SPECTRUM SLIDING ANALYSIS

Spectral estimation often forms the basis for distinguishing and tracking signals of interest in the presence of noise and for extracting information from the received data. The application of Fourier techniques to the problem of estimating the properties of sinusoids in noise dates back as far as Shuster (1898), Fourier spectrum analysis is the basis for almost all spectral-estimation equipment, * including the common sweeping-filter spectrum analyzer, the parallel filter bank, the fast Fourier transform (FFT), the delay-line time compressor (Deltic), and the compressive spectrum analyzer (Microscan). A problem with Fourier spectrum analysis, however, is that it makes implicit assumptions concerning data outside the observation interval and, frequently, these physically unrealistic assumptions reduce the quality of the estimates. During the past decade, two radically different non-Fourier spectral-estimation techniques have emerged — maximum-entropy spectrum analysis (Burg, 1967) and spectral decomposition (Pisarenko, 1973). These techniques offer alternative and often more realistic data models which, in many cases, lead to better estimation performance. This paper reviews the Fourier methods and compares them to the new techniques in terms of signal models assumed by the three basic methods and their ability to distinguish multiple sinusoids in noise.

This paper describes a new high speed spectrum analysis technique that tiles a spectral display using a rapid step and dwell spectral sweep. The step and dwell translation operates as a block down converter that translates successive frequency spans to an IF filter which is sampled, A-to-D converted, and processed by a high dynamic range FFT spectrum analyzer. The step and dwell sweep of the analog block down converter enables sequential spectral probes reminiscent of a swept frequency analyzer. The windowed FFT performed during the dwell synthesizes a parallel filter bank that partitions the spectral span into the same resolution bands as an FM spectral sweep through the same spectral span. By performing this second partition in parallel rather than sequentially we can reduce the time duration required to sweep the desired spectral span or we can implement post detection averaging by performing multiple FFTs during each dwell interval. The rapid spectral sweeps that can be realized by this technique make the analyzer well suited for analyzing transient and non periodic input signals.

This paper assesses the reliability of code-compliant linear and nonlinear dynamic analyses for irregular buildings with base isolation system (BIS). Comprehensive analyses are carried out for a case study comprising a large reinforced concrete multi-storey framed hospital with 327 high-damping rubber bearings. Spectral and time history (linear and nonlinear) analyses were performed on the three-dimensional (3D) finite element model (FEM) of the structure; simplified analyses were also conducted on single-degree-of-freedom (SDOF) systems. It is found that, at damageability limit state, the values of maximum interstorey drifts (d/h) computed with spectral analyses on the three-dimensional FEM range between 1/6 and 1/10 of the code limit (d/h = 0.33%); thus more stringent code limits should be required for buildings with BISs. The maximum floor acceleration is reduced by about 70% with respect to the ground acceleration (free field site); the acceleration profile is uniform along the height of the multi-storey frame. Threshold values of floor accelerations to assess the seismic performance of equipments in buildings with BIS are lacking. At ultimate limit state (ULS), spectral analyses provide values of actions and deformations that are less conservative than those derived through time history analyses. To perform reliable dynamic analyses of base isolated buildings it is crucial to select natural earthquake ground motions compliant with the fundamental period of vibration of the structural system. Nevertheless, it is not straightforward to select adequate natural strong motions in the catalogues available world-wide; buildings incorporating BISs possess periods of vibration which are generally higher than 2.0 s. As a result, distant and high-magnitude earthquakes are effective for base isolated buildings; nevertheless, such earthquakes are scarce in the seismic databases. The outcomes of the present study also demonstrate that simplified linear analyses tend to provide estimates of the response quantities, displacements of base isolators and base shear of the superstructure, which can be reliably employed at preliminary design stage. Spectral analysis results of the 3D model tend to match those of the SDOF systems, even for irregular superstructure, provided that modal mass participating ratios are greater than 85–90%. The results of spectral analyses on both SDOF and three-dimensional FEM envelope the outcomes of linear time histories.

