Frequency Domain Local Tests for Change Detection

This book is downloadable from http://www.irisa.fr/sisthem/kniga/. Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established. We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.

Different strategies have been proposed in previous studies for monitoring the Atlantic meridional overturning circulation (AMOC). As well as arrays to directly monitor the AMOC strength, various fingerprints have been suggested to represent an aspect of the AMOC based on properties such as temperature and density. The additional value of fingerprints potentially includes the ability to detect a change earlier than a change in the AMOC itself, the ability to extend a time series back into the past, and the ability to detect crossing a threshold. In this study we select metrics that have been proposed as fingerprints in previous studies and evaluate their ability to detect AMOC changes in a number of scenarios (internal variability, weakening from increased greenhouse gases, weakening from hosing and hysteresis) in the eddy-permitting coupled climate model HadGEM3-GC2. We find that the metrics that perform best are the temperature metrics based on large-scale differences, the large-scale meridional density gradient, and the vertical density difference in the Labrador Sea. The best metric for monitoring the AMOC depends somewhat on the processes driving the change. Hence the best strategy would be to consider multiple fingerprints to provide early detection of all likely AMOC changes.

OBJETIVO: Verificar a existência de associação entre o VEMP no domínio do tempo e no domínio das frequências. MÉTODOS: A amostra foi composta por 18 indivíduos (36 orelhas), sendo seis do gênero masculino e 12 do gênero feminino. No exame de VEMP no domínio do tempo, foram promediados 200 estímulos tone burst na frequência de 500 Hz, com taxa de estimulação de 5,1 estímulos/s na intensidade de 95 dBNAn. Seguiu-se com a captação no domínio das frequências, onde foram promediados 200 estímulos constituídos por tons puros de 500 Hz (frequência portadora), modulados na frequência de 40 Hz. Foi realizado o cálculo do Odds ratio e aplicado o teste Qui-quadrado para a comparação entre as respostas dos dois domínios. Estudo de coorte contemporânea com corte transversal. RESULTADOS: O VEMP no domínio do tempo foi registrado por meio da estimulação e captação unilateral, com morfologia adequada, em 88,88% das orelhas. Na realização do exame no domínio das frequências, foi registrada a presença de pico de estado estável em 30 (83,33%) dos 36 exames. Quando realizado o teste Qui-quadrado e o cálculo do Odds ratio, foi possível observar a existência de forte relação entre os dois domínios. CONCLUSÃO: Conclui-se que existe forte associação entre os exames de VEMP no domínio do tempo e no domínio das frequências, o que habilita sua utilização na prática clínica.

A new implementation of frequency domain online test approach is proposed for general digital linear time-invariant(LTI) systems. An analytical signal processing approach is used to extract the online test relation of LTI systems in frequency domain. An optimal implementation of the developed technique is proposed using sliding Goertzel DFT to reduce area and computational overheads with minimum error detection latency. Finite-precision analysis is done for fixed-point implementation of the proposed technique. The overhead is fixed and does not increase as complexity of LTI system increases. Simulation of fault detection capability is done for different filters using developed FLI fault injector platform.

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One of the most important indicators of the production channels of developed and developing countries is undoubtedly the energy problem. In addition, the nexus between economic growth and energy use differs from country to country. The main purpose of this article is to examine the nexus between economic growth and energy consumption in Turkey in the period of 2016 and 2022. Daily electricity consumption is used as an indicator of energy consumption, and industrial production index data is used as an indicator of economic growth. Toda-Yamamoto (1995) and Breitung-Candelon (2006) frequency domain tests were used to determine for the causality. According to the outcomes of the Toda-Yamamoto causality test, a unidirectional causality relationship was found from economic growth to energy consumption in the long run. Breitung- Candelon frequency domain causality test results revealed a short (temporary) and long (permanent) relationship from economic growth to energy consumption. In Türkiye, increases in economic growth (expansion of the production channel), increase the energy need more and thus this situation increases import dependency.

