Saba Emrani

Persistent Homology of Delay Embeddings and its Application to Wheeze Detection

We propose a new approach to detect and quantify the periodic structure of dynamical systems using topological methods. We propose to use delay-coordinate embedding as a tool to detect the presence of harmonic structures by using persistent homology for robust analysis of point clouds of delay-coordinate embeddings. To discover the proper delay, we propose an autocorrelation like (ACL) function of the signals, and apply the introduced topological approach to analyze breathing sound signals for wheeze detection. Experiments have been carried out to substantiate the capabilities of the proposed method.

Individual particle optimized functional link neural network for real time identification of nonlinear dynamic systems

This study considers a functional link neural network (FLNN) structure for identifying nonlinear dynamic systems. We tackle the problem of system identification in noisy environments by introducing an adaptive tuning structure based on individual particle optimization (IPO) for the nonlinear systems identification via functional link neural network. The IPO algorithm is applied in order to train the FLNN and achieve the optimum weights of the network for efficiently identifying the nonlinear systems. The proposed optimized FLNN is tested through several experiments, including real-time identification of some nonlinear dynamic systems. Finally, we develop a comparison between the results with the previous counterpart optimized FLNN based LMS, BP, and some evolutionary (GA, PSO, CLPSO) training algorithms. Simulation results verify that the proposed optimization technique, IPO, outperforms these algorithms in the sense of speedup and performance. The remarkable issue addressed here is introducing the IPO algorithm as a real-time optimal tuning technique, which is applicable in other real-time adaptive structures.

Coverage control in unknown environments using neural networks

This paper proposes a distributed adaptive control algorithm for coverage control in unknown environments with networked mobile sensors. An online neural network weight tuning algorithm is used in order for the robots to estimate the sensory function of the environment, and the control law is derived according to the feedforward neural network estimation of the distribution density function of the environment. It is distributed in that it only takes advantage of local information of each robot. A Lyapunov function is introduced in order to show that the proposed control law causes the network to converge to a near-optimal sensing configuration. Due to neural network nonlinear approximation property, a major advantage of the proposed method is that in contrary to previous well known approaches for coverage, it is not restricted to a linear regression form. Finally the controller is demonstrated in numerical simulations. Simulation results have been shown that the proposed controller outperforms the previous adaptive approaches in the sense of performance and convergence rate.

REAL TIME DETECTION OF HARMONIC STRUCTURE: A CASE FOR TOPOLOGICAL SIGNAL ANALYSIS

The goal of this study is to find evidence of cyclicity or periodicity in data with low computational complexity and high accuracy. Using delay embeddings, we transform the timedomain signal into a point cloud, whose topology reflects the periodic behavior of the signal. Persistent homology is employed to determine the underlying manifold of the point cloud, and the Euler characteristic provides for a fast computation of topology of the resulting manifold. We apply the introduced approach to breathing sound signals for wheeze detection. Our experiments substantiate the capabilities of the proposed method.

Wheeze Detection and Location using Spectro-temporal Analysis of Lung Sounds

Wheezes are abnormal lung sounds, which usually imply obstructive airways diseases. The objective of this study is to design an automatic wheeze detector for a wearable health monitoring system, which is able to locate the wheezes inside the respiratory cycle with high accuracy, and low computational complexity. We compute important features of wheezes, which we classify as temporal and spectral characteristics and employed to analyze recorded lung sounds including wheezes from patients with asthma. Time-frequency (TF) technique as well as wavelet packet decomposition (WPD) is used for this purpose. Experimental results verify the promising performance of described methods.

An adaptive leader-follower formation controller for multiple AUVs in spatial motions

This paper considers the problem of leader-follower formation control for multiple Autonomous Underwater Vehicles (AUVs) in spatial motions. The objective is to derive a leader robot along a desired trajectory, and make the follower robots keep a desired formation with respect to the leaders configuration in 3-dimensional spaces. Unlike previous studies on formation control of multiple AUVs, hydrodynamic parameter uncertainties of the AUVs is incorporated into the formation control law. To deal with such uncertainties, an adaptive control rule mainly based on inverse dynamics of the plant is developed. A Lyaponov based close-loop stability analysis is also fully presented in the paper. In order to approve the capability of the proposed controller, some illustrative cases are considered. The results of the simulations are very promising.

Virtual force based individual particle optimization for coverage in wireless sensor networks

This paper proposes a novel deployment algorithm for wireless sensor networks namely VFIPO based on individual particle optimization (IPO) incorporated with virtual force (VF) algorithm, which overcomes the shortcomings of applying the VF algorithm. Simulation results verify that mobile nodes deployment with VFIPO outperforms the previous deployment algorithms such as VF, PSO, VFPSO and IPO based approaches with respect to effective coverage area and computation time.

Coverage control for mobile sensing robots in unknown environments using neural network

This study proposes a distributed control algorithm for coverage in unknown environments with networked mobile sensors. The control law is derived according to the online neural network estimation of the sensory function of the environments. In contrary to previous adaptive approaches, a major advantage of the proposed method is that due to the neural network nonlinear approximation property, it is not restricted to multi Gaussian sensory functions. Simulation results show that the proposed controller outperforms the previous adaptive approaches in the sense of performance and convergence rate.

Spectral estimation in highly transient data

We propose a new framework for estimating different frequencies in piece-wise periodic signals with time varying amplitude and phase. Through a 3-dimensional delay embedding of the introduced model, we construct a union of intersecting planes where each plane corresponds to one frequency. The equations of each of these planes only depend on the associated frequency, and are used to calculate the tone in each segment. A sparse subspace clustering technique is utilized to find the segmentation of the data, and the points in each cluster are used to compute the normal vectors. In the presence of white Gaussian noise, principal component analysis is used to robustly perform this computation. Experimental results demonstrate the effectiveness of the proposed framework.

A Novel Framework for Pulse Pressure Wave Analysis Using Persistent Homology

Four characteristic points of pulse pressure waves-the systolic peak, the anacrotic notch, the dicrotic notch, and the diastolic foot-are used to estimate various aspects of cardiovascular function, such as heart rate and augmentation index. We propose a novel approach to extracting these characteristic points using a topological signal processing framework. We characterize the topology of the signals using a collection of persistence intervals, which are encapsulated in a persistence diagram. The characteristic points are identified based on their time of occurrence and their distance from the identity line in the persistence diagram. We validate this approach by collecting radial pulse pressure data from twenty-eight participants using a wearable tonometer, and computing the peripheral augmentation index using a traditional derivative-based method and our novel persistence-based method. The augmentation index values computed using the two methods are statistically indistinguishable, suggesting that this representation merits further exploration as a tool for analyzing pulse pressure waves.