Novel acousto-optic systems for spectrum analysis and phased array radar signal processing are proposed. The pulsed source 2-D multiplicative time and space integrating spectrum analyzer is proposed and experimentally demonstrated. This architecture is simpler, physically smaller, and less sensitive to mechanical vibrations than the pulsed source 2-D additive Mach-Zehnder interferometer based spectrum analyzer. Simple and compact optical disk based systems for 1-D and 2-D spectrum analysis are presented. The high data packing density, angular motion, and parallel read out capability of optical disks is uniquely exploited to generate the very high time bandwidth product reference signals required for temporal spectrum analysis, that are otherwise difficult to generate using electronics. A continuous wave 1-D time integrating disk spectrum analyzer is successfully demonstrated in the laboratory. The limited dynamic range due to huge bias levels in interferometric time integrating processors is alleviated by using photorefractive crystals as real time bias removers. Continuous wave 1-D and 2-D bias free acousto-optic architectures for spectrum analysis are theoretically and experimentally investigated from a systems viewpoint. Simple, compact, and powerful acousto-optic systems for control and signal processing in phased array radars are proposed that eliminate many system components like phase shifters, mixers, power dividers, and complex antenna feed networks, that are encountered in typical electronically controlled phased array radars. Via a single control parameter, namely frequency, the optical technique provides appropriately phased signals for transmission and reception in phased array antennas. The system is capable of continuous beam scanning, simultaneous multiple beam formation, and multi-target tracking, and can be integrated on a substrate to provide smaller and lighter phased array radars. Also, the system is resistant to electromagnetic interference (EMI), and signal phase shifts are independent of the radar carrier frequency employed. A 1-D linear phased array radar acousto-optic beam steerer is successfully demonstrated in the laboratory, and performance issues such as detector phase sampling error and phase linearity are highlighted. The linear array design is extended to planar and multiple linear arrays.

It has been shown both theoretically [1] and experimentally [2] that a spectrum analyzer based on an externally driven spin torque nano-oscillator (STNOs) can perform high-speed, wide-band spectrum analysis in a microwave frequency band. In such a spectrum analyzer, an STNO generates a signal whose frequency is tuned in a linear manner over a scanning bandwidth Δf. Theoretical estimations show that when employing an STNO in spectrum analysis, the following can be achieved: a Δf of 10 GHz, a linear scanning rate ρ=Δf/T (where T is the scanning period) of up to 2 GHz/ns, all in a frequency range of operation between 200 MHz and 70 GHz [3]. The fast speed of frequency analysis in a STNO-based spectrum analyzer can be achieved mainly due to the nano-scale size of the STNO, and the consequent ns characteristic scanning times. At a sufficiently large scanning rate ρ, the accuracy of the frequency determination ΔF (defined as the minimum separation required to distinguish two neighboring frequencies) of an STNO-based spectrum analyzer is fundamentally limited by the bandwidth theorem. In contrast, at low scanning rates, this accuracy is limited by the STNO generation linewidth [4], which is determined by the thermal (or random-walk) noise that is causing the jitter of the STNO generation frequency. Fig. 1 shows the frequency accuracy ΔF as a function of the scanning rate ρ for the STNO-based spectrum analyzer [2], where the gray solid line is the result of numerical modeling, dash-dotted line is a result of the experimental measurement [2], and dashed line shows the limit due to the “bandwidth” theorem. It is evident that at sufficiently large scanning rate, ΔF approaches the fundamental limit ΔF =1/T, whereas at smaller ρ the ΔF is limited by the magnitude of the STNO linewidth.

Identification of failure mechanism of the ottoman bath (hamam) at apollonia (pazarouda) exploiatation of historical data

The harmonic distortion level (HDL), an important criterion used to evaluate loudspeaker performance, can usually be measured using spectral analysis. However, spectral analysis entails great computational complexity. Furthermore, long measurement times are necessary to measure HDL accurately in a noisy environment. Therefore, we proposed the HDL measurement technique for a loudspeaker using an adaptive filter. This measurement technique can measure HDL as accurately as spectral analysis using fast Fourier transform (FFT). Furthermore, its computational complexity is much less than that of spectral analysis. Nevertheless, this measurement technique requires a long measurement time to assess the convergence of filter coefficients. This paper presents a description of the new HDL measurement technique of a loudspeaker using plural adaptive filters. The proposed measurement technique can measure HDL more accurately than spectral analysis in a noisy environment. We evaluated the performance of the proposed measurement technique using computer simulations. Results based on computer simulations show that the proposed measurement technique is more effective than spectral analysis in an acoustic environment with background noise.