High-resolution remote-sensing image change detection plays an important role in areas such as land and resources investigation, natural disaster prediction, and military strategy research. Current change detection methods often focus on extracting more discriminative features, while ignoring the information loss and imbalance problems in the process of feature fusion, which results in weakness in small change objects and edge pixels of change objects. In this letter, a simple but efficient network architecture, extraction, comparison and fusion network (ECFNet), for change detection in remote-sensing images is proposed. By constraining the number of feature channels in the fusion process by the feature comparison module (FCM), ECFNet can better utilize the fine-grained information in the multiscale feature map for result prediction, which not only improves the detection performance of small objects, but also reduces the false detection around the edge pixels of change objects. Experiments on the deeply supervised image fusion network for change detection (DSIFN-CD) test set show that ECFNet achieves state-of-the-art results with a small amount of computation.

This paper presents a method for improving filter-based network-state estimation by adding detection and estimation of sudden changes in the system state. This can be of benefit in various contexts, e.g. in network management and adaptive streaming applications. In particular, it is shown that the performance of available-bandwidth estimation can be significantly enhanced by employing change detection in conjunction with a filter-based estimator. The use of filtering makes it feasible to track the communication network state and to estimate selected properties in real-time. In addition, filter-based methods may be combined with change detection in order to overcome the trade-off regarding stable estimation versus speed of adaptation to change. We discuss filtering and change detection in general, and present the novel approach of combining the filter-based available-bandwidth estimator BART with the Generalized Likelihood Ratio (GLR) change-detection test, which estimates both the time and magnitude of changes.

The ability to detect concept drift, i.e., a structural change in the acquired datastream, and react accordingly is a major achievement for intelligent sensing units. This ability allows the unit, for actively tuning the application, to maintain high performance, changing online the operational strategy, detecting and isolating possible occurring faults to name a few tasks. In the paper, we consider a just-in-time strategy for adaptation; the sensing unit reacts exactly when needed, i.e., when concept drift is detected. Change detection tests (CDTs), designed to inspect structural changes in industrial and environmental data, are coupled here with adaptive k-nearest neighbor and support vector machine classifiers, and suitably retrained when the change is detected. Computational complexity and memory requirements of the CDT and the classifier, due to precious limited resources in embedded sensing, are taken into account in the application design. We show that a hierarchical CDT coupled with an adaptive resource-aware classifier is a suitable tool for processing and classifying sequential streams of data.

An energy storage system simulation requires a battery model capable of precisely predicting the dynamic behaviour and I – V characteristics. An equivalent circuit model (ECM) of a battery generates an electric circuit able to replicate complicated battery characteristics. Combination of time domain and frequency domain tests can be utilised to parameterise ECM components properly. The goal of this study is to discuss two systematic approaches to decide the ECM parameters, which can be replicated on any battery technology. Time and frequency domain testing are carried out on a commercial electric bicycle Li‐ion battery composed of Samsung ICR18650–22P. Then, impedance analyzer approach and Bayesian network method are applied to estimate the ECM circuit elements with different circuit topologies. The models were developed in MATLAB/Simulink environment. A highly dynamic drive cycle based on the E‐bike was utilised to validate the ECMs through hardware‐in‐loop testing.

