-Frequency Power Pulsations

The increasing penetration of wind generation on the Island of Ireland has been accompanied by close investigation of low-frequency pulsations contained within active power flow. A primary concern is excitation of low-frequency oscillation modes already present on the system, particularly the 0.75 Hz mode as a consequence of interconnection between the Northern and Southern power system networks. In order to determine whether the prevalence of wind generation has a negative effect (excites modes) or positive impact (damping of modes) on the power system, oscillations must be measured and characterised. Using time – frequency methods, this paper presents work that has been conducted to extract features from low-frequency active power pulsations to determine the composition of oscillatory modes which may impact on dynamic stability. The paper proposes a combined wavelet-Prony method to extract modal components and determine damping factors. The method is exemplified using real data obtained from wind farm measurements.

Analysis of Fixed-Speed Wind Farm Low-Frequency Power Pulsations using a Wavelet-Prony Method

The increasing penetration of wind generation on the Island of Ireland has been accompanied by close investigation of low-frequency periodic pulsations contained within the active power flow from different wind farms. A primary concern is excitation of existing low-frequency oscillation modes already present on the system, particularly the 0.75 Hz mode as a consequence of the interconnected Northern and Southern power system networks. Recently grid code requirements on the Northern Ireland power system have been updated stipulating that wind farms connected after 2005 must be able to control the magnitude of oscillations in the range of 0.25 - 1.75 Hz to within 1% of the wind farm's registered output. In order to determine whether wind farm low-frequency oscillations have a negative effect (excite other modes) or possibly a positive impact (damping of existing modes) on the power system, the oscillations at the point of connection must be measured and characterised. Using time - frequency methods, research presented in this paper has been conducted to extract signal features from measured low-frequency active power pulsations produced by wind farms to determine the effective composition of possible oscillatory modes which may have a detrimental effect on system dynamic stability. The paper proposes a combined wavelet-Prony method to extract modal components and determine damping factors. The method is exemplified using real data obtained from wind farm measurements.

Wavelets for the Analysis and Compression of Power System Disturbances

Wavelets introduce new classes of basis functions for time-frequency signal analysis and have properties particularly suited to the transient components and discontinuities evident in power system disturbances. Wavelet analysis involves representing signals in terms of simpler, fixed building blocks at different scales and positions. This paper examines the analysis and subsequent compression properties of the discrete wavelet and wavelet packet transforms and evaluates both transforms using an actual power system disturbance from a digital fault recorder. The paper presents comparative compression results using the wavelet and discrete cosine transforms and examines the application of wavelet compression in power monitoring to mitigate against data communications overheads.

A Tower-Shadow Model for Wind Turbines Using a Wavelet-Prony Method

The increasing penetration of wind generation on the Island of Ireland has been accompanied by close investigation of low-frequency pulsations contained within active power flow. A primary concern is excitation of low-frequency oscillation modes already present on the system, particularly the 0.75 Hz mode as a consequence of interconnection between the Northern and Southern power system networks. In order to determine whether the prevalence of wind generation has a negative effect (excites modes) or positive impact (damping of modes) on the power system, oscillations must be measured and characterised. Using time – frequency methods, this paper presents work that has been conducted to extract features from low-frequency active power pulsations to determine the composition of oscillatory modes which may impact on dynamic stability. The paper proposes a combined wavelet-Prony method to extract modal components and determine damping factors. The method is exemplified using real data obtained from wind farm measurements.

Protection Assessment using Reduced Power System Fault Data

Wavelet transforms provide basis functions for time-frequency analysis and have properties that are particularly useful for the compression of analogue point on wave transient and disturbance power system signals. This paper evaluates the compression properties of the discrete wavelet transform using actual power system data. The results presented in the paper indicate that reduction ratios up to 10:1 with acceptable distortion are achievable. The paper discusses the application of the reduction method for expedient fault analysis and protection assessment.

Amplitude modulated signal transmission through a phase conjugating lens

Free space transmission of an on-off modulated sinusoidal signal through a phase conjugating lens (PCL) is theoretically examined using a combined time/frequency domain approach. The on-off keyed (OOK) signal is generated by a dipole antenna located in the far-field zone of the lens. The PCL consists of a dual layer of antenna elements interconnected via phase conjugating circuitry. We demonstrate that electromagnetic interference between antenna elements creates spatially localised areas of good-quality reception and zones where the signal is significantly denigrated by interference. Next, it is shown that destructive interference and packet desynchronisation effects critically depend on bit rate. It is also shown that a circular concave lens can be used to produce high-quality signal reception in a given direction while suppressing signal reception in all other directions. The effect that the bandwidth of the phase conjugating unit has on the transmitted signal properties for the cases of high and low bit rate OOK modulation are studied and a signal quality characterisation scheme is proposed which uses cross-correlation. The results of the study yields understanding of the performance of phase conjugating arrays under OOK modulation. The work suggests a novel approach for realising a secure communication wireless system.

