Discharge and turbidity of the regulated River Tees. Variance spectrum analysis

High suspended sediment loads may be deleterious to adult salmonids and invertebrates in gravel-bedded streams. Further, the accumulation of fine material in the interstices of the gravel may have an adverse impact on the recruitment of the young stages of salmonids. It is important therefore not only to quantify the rates and degrees of silting but also to identify sediment sources and to determine both, the frequency of sediment inputs to the system and the duration of high sediment concentrations. This report explores the application of variance spectrum analysis to the isolation of sediment periodicities. For the particular river chosen for examination the method demonstrated the essentially undisturbed nature of the catchment. The regulated river chosen for examination is the River Tees in Northern England. Variance spectrum analysis was applied to a series of over 4000 paired daily turbidity and discharge readings.

Measurement of chirp parameter and modulation index of a semiconductor laser based on optical spectrum analysis

A novel and simple method for measuring the chirp parameter, frequency, and intensity modulation indexes of directly modulated lasers is proposed in a small-signal modulation scheme. A graphical approach is presented. An analytical solution to the measurement of low chirp parameters is also given. The measured results agree well with those obtained using the conventional methods.

Mechanical fatigue and spectrum analysis of small-satellite

Mechanical fatigue due to environmental loads and spectrum analysis due to launch loads of the primary structure of a low cost, low-earth orbit small satellite intended for earth observation missions are presented. The payload of the satellite under consideration is a precise optical unit to image the earth’s surface having a mass of 45 kg. 3-D Finite Element Model for the satellite structure is generated by applying substructure method. Modal analysis is required to determine natural frequencies of the satellite and define its mode shape. Then, ranking of mode shapes according to specific constraint is performed. Harmonic analysis at resonance frequencies with the highest ranking is done and cumulative fatigue damage analysis is performed. Spectrum analysis is performed for Small Sat structure to verify the satellite structure reliability under all dynamic random vibration loads applied during transportation and launch cases.

An extensible complex fast Fourier transform processor chip for real-time spectrum analysis and measurement

This paper describes in detail the design of a CMOS custom fast Fourier transform (FFT) processor for computing a 256-point complex FFT. The FFT is well-suited for real-time spectrum analysis in instrumentation and measurement applications. The FFT butterfly processor reported here consists of one parallel-parallel multiplier and two adders. It is capable of computing one butterfly computation every 100 ns thus it can compute a 256-point complex FFT in 102.4 μs excluding data input and output processes.

An integrated 256-point complex FFT processor for real-time spectrum analysis and measurement

This paper describes in detail the design of a custom CMOS Fast Fourier Transform (FFT) processor for computing 256-point complex FFT. The FFT is well suited for real-time spectrum analysis in instrumentation and measurement applications. The FFT butterfly processor consists of one parallel-parallel multiplier and two adders. It is capable of computing one butterfly computation every 100 ns thus it can compute 256-complex point FFT in 25.6 μs excluding data input and output processes.

Identification of concurrent control chart patterns with singular spectrum analysis and learning vector quantization

Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.

Mesopelagic fish abundance and particle size spectrum analysis from Maria S. Merian cruise MSM26, spring 2013

One of the goals of EU BASIN is to understand variability in production across the Atlantic and the impact of this variability on higher trophic levels. One aspect of these investigations is to examine the biomes defined by Longhurst (2007). These biomes are largely based on productivity measured with remote sensing. During MSM 26, mesopelagic fish and size-spectrum data were collected to test the biome classifications of the north Atlantic. In most marine systems, the size-spectrum is a decay function with more, smaller organisms and fewer larger organisms. The intercept of the size-spectrum has been linked to overall productivity while the slope represents the "rate of decay" of this productivity (Zhou 2006, doi:10.1093/plankt/fbi119). A Laser In-Situ Scattering Transmissometer was used to collect size-spectrum data and net collections were made to capture mesopelagic fish. The relationship among the mesopelagic fish size and abundance distributions will be compared to the estimates of production from the size-spectrum data to evaluate the biomes of the stations occupied during MSM 26.

Particle size spectrum analysis during Maria S. Merian cruise MSM26, spring 2013

A Laser In-Situ Scattering Transmissometer (LISST) was used to collect vertical distribution data of particles from 2.5 to 500 µm in size. The LISST uses a multi-ring detector to measure scattering light of particles from a laser diode. Particles are classified into 32 log-spaced bins and the concentration of each bin is calculated as micro-liters per liter (µl/l). The instrument is rated to a depth of 300 m, and also records temperature and pressure. The sample interval was set to record every second. The LISST was attached to the LOPC frame to conduct casts and allow for particle-size comparisons between the two instruments. The LOPC is rated to a depth of 2000 m, thus a short deployment to a depth of 300 m was first conducted with both instruments. The instruments were then returned to the deck and the LISST removed via a quick release bracket so deep LOPC casts could be continued at a station. Raw LISST size-spectrum data is presented as concentrations for each of the 32 size bins for every second of the cast.

Evaluation, energy optimization, and spectrum analysis of an artificial noise technique to improve CWSN security

This paper presents the security evaluation, energy consumption optimization, and spectrum scarcity analysis of artificial noise techniques to increase physical-layer security in Cognitive Wireless Sensor Networks (CWSNs). These techniques introduce noise into the spectrum in order to hide real information. Nevertheless, they directly affect two important parameters in Cognitive Wireless Sensor Networks (CWSNs), energy consumption and spectrum utilization. Both are affected because the number of packets transmitted by the network and the active period of the nodes increase. Security evaluation demonstrates that these techniques are effective against eavesdropper attacks, but also optimization allows for the implementation of these approaches in low-resource networks such as Cognitive Wireless Sensor Networks. In this work, the scenario is formally modeled and the optimization according to the simulation results and the impact analysis over the frequency spectrum are presented.

Gaussian Process Kernels for Cross-Spectrum Analysis in Electrophysiological Time Series

Multi-output Gaussian processes provide a convenient framework for multi-task problems. An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels. Recently, the spectral mixture (SM) kernel was proposed to model the spectral density of a single task in a Gaussian process framework. This work develops a novel covariance kernel for multiple outputs, called the cross-spectral mixture (CSM) kernel. This new, flexible kernel represents both the power and phase relationship between multiple observation channels. The expressive capabilities of the CSM kernel are demonstrated through implementation of 1) a Bayesian hidden Markov model, where the emission distribution is a multi-output Gaussian process with a CSM covariance kernel, and 2) a Gaussian process factor analysis model, where factor scores represent the utilization of cross-spectral neural circuits. Results are presented for measured multi-region electrophysiological data.