Vahid Emamian

Robust clustering of acoustic emission signals using the Kohonen network

Acoustic emission-based techniques are promising for nondestructive inspection of mechanical systems. For reliable automatic fault monitoring, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. In this paper we propose the application of the Kohonen network for this purpose. The principal components of the short-time Fourier transforms of the data were applied input of the network. The clustering results confirm the capability of the Kohonen network for reliable source identification of acoustic emission signals, assuming enough care has been taken in implementing the training algorithm of the network.

Robust Clustering of Acoustic Emission Signals Using Neural Networks and Signal Subspace Projections

Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems. For reliable automatic fault monitoring related to the generation and propagation of cracks, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. A prominent difficulty is the inability to differentiate events due to crack growth from noise of various origins. This work presents a novel algorithm for automatic clustering and separation of acoustic emission (AE) events based on multiple features extracted from the experimental data. The algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA), and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM) neural network, which outputs the noise and AE signals into separate neurons. To improve the efficiency of classification, the short-time Fourier transform (STFT) is applied to retain the time-frequency features of the remaining events, reducing the dimension of the data. The algorithm is verified with two sets of data, and a correct classification ratio over 95% is achieved.

Outage probability with transmit and receive diversity in a shadowing environment

We study the effects of lognormal shadowing on systems employing transmit and receive diversity. We derive a closed form formula for the average outage probability of a cellular system with M transmit antennas and N receive antennas. Two cases are considered: (i) all M /spl times/ N channels are affected by the same shadowing environment, and (ii) all channels face independent shadowing. We validate the formulas that are derived by Monte-Carlo simulations. The results show that the outage probability of the independent-shadowing channels is less than the case in which all channels face the same shadowing.

Acoustic emission classification for failure prediction due to mechanical fatigue

Acoustic Emission signals (AE), generated by the formation and growth of micro-cracks in metal components, have the potential for use in mechanical fault detection in monitoring complex- shaped components in machinery including helicopters and aircraft. A major challenge for an AE-based fault detection algorithm is to distinguish crack-related AE signals from other interfering transient signals, such as fretting-related AE signals and electromagnetic transients. Although under a controlled laboratory environment we have fewer interference sources, there are other undesired sources which have to be considered. In this paper, we present some methods, which make their decision based on the features extracted from time-delay and joint time-frequency components by means of a Self- Organizing Map (SOM) neural network using experimental data collected in a laboratory by colleagues at the Georgia Institute of Technology.

Sequential Image Registration for Astronomical Images

Astronomical images are characterized by their smooth features, low level of Signal to Noise Ratio (SNR), and their extreme sensitivity to the motion of platform. Due to the low SNR, it is necessary to collect a large number of frames and consider the average. However, it is a common occurrence to have unregistered frames in the sequence. Frame registration using feature-based approach fails due to low contrast. Also, area-based approaches such as template matching and phase correlation methods, although accurate, suffer from computational inefficiency as a result of the large size and number of image frames in a sequence. This paper introduces a novel two-stage algorithm to accelerate the process of registration. The first stage projects the direction of movement as a cluster of parallel streaks and determines the angle of motion, using Linear Hough Transform. The next stage utilizes Normalized Cross Correlation only in the estimated direction to find the exact amount of displacement. Experimental results have been tabulated to illustrate superior computational efficiency of the proposed algorithm versus phase correlation, as well as robustness of the procedure in the presence of the noise.

Comparing power consumptions of collaborative and non-collaborative systems

In a collaborative wireless network, communicating nodes collaborate in routing and/or improving the quality of transmission of each others packets. This is especially useful when the channel between a pair of nodes is in a deep shadow-fading state. In this situation, increasing the power level may either not resolve the problem or be too power consuming, while generating interference for other receivers on the same channel. A collaboration node, which has good propagation channels to both the source and the destination, may be used to relay the packets between them. The average amounts of power consumed by nodes in a standard wireless network that uses single-hop transmission and a collaborative wireless network that uses two-hop transmission is compared. It is shown that under certain conditions the ratio of the average power consumptions in the two networks, when N collaborating nodes on average are available for each node, can be approximated by k ln N + q. The constant k and q are related to the propagation channel. For a Nakagami fading channel with parameter m, k = 1/ln m and q = 1, while for a shadowing channel with standard deviation /spl sigma//sub dB/, k = /spl sigma//sub dB/ / /spl radic/ /spl pi/ and q = 0.23 /spl sigma//sub dB/.

Acoustic emission classification using signal subspace projections

In using acoustic emissions (AE) for mechanical diagnostics, one major problem is the differentiation of events due to crack growth in a component from noise of various origins. This work presents two algorithms for automatic clustering and separation of AE events based on multiple features extracted from experimental data. The first algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA), and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM), which outputs the noise and AE signals into separate neurons. The algorithm is verified with two sets of data, and a correct classification ratio of over 95% is achieved. The second algorithm characterizes the AE signal subspace based on the principal eigenvectors of the covariance matrix of an ensemble of the AE signals. The latter algorithm has a correct classification ratio over 90%.

Security of IoT Devices

ABSTRACT The network of home devices is the concept of establishing a web of connections using SMART devices in the household. These devices, a part of the Internet of Things (IoT), have expanded the idea of interconnectedness on the Internet. As technology progresses, more devices are given the capability of connecting to a network, creating convenience for users. Within a household specifically, SMART appliances such as washers, dryers, refrigerators and TVs can connect to a network via Bluetooth, Ethernet or Wi-Fi. Although the benefits of IoT devices are significant, so are the potential downsides that can be created when improper implementation of protocols used to communicate information among networks are used. As millions of devices connect to the Internet daily, it is important to measure the security of these connections. Not uncommon to find, a popular trade-off exists between security-implementation cost and actual security levels. In this research, we study proper security implementation of connected household electronic devices for better safeguarding the transmitted data.

Outage Analysis of a Multi-User Spatial Diversity System in a Shadow-Fade Propagating Channel

In a wireless network, communication between a source and a destination mobile station (DMS) fails to establish if the source or the DMS is located inside a deep shadow-fading propagating channel. In this situation, intermediate mobile stations may be used to relay the signal between the two nodes. In a cellular system the source is a base station (BS) and the DMS is a weak mobile station (MS) while in an ad-hoc network, the source and the DMS are two nodes of the network. This paper presents the scheme of “multi-user spatial diversity” as a method of diversity to combat the undesired shadow-fade channel behavior. A model is presented for the case where one or several mobile stations (MSs) relay the signal between the source and the DMS, in a shadow-fading environment. A formula is derived for the average outage probability of the received signal-to-noise ratio at the DMS, when M intermediate MSs relay the signal from the source to the DMS according to a particular protocol. The outage probability improves as the number of the relays increases.

Application of Cooperative Wireless Networks in Reducing Power Consumption of Robots Exploring a Planet

This paper presents the comparison of the average amounts of power consumed by robots in a standard network that uses single-hop transmission and a cooperative network that uses two-hop transmission. It is shown that the ratio of the average power consumptions in the two networks depends on the number of cooperating robots on average that are available for the source and the destination, and the propagation channel. It is shown that robots in a cooperative network significantly consume less power on average.