Sriram Narasimhan

Video force microscopy reveals the mechanics of ventral furrow invagination in Drosophila

The absence of tools for mapping the forces that drive morphogenetic movements in embryos has impeded our understanding of animal development. Here we describe a unique approach, video force microscopy (VFM), that allows detailed, dynamic force maps to be produced from time-lapse images. The forces at work in an embryo are considered to be decomposed into active and passive elements, where active forces originate from contributions (e.g., actomyosin contraction) that do mechanical work to the system and passive ones (e.g., viscous cytoplasm) that dissipate energy. In the present analysis, the effects of all passive components are considered to be subsumed by an effective cytoplasmic viscosity, and the driving forces are resolved into equivalent forces along the edges of the polygonal boundaries into which the region of interest is divided. Advanced mathematical inverse methods are used to determine these driving forces. When applied to multiphoton sections of wild-type and mutant Drosophila melanogaster embryos, VFM is able to calculate the equivalent driving forces acting along individual cell edges and to do so with subminute temporal resolution. In the wild type, forces along the apical surface of the presumptive mesoderm are found to be large and to vary parabolically with time and angular position, whereas forces along the basal surface of the ectoderm, for example, are found to be smaller and nearly uniform with position. VFM shows that in mutants with reduced junction integrity and myosin II activity, the driving forces are reduced, thus accounting for ventral furrow failure.

Hybrid Time‐Frequency Blind Source Separation Towards Ambient System Identification of Structures

This article will discuss how ambient system identification in noisy environments, in the presence of low-energy modes or closely-spaced modes, is a challenging task. Conventional blind source separation techniques such as second-order blind identification (SOBI) and Independent Component Analysis (ICA) do not perform satisfactorily under these conditions. Furthermore, structural system identification for flexible structures require the extraction of more modes than the available number of independent sensor measurements. This results in the estimation of a non-square modal matrix that is spatially sparse. To overcome these challenges, methods that integrate blind identification with time-frequency decomposition of signals have been previously presented. The basic idea of these methods is to exploit the resolution and sparsity provided by time-frequency decomposition of signals, while retaining the advantages of second-order source separation methods. These hybrid methods integrate two powerful time-frequency decompositions—wavelet transforms and empirical mode decomposition—into the framework of SOBI. In the first case, the measurements are transformed into the time-frequency domain, followed by the identification using a SOBI-based method in the transformed domain. In the second case, a subset of the operations are performed in the transformed domain, while the remaining procedure is conducted using the traditional SOBI method. A new method to address the under-determined case arising from sparse measurements is proposed. Each of these methods serve to address a particular situation: closely-spaced modes or low-energy modes. The proposed methods are verified by applying them to extract the modal information of an airport control tower structure located in Canada.

Modified Cross-Correlation Method for the Blind Identification of Structures

Recently, blind source separation (BSS) methods have gained significant attention in the area of signal processing. Independent component analysis (ICA) and second-order blind identification (SOBI) are two popular BSS methods that have been applied to modal identification of mechanical and structural systems. Published results by several researchers have shown that ICA performs satisfactorily for systems with very low levels of structural damping, for example, for damping ratios of the order of 1% critical. For practical structural applications with higher levels of damping, methods based on SOBI have shown significant improvement over ICA methods. However, traditional SOBI methods suffer when nonstationary sources are present, such as those that occur during earthquakes and other transient excitations. In this paper, a new technique based on SOBI, called the modified cross-correlation method, is proposed to address these shortcomings. The conditions in which the problem of structural system identification can be posed as a BSS problem is also discussed. The results of simulation described in terms of identified natural frequencies, mode shapes, and damping ratios are presented for the cases of synthetic wind and recorded earthquake excitations. The results of identification show that the proposed method achieves better performance over traditional ICA and SOBI methods. Both experimental and large-scale structural simulation results are included to demonstrate the applicability of the newly proposed method to structural identification problems.

