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Modern data collection in the Earth Sciences has propelled the need for understanding signal processing and time-series analysis techniques. However, there is an educational disconnect in the lack of instruction of time-series analysis techniques in many Earth Science academic departments. Furthermore, there are no platform-independent freeware tools available for teaching Earth signals analysis. In order to address these issues, we developed the Java-Digital Signal Processing/Earth Systems Edition (J-DSP/ESE), a platform-independent software tool that can be integrated with the Earth Science university curriculum for signal processing and analysis instruction. This tool has an intuitive block-based programming environment, and students do not need be familiar with any programming language to use it. In order to demonstrate the utility of this software in an instructional environment, we developed three tutorials related to basic signal processing, and signal analysis of modem and past climate change. The tutorials use published data to examine the relationship between 20th century atmospheric CO2 and global temperature, and the relationship between ocean temperature and solar radiation over the past 300,000 y. The tutorials were administered in two workshops with different communities of students in Earth Science and electrical engineering. Our technical assessments show that the students were able to comprehend basic signal processing and analysis of climate signals using J-DSP/ESE. In the subjective assessments, a vast majority of students stated that the software was easy to learn and use, and that it significantly improved their understanding of climate change. [ABSTRACT FROM AUTHOR]

Environmental monitoring relies on compact, portable sensor systems capable of detecting pollutants in real-time. An integrated chemical sensor array system is developed for detection and identification of environmental pollutants in diesel and gasoline exhaust fumes. The system consists of a low noise floor analog front-end (AFE) followed by a signal processing stage. In this paper, we present techniques to detect, digitize, denoise and classify a certain set of analytes. The proposed AFE reads out the output of eight conductometric sensors and eight amperometric electrochemical sensors and achieves 91 dB SNR at 23.4 mW quiescent power consumption for all channels. We demonstrate signal denoising using a discrete wavelet transform based technique. Appropriate features are extracted from sensor data, and pattern classification methods are used to identify the analytes. Several existing pattern classification algorithms are used for analyte detection and the comparative results are presented. [ABSTRACT FROM AUTHOR]

Distributed wireless sensor networks (WSNs) are being proposed for various applications including defense, security, and smart stages. The introduction of hardware wireless sensors in a signal processing education setting can serve as a paradigm for data acquisition, collaborative signal processing, or simply as a platform for obtaining, processing, and analyzing real-life real-time data. In this paper, a software interface that enables the java-digital signal processing (J-DSP) visual programming environment to communicate in a two-way manner with a wireless sensor network is presented. This interface was developed by writing nesC (an extension to the C programming language for sensors) code that enables J-DSP to issue commands to multiple wireless sensor motes, activate specific transducers, and analyze data using any of the existing J-DSP signal processing functions in real time. A series of exercises were developed and disseminated to provide hardware experiences to signals and systems and signal processing undergraduate students. The hardware with the J-DSP software has been used for two semesters in the senior level digital signal processing (DSP) course at Arizona State University. The interface, the exercises, and their assessment (instruments and results) are described in the paper. [ABSTRACT FROM AUTHOR]

Abstract: Gradient-type adaptive channel equalizers can suffer from burst error problems under sustained fading and in the absence of sufficient excitation. To overcome this problem, we propose an algorithm called the gradient-with-projection-on-bounding-ellipsoid (GPrOBE). The main idea in the proposed GPrOBE algorithm is that the equalizer parameters are computed by projecting the gradient estimate onto a set that is updated using a priori information on the instantaneous error magnitude. Simulation results show an improvement in the performance of the GPrOBE algorithm over other gradient-based algorithms in terms of reduced error bursts. Normalized error energy (NEE) performance analysis highlights that the GPrOBE algorithm yields solutions that are close to minimum mean-square-error (MMSE), while providing instantaneous performance guarantees during periods of insufficient excitation. Bit error rate (BER) experiments reveal that the GPrOBE algorithm is more robust in channels with sustained fading. [Copyright &y& Elsevier]

The Java-DSP (J-DSP) on-line laboratory software has been developed from the ground up at Arizona State University to support the computer lab portion of the senior-level DSP course. J-DSP provides capabilities for web-based DSP simulations that can be run using a PC with a Java-enabled browser. J-DSP is accompanied by exercises that actively engage students in several concepts including Z-transforms, filter design, spectral analysis, and random signal processing. Tools and on-line instruments for assessment of the J-DSP software and the associated lab exercises have been developed and described in this letter. Statistical analysis of the pre/post assessment data revealed that the effect of student involvement and the student learning has been enhanced by using J-DSP. [ABSTRACT FROM AUTHOR]