My thesis studies how people pay attention to other people and the environment. How does the brain figure out what is important and what are the neural mechanisms underlying attention? What is special about salient social cues compared to salient non-social cues? In Chapter I, I review social cues that attract attention, with an emphasis on the neurobiology of these social cues. I also review neurological and psychiatric links: the relationship between saliency, the amygdala and autism. The first empirical chapter then begins by noting that people constantly move in the environment. In Chapter II, I study the spatial cues that attract attention during locomotion using a cued speeded discrimination task. I found that when the motion was expansive, attention was attracted towards the singular point of the optic flow (the focus of expansion, FOE) in a sustained fashion. The more ecologically valid the motion features became (e.g., temporal expansion of each object, spatial depth structure implied by distribution of the size of the objects), the stronger the attentional effects. However, compared to inanimate objects and cues, people preferentially attend to animals and faces, a process in which the amygdala is thought to play an important role. To directly compare social cues and non-social cues in the same experiment and investigate the neural structures processing social cues, in Chapter III, I employ a change detection task and test four rare patients with bilateral amygdala lesions. All four amygdala patients showed a normal pattern of reliably faster and more accurate detection of animate stimuli, suggesting that advantageous processing of social cues can be preserved even without the amygdala, a key structure of the “social brain”. People not only attend to faces, but also pay attention to others’ facial emotions and analyze faces in great detail. Humans have a dedicated system for processing faces and the amygdala has long been associated with a key role in recognizing facial emotions. In Chapter IV, I study the neural mechanisms of emotion perception and find that single neurons in the human amygdala are selective for subjective judgment of others’ emotions. Lastly, people typically pay special attention to faces and people, but people with autism spectrum disorders (ASD) might not. To further study social attention and explore possible deficits of social attention in autism, in Chapter V, I employ a visual search task and show that people with ASD have reduced attention, especially social attention, to target-congruent objects in the search array. This deficit cannot be explained by low-level visual properties of the stimuli and is independent of the amygdala, but it is dependent on task demands. Overall, through visual psychophysics with concurrent eye-tracking, my thesis found and analyzed socially salient cues and compared social vs. non-social cues and healthy vs. clinical populations. Neural mechanisms underlying social saliency were elucidated through electrophysiology and lesion studies. I finally propose further research questions based on the findings in my thesis and introduce my follow-up studies and preliminary results beyond the scope of this thesis in the very last section, Future Directions.

Sen (1984) recursive and noncursive procedures based on M–statistics to test for a possible change in the regression relationships occuring at an unknown time point. Here two modifications of Sen's recursive procedure for the location model are considered. These procedures are based on recursive M–estimators and a recursive one–step version M–estimators. They are shown to be asymptotically equivalent to Sen's one, however easily computable.

A method is described for filtering magnetotelluric (MT) data in the wavelet domain that requires a minimum of human intervention and leaves good data sections unchanged. Good data sections are preserved because data in the wavelet domain is analyzed through hierarchies, or scale levels, allowing separation of noise from signals. This is done without any assumption on the data distribution on the MT transfer function. Noisy portions of the data are discarded through thresholding wavelet coefficients. The procedure can recognize and filter out point defects that appear as a fraction of unusual observations of impulsive nature either in time domain or frequency domain. Two examples of real MT data are presented, with noise caused by both meteorological activity and power-line contribution. In the examples given in this paper, noise is better seen in time and frequency domains, respectively. Point defects are filtered out to eliminate their deleterious influence on the MT transfer function estimates. After the filtering stage, data is processed in the frequency domain, using a robust algorithm to yield two sets of reliable MT transfer functions.

There are some different statements for necessary and sufficient conditions of strict positive realness in the control literatures. These statements are usually extracted by using state space approaches. In this paper, unlike the other works the frequency domain tools is used to prove the necessary and sufficient conditions for test strict positive realness property of real rational transfer function. The first main advantages of this approach is develop a new sufficient condition for strict positive realness and the second is prove necessary and sufficient conditions for strict positive realness property of real rational transfer function directly by using original definition in the frequency domain and without any restriction on the relative degree of transfer function by default. In this paper, the original definition of strict positive realness in the frequency domain is interpreted as a mapping from S-plane to G(s)-plane. Then the maximum modulus principle and the Taylor expansion which are the fundamental tools in the complex analysis is used to extract a new sufficiency condition for strictly positive real (SPR) transfer functions. This geometry interpretation implies a new more accurate definition for SPR transfer functions. Finally more completed and simplified version of necessary and sufficient conditions for strict positive realness is proved based on the complex analysis

Chapter 9 - Frequency-Domain Approach