Wavelet-based blind identification of the UCLA Factor building using ambient and earthquake responses

Blind source separation using second-order blind identification (SOBI) has been successfully applied to the problem of output-only identification, popularly known as ambient system identification. In this paper, the basic principles of SOBI for the static mixtures case is extended using the stationary wavelet transform (SWT) in order to improve the separability of sources, thereby improving the quality of identification. Whereas SOBI operates on the covariance matrices constructed directly from measurements, the method presented in this paper, known as the wavelet-based modified cross-correlation method, operates on multiple covariance matrices constructed from the correlation of the responses. The SWT is selected because of its time-invariance property, which means that the transform of a time-shifted signal can be obtained as a shifted version of the transform of the original signal. This important property is exploited in the construction of several time-lagged covariance matrices. The issue of non-stationary sources is addressed through the formation of several time-shifted, windowed covariance matrices. Modal identification results are presented for the UCLA Factor building using ambient vibration data and for recorded responses from the Parkfield earthquake, and compared with published results for this building. Additionally, the effect of sensor density on the identification results is also investigated.

Re-tuning tuned mass dampers using ambient vibration measurements

Deterioration, accidental changes in the operating conditions, or incorrect estimates of the structure modal properties lead to de-tuning in tuned mass dampers (TMDs). To restore optimal performance, it is necessary to estimate the modal properties of the system, and re-tune the TMD to its optimal state. The presence of closely spaced modes and a relatively large amount of damping in the dominant modes renders the process of identification difficult. Furthermore, the process of estimating the modal properties of the bare structure using ambient vibration measurements of the structure with the TMD is challenging. In order to overcome these challenges, a novel identification and re-tuning algorithm is proposed. The process of identification consists of empirical mode decomposition to separate the closely spaced modes, followed by the blind identification of the remaining modes. Algorithms for estimating the fundamental frequency and the mode shape of the primary structure necessary for re-tuning the TMD are proposed. Experimental results from the application of the proposed algorithms to identify and re-tune a laboratory structure TMD system are presented.

Underdetermined Blind Identification of Structures by Using the Modified Cross-Correlation Method

The modified cross-correlation (MCC) blind identification method is extended to handle the underdetermined case of structural system identification. The underdetermined case is one in which the number of sensors is less than the number of identifiable modes. The basic framework of the modified cross-correlation method is retained in cases in which multiple covariance matrices constructed from the correlation of the responses are diagonalized. The solution to the underdetermined blind identification consists of two stages: the generation of intrinsic mode functions (IMFs) from the measurements by using empirical mode decomposition (EMD) and the application of the modified cross-correlation method to the decomposed signals. The available measurements are first decomposed into IMFs by using the sifting process of EMD. Subsequently, the IMFs are used as initial estimates for the sources, and the MCC method is implemented in an iterative framework. Initial estimates for the mixing matrix necessary to start the...

Adaptive Compensation for Detuning in Pendulum Tuned Mass Dampers

Detuning, resulting from deterioration, inadvertent changes to structure properties, and design forecasting, can lead to a significant loss of performance in tuned mass dampers (TMDs). To overcome this issue, an adaptive compensation mechanism for suspended pendulum TMDs is proposed. The adaptive pendulum mass damper is a three-dimensional pendulum, augmented with a tuning frame to adjust its natural frequency, and two adjustable air dampers adjust damping. The adjustments for the natural frequency and damping compensation are achieved using a system of stepper motors and a microcontroller. There are two major components in the proposed methodology: identification and control, one followed by the other, in that order. The identification is carried out using spectral information obtained from the structural acceleration responses. The performance of the adaptive pendulum system is studied via both experiments and simulations. The main contribution of this paper is to develop an effective means of compensation for detuning in TMDs, while retaining the simplicity of passive pendulum TMDs. The proposed methodology allows pendulum TMDs to be tuned in place using relatively simple hardware and algorithms, based on ambient vibration measurements only.