Voltage Sensing Using an Asynchronous Charge-to-Digital Converter for Energy-Autonomous Environments

In future systems with relatively unreliable and unpredictable energy sources such as harvesters, the system power supply may become non-deterministic. For energy effective operations, Vdd is an important parameter in any meaningful system control mechanism. Reliable and accurate on-chip voltage sensors are therefore indispensible for the power and computation management of such systems. Existing voltage sensing methods are not suitable because they usually require a stable and known reference (voltage, current, time, frequency, etc.), which is difficult to obtain in this environment. This paper describes an autonomous reference-free voltage sensor designed using an asynchronous counter powered by the charge on a capacitor and a small controller. Unlike existing methods, the voltage information is directly generated as a digital code. The sensor, fabricated in the 180 nm technology node, was tested successfully through performing measurements over the voltage range from 1.8 V down to 0.8 V.

Data Reduction of Power System Disturbances using Wavelet Transforms for Network Operation and Control

Wavelet transforms provide basis functions for time-frequency analysis and have properties that are particularly useful for compression of analogue point on wave transient and disturbance power system signals. This paper evaluates the reduction properties of the wavelet transform using real power system data and discusses the application of the reduction method for information transfer in network communications.

The use of an instrumented vehicle in a wavelet based damage detection approach for bridge structures

Periodic monitoring of structures such as bridges is necessary as their condition can deteriorate due to environmental conditions and ageing, causing the bridge to become unsafe. This monitoring - so called Structural Health Monitoring (SHM) - can give an early warning if a bridge becomes unsafe. This paper investigates an alternative wavelet-based approach for the monitoring of bridge structures which consists of the use of a vehicle fitted with accelerometers on its axles. A simplified vehicle-bridge interaction model is used in theoretical simulations to examine the effectiveness of the approach in detecting damage in the bridge. The accelerations of the vehicle are processed using a continuous wavelet transform, allowing a time-frequency analysis to be performed. This enables the identification of both the existence and location of damage from the vehicle response. Based on this analysis, a damage index is established. A parametric study is carried out to investigate the effect of parameters such as the bridge span length, vehicle speed, vehicle mass, damage level, signal noise level and road surface roughness on the accuracy of results. In addition, a laboratory experiment is carried out to validate the results of the theoretical analysis and assess the ability of the approach to detect changes in the bridge response.

Wavelet Based Damage Detection Approach for Bridge Structures Utilising Vehicle Vibration

This paper investigates a wavelet-based damage detection approach for bridge structures. By analysing the continuous wavelet transform of the vehicle response, the approach aims to identify changes in the bridge response which may indicate the existence of damage. A numerical vehicle-bridge interaction model is used in simulations as part of a sensitivity study. Furthermore, a laboratory experiment is carried out to investigate the effects of varying vehicle configuration, speed and bridge damping on the ability of the vehicle to detect changes in the bridge response. The accelerations of the vehicle and bridge are processed using a continuous wavelet transform, allowing time-frequency analysis to be carried out on the responses of the laboratory vehicle-bridge interaction system. Results indicate the most favourable conditions for successful implementation of the approach.

Using Brain Data for Sentiment Analysis

We present the results of exploratory experiments using lexical valence extracted from brain using electroencephalography (EEG) for sentiment analysis. We selected 78 English words (36 for training and 42 for testing), presented as stimuli to 3 English native speakers. EEG signals were recorded from the subjects while they performed a mental imaging task for each word stimulus. Wavelet decomposition was employed to extract EEG features from the time-frequency domain. The extracted features were used as inputs to a sparse multinomial logistic regression (SMLR) classifier for valence classification, after univariate ANOVA feature selection. After mapping EEG signals to sentiment valences, we exploited the lexical polarity extracted from brain data for the prediction of the valence of 12 sentences taken from the SemEval-2007 shared task, and compared it against existing lexical resources.

Material Identification of Loose Particles in Sealed Electronic Devices Using PCA and SVM

The existence of loose particles left inside the sealed electronic devices is one of the main factors affecting the reliability of the whole system. It is important to identify the particle material for analyzing their source. The conventional material identification algorithms mainly rely on time, frequency and wavelet domain features. However, these features are usually overlapped and redundant, resulting in unsatisfactory material identification accuracy. The main objective of this paper is to improve the accuracy of material identification. First, the principal component analysis (PCA) is employed to reselect the nine features extracted from time and frequency domains, leading to six less correlated principal components. And then the reselected principal components are used for material identification using a support vector machine (SVM). Finally, the experimental results show that this new method can effectively distinguish the type of materials including wire, aluminum and tin particles.

A wavelet based drive-by bridge inspection system

This paper investigates a low-cost wavelet-based approach for the preliminary monitoring of bridge structures, consisting of the use of a vehicle fitted with accelerometers on its axles. The approach aims to reduce the need for direct instrumentation of the bridge. A time-frequency analysis is carried out in order to identify the existence and location of damage from vehicle accelerations. Firstly, in theoretical simulations, a simplified vehicle-bridge interaction model is used to investigate the effectiveness of the approach. A number of damage indicators are evaluated and compared. A range of parameters such as the bridge span, vehicle speed, damage level and location, signal noise and road roughness are varied in simulations. Secondly, a scaled laboratory experiment is carried out to validate the results of the theoretical analysis and assess the ability of the selected damage indicators to detect changes in the bridge response from vehicle accelerations.