PurposePrevious spectral analysis studies on obstructive sleep apnea (OSA) involved small samples, and the results were inconsistent. We performed a spectral analysis of sleep EEG based on different severities of OSA using the Sleep Heart Health Study data. This study aimed to determine the difference in EEG spectral power during sleep in the non-OSA group and with different severities of OSA in the general population.Patients and MethodsThe participants (n = 5,804) underwent polysomnography, and they were classified into non-OSA, mild OSA, moderate OSA, and severe OSA groups. The fast Fourier transformation was used to compute the EEG power spectrum for total sleep duration within contiguous 30-second epochs of sleep. The EEG spectral powers of the groups were compared using 4,493 participants after adjusting potential confounding factors that could affect sleep EEG.ResultsThe power spectra differed significantly among the groups for all frequency bands (p corr < 0.001). We found that the quantitative EEG spectral powers in the beta and sigma bands of total sleep differed (p corr < 0.001) among the participants in the non-OSA group and with different severities of OSA, controlling for covariates. The beta power was higher and the sigma power was lower in the OSA groups than in the non-OSA group. The beta power decreased in the order of severe OSA, moderate OSA, mild OSA, and non-OSA.ConclusionThis study suggests that there are differences between the microstructures of PSG-derived sleep EEG of non-OSA participants and those with different severities of OSA.

Most existing radio frequency (RF) spectrum analyzers use conventional superheterodyne architecture to remove images associated with the down conversion of RF input frequency to some intermediate frequency (IF) for further processing, and their complexity increases as the frequency range of interest is extended. This article describes a novel digital system architecture for spectrum analyzers based on quadrature down conversion. Quadrature down conversion architectures where image responses are inherently rejected are normally used to analyze a single frequency or a very narrow frequency spectrum. This article proposes using quadrature architecture in an ultrawideband spectrum analysis application. A wideband spectrum analyzer receiver with compensation for gain and phase imbalances in the RF input range, as well as compensation for gain and phase imbalances within the IF passband complete with resolution bandwidth (RBW) filtering, video bandwidth (VBW) filtering, and amplitude detection, is implemented in a low-cost field-programmable gate arrays (FPGAs). The proposed method still achieves the desired image rejection performance specification, is power-efficient, and significantly simplifies the RF front-end hardware in comparison to state-of-the-art methods.

The fast Fourier transform (FFT) algorithm is widely used as a standard tool to carry out spectral analysis because of its computational efficiency. However, the presence of multiple tones frequently requires a fine frequency resolution to achieve sufficient accuracy, which imposes the use of a large number of FFT points that results in large area and power overheads. In this paper, an FFT method is proposed for on-chip spectral analysis of multi-tone signals with particular harmonic and intermodulation components. This accurate FFT analysis approach is based on coherent sampling, but it requires a significantly smaller number of points to make the FFT realization more suitable for on-chip built-in testing and calibration applications that require area and power efficiency. The technique was assessed by comparing the simulation results from the proposed method of single and multiple tones with the simulation results obtained from the FFT of coherently sampled tones. The results indicate that the proper selection of test tone frequencies can avoid spectral leakage even with multiple narrowly spaced tones. When low-frequency signals are captured with an analog-to-digital converter (ADC) for on-chip analysis, the overall accuracy is limited by the ADC's resolution, linearity, noise, and bandwidth limitations. Post-layout simulations of a 16-point FFT showed that third-order intermodulation (IM3) testing with two tones can be performed with 1.5-dB accuracy for IM3 levels of up to 50 dB below the fundamental tones that are quantized with a 10-bit resolution. In a 45-nm CMOS technology, the layout area of the 16-point FFT for on-chip built-in testing is 0.073 mm2, and its estimated power consumption is 6.47 mW.

To apply both autoregressive (AR) and fast Fourier transform (FFT) spectral analysis at rest, during two different dynamic exercise intensities and in recovery from maximal exercise and to compare raw and normalized powers obtained with both methods. Sixteen participants (age 22.3 +/- 4.3 year) performed resting, submaximal and maximal protocols. The submaximal protocol consisted of two 5-min walks at 4 km h(-1) at treadmill grades of 0 and 7.5%. Beat-to-beat R-R series were recorded. FFT and AR analyses were preformed on the same R-R series. Compared to AR, FFT provided higher total power (TP) and raw high-frequency (HF) power at rest and exercise. Furthermore, FFT LF/HF ratio was lower than with the AR, except under resting conditions. Both methods showed reductions in TP, raw HF and LF powers during exercise and recovery. Only the AR revealed a significant reduction for normalized HF power and increase for normalized LF power in transition from rest to exercise conditions. AR and FFT methods are not interchangeable at rest or during dynamic exercise conditions. The AR method is more sensitive to the effects of exercise on the normalized power spectra of heart rate variability (HRV) than FFT. Finally, as both approaches are equally insensitive to the increase of exercise relative intensity, there is no practical advantage of performing HRV spectral analyses by the AR or FFT at higher workloads.