Decentralized modal identification using sparse blind source separation

Popular ambient vibration-based system identification methods process information collected from a dense array of sensors centrally to yield the modal properties. In such methods, the need for a centralized processing unit capable of satisfying large memory and processing demands is unavoidable. With the advent of wireless smart sensor networks, it is now possible to process information locally at the sensor level, instead. The information at the individual sensor level can then be concatenated to obtain the global structure characteristics. A novel decentralized algorithm based on wavelet transforms to infer global structure mode information using measurements obtained using a small group of sensors at a time is proposed in this paper. The focus of the paper is on algorithmic development, while the actual hardware and software implementation is not pursued here. The problem of identification is cast within the framework of under-determined blind source separation invoking transformations of measurements to the time–frequency domain resulting in a sparse representation. The partial mode shape coefficients so identified are then combined to yield complete modal information. The transformations are undertaken using stationary wavelet packet transform (SWPT), yielding a sparse representation in the wavelet domain. Principal component analysis (PCA) is then performed on the resulting wavelet coefficients, yielding the partial mixing matrix coefficients from a few measurement channels at a time. This process is repeated using measurements obtained from multiple sensor groups, and the results so obtained from each group are concatenated to obtain the global modal characteristics of the structure.

Blind identification of earthquake-excited structures

A new method based on the popular second-order blind identification method, SOBI, is presented to estimate the modal properties of structures under non-stationary earthquake excitations. Since the proposed method is cast within the framework of blind source separation, the issues associated with model-order pre-selection and the use of stability charts in traditional system identification methods are not present. The SOBI method involves the joint diagonalization of multiple covariance matrices of measurements, which is rendered difficult in the presence of non-stationary excitations. This difficulty is overcome in the proposed method by a diagonalization procedure involving a new set of weighted covariance matrices. There are two main contributions in this paper. First, a diagonalization technique that involves the joint-approximate diagonalization of the proposed set of several time-lagged and suitably weighted covariance matrices is developed. Next, a parametric relationship between the key parameters of the proposed method and a suitably chosen non-stationary parameter of the response is developed to aid in the selection of the optimal parameters under non-stationary excitations. In order to demonstrate the results obtained using the proposed method, identification results from the UCLA Factor building using recorded responses from the Parkfield earthquake are utilized.

Decentralized Modal Identification of a Pony Truss Pedestrian Bridge Using Wireless Sensors

Most of the vibration-based ambient modal identification methods in the literature are structured to process vibration data collected from a dense array of sensors centrally to yield modal information. For large systems, for example bridges, one of the main disadvantages of such a centralized architecture is the cost of dense instrumentation, predominantly consisting of the sensors themselves, the data acquisition system, and the associated cabling. Recent advances in wireless smart sensors have addressed the issue of sensor cost to some extent; however, most of the algorithms—with the exception of very few—still retain an essentially centralized architecture. To harness the full potential of decentralized implementation, the authors have developed a new class of algorithms exploiting the concepts of sparsity (using wavelet transforms) within the framework of blind source separation. The problem of identification is cast within the framework of underdetermined blind source separation invoking transformations of measurements to the wavelet domain resulting in a sparse representation. Although the details of these decentralized algorithms have been discussed in other articles, in this paper, for the first time, these algorithms are studied experimentally on a full-scale structure using wireless sensors. In a truly decentralized implementation, only two sensors are roved along the length of a pedestrian bridge, and the performance of the proposed algorithms is studied in detail. A pedestrian bridge located in Montreal, Quebec, Canada, is chosen primarily to highlight the methodology used to address modal identification under low-sensor density and for pedestrian loading. Issues arising from several modes being excited on this bridge and the presence of narrowband pedestrian excitations are addressed. The accuracy of modal identification that is achieved using the proposed decentralized algorithms is compared with the results obtained from their centralized counterparts.