Ear Recognition has recently received significant attention in the literature. Even though current ear recognition systems have reached a certain level of maturity, their success is still limited. This paper presents an efficient complete ear-based biometric system that can process five frames/sec; Hence it can be used for surveillance applications. The ear detection is achieved using Haar features arranged in a cascaded Adaboost classifier. The feature extraction is based on dividing the ear image into several blocks from which Local Binary Pattern feature distributions are extracted. These feature distributions are then fused at the feature level to represent the original ear texture in the classification stage. The contribution of this paper is three fold: (i) Applying a new technique for ear feature extraction, and studying various optimization parameters for that technique; (ii) Presenting a practical ear recognition system and a detailed analysis about error propagation in that system; (iii) Studying the occlusion effect of several ear parts. Detailed experiments show that the proposed ear recognition system achieved better performance (94:34%) compared to other shape-based systems as Scale-invariant feature transform (67:92%). The proposed approach can also handle efficiently hair occlusion. Experimental results show that the proposed system can achieve about (78%) rank-1 identification, even in presence of 60% occlusion.
Pressurized rail tank cars transport large volumes of volatile liquids and gases throughout the country, much of which is
hazardous and/or flammable. These gases, once released in the atmosphere, can wreak havoc with the environment and
local populations. We developed a system which can non-intrusively and non-invasively detect and locate pinhole-sized
leaks in pressurized rail tank cars using acoustic sensors. The sound waves from a leak are produced by
turbulence from the gas leaking to the atmosphere. For example, a 500 μm hole in an air tank pressurized to 689 kPa
produces a broad audio frequency spectrum with a peak near 40 kHz. This signal is detectable at 10 meters with a
sound pressure level of 25 dB. We are able to locate a leak source using triangulation techniques. The prototype of the
system consists of a network of acoustic sensors and is located approximately 10 meters from the center of the rail-line.
The prototype has two types of acoustic sensors, each with different narrow frequency response band: 40 kHz and 80
kHz. The prototype is connected to the Internet using WiFi (802.11g) transceiver and can be remotely operated from
anywhere in the world. The paper discusses the construction, operation and performance of the system.
As MEMS find application niches in an increasingly wider range of systems and platforms, nonstandard methods of device excitation are being explored as a means to achieve the desired sensor or actuator functionality. One such nonstandard method, chaotic excitation, has been used as a research tool to understand nonlinear behavior in microsystems. An extension of this work involves the use of chaotic excitation and other nonlinear phenomena to provide detailed device state information, and to enhance device operation. In order to fully understand how a MEMS device will behave under chaotic excitation, a Veeco Instruments Wyko NT1100 optical profilometer with dynamic MEMS (DMEMS) measurement capability has been used to observe the motion of a chaotically excited lateral comb resonator (LCR) device. This briefing presents theoretical modeling results based on measured parameter values that are validated by experimentally measured chaotic displacement data. Methods of using this chaotic output data for pre-packaging and in situ MEMS fault detection are discussed. The application of chaotic driving schemes to improve the sensitivity of MEMS-based inertial and chemical sensors is briefly discussed as well.
We investigate the use of a vibrational approach for the detection of barely visible impact damage in a composite UAV wing. The wing is excited by a shaker according to a predetermined signal, and the response is observed by a system of fiber Bragg grating strain sensors. We use two different driving sequences: a stochastic signal consisting of white noise, and the output from a chaotic Lorenz oscillator. On these data we apply a variety of time series analysis techniques to detect, quantify, and localize the damage incurred from a pendulum impactor, including classical linear analysis (e.g. modal analyses), as well as recently developed nonlinear analysis methods. We compare the performance of these methods, investigate the reproducibility of the results, and find that two nonlinear statistics are able to detect barely visible damage.
The Institute for Scientific Research (ISR) and the Naval Research Laboratory (NRL) will build and operate portable real-time fiber Bragg grating interrogator systems for monitoring strain in ISR's Multi-Modal Sensor (MMS) uninhabited aerial vehicle (UAV). ISR's UAV is constructed of fiberglass composites with aluminum stiffeners. The cargo bay and on-board electronics are intended to accommodate a variety of compact sensors. Because of the small size of the UAV, weight and volume are restricted, necessitating considerable redesign of laboratory interrogators to meet UAV constraints. NRL will be supplying a multiplexed interrogator for monitoring structural response rates in the UAV up to about 2 kHz, while ISR will develop an optical frequency domain reflectometer (OFDR) for measuring lower frequency response of large numbers of gratings below about 100 Hz. The OFDR system will test a special differencing technique to separate strain induced signals from environmentally induced signals. A National Instruments CompactRIO system with a 3 million gate FPGA and a 200 MHz Pentium processor is being used for real-time data acquisition and onboard signal analysis. The CompactRIO system weighs about 1.6 kg, measures 18cm x 9cm x 9cm, consumes less than 5 W of power, and withstands over 50g of shock. Lithium polymer batteries will be used to power the system for flight times up to about one hour in the present configuration. While the near-term objective of this project is to overcome the challenges of applying fiber-optic strain monitors to aerial vehicles, the longer-term objective is to develop a system for detecting damage in aerial vehicles using chaotic attractor based methods. One of the key issues in damage detection by this means revolves around the ability to use the chaotic excitation of the airframe from random aerodynamic vortices to detect the onset of composite degradation. There is evidence that attractor based methods applied to these ambient chaotic vibrations will provide a very sensitive indication of damage.