Theoretical and experimental kineto elastodynamic analysis of high speed linkage

Electrocardiographic (ECG) signals were recorded from ten normal resting male subjects using two bipolar lead configurations: sternal and axillary. The recordings were made under low-noise conditions with a recording system bandpass extending from dc to 10 kHz. The ECG data were digitized and spectrally analyzed using a fast Fourier transform (FFT). The spectral analysis included both single ECG cycles and averaged ECG cycles. Slight differences were found between the averaged sternal and axillary ECG spectra in the low-frequency highamplitude range. An overall mean spectrum was produced as a bound for the normal resting male ECG spectrum as encountered in this study. The maximum value of this spectrum occurred at the fundamental (1.22 Hz); it had a -20-dB point at 36 Hz, -40-dB at 105 Hz, and -60-dB at 300 Hz. To investigate the effects of band-limiting on the ECG, an ideal digital filter was implemented using the FFT. It was found that waveform notching is caused by frequencies over a wide passband, waveform amplitude information extends only to 200 Hz, and waveform duration information lies below 60 Hz.

The human brain is obviously a complex system, and exhibits rich spatiotemporal dynamics. Among the non-invasive techniques for probing human brain dynamics, electroencephalography (EEG) provides a direct measure of cortical activity with millisecond temporal resolution. Early on, EEG analysis was restricted to visual inspection of EEG records. Since there is no definite criterion evaluated by the experts, visual analysis of EEG signals is insufficient. For example, in the case of dominant alpha activity delta and theta, activities are not noticed. Routine clinical diagnosis needs to analysis of EEG signals. Therefore, some automation and computer techniques have been used for this aim (Guler et al., 2001). Since the early days of automatic EEG processing, representations based on a Fourier transform have been most commonly applied. This approach is based on earlier observations that the EEG spectrum contains some characteristic waveforms that fall primarily within four frequency bands—delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz). Such methods have proved beneficial for various EEG characterizations, but fast Fourier transform (FFT), suffer from large noise sensitivity. Parametric power spectrum estimation methods such as AR, reduces the spectral loss problems and gives better frequency resolution. AR method has also an advantage over FFT that, it needs shorter duration data records than FFT (Zoubir et al., 1998). A powerful method was proposed in the late 1980s to perform time-scale analysis of signals: the wavelet transforms (WT). This method provides a unified framework for different techniques that have been developed for various applications. Since the WT is appropriate for analysis of non-stationary signals and this represents a major advantage over spectral analysis, it is well suited to locating transient events, which may occur during epileptic seizures. Wavelet’s feature extraction and representation properties can be used to analyse various transient events in biological signals. (Adeli et al., 2003) gave an overview of the discrete wavelet transform (DWT) developed for recognising and quantifying spikes, sharp waves and spike-waves. Wavelet transform has been used to analyze and characterise epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. Through wavelet decomposition of the EEG records, transient features are accurately captured and localised in both time and frequency context. The capability of this mathematical microscope to analyse different scales of neural rhythms is shown to be a

The earthquake that often hit Indonesia caused thousands of lives and caused damage to buildings. These earthquakes often occur because Indonesia is in two regions, namely the Pacific earthquake path (Circum Pacific Earthquake Belt) and the Asian earthquake lane (Trans Asiatic Earthquake Belt). Earthquake disasters cause damage to building structures. When an earthquake occurs, it is expected that the building can accept earthquake force at a certain level without significant damage to its structure. In general, earthquake analysis is divided into two major parts, namely static earthquake analysis and dynamic earthquake analysis. In buildings that are very high, irregular, multilevel, and buildings that require enormous accuracy are used dynamic analysis planning, which consists of a variety of spectral response analysis and dynamic time response dynamic analysis. This study aims to determine the building's security in terms of displacement, drift, and base shear. The method used is a dynamic analysis of the response spectrum using the ETABS program. The maximum total drift in the X direction is 0.0200475 m and in the Y direction is 0.020405 m, so the building is safe against ultimate boundary performance (0.02h) and service boundary performance {(0.03 / R) x h}. So that the displacement in the building does not exceed the maximum displacement, the building is safe from earthquake plans.