Homin Kwon

Experiments With Sensor Motes and Java-DSP

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.

Real-time acoustic monitoring using wireless sensor motes

Wireless sensor networks (WSN) have recently gained popularity in distributed monitoring and surveillance applications. The objective of these devices is to extract pertinent information under several constrains such as low computational capabilities, limited arithmetic precision, and the need to conserve power. One of the most revealing environmental cues is audio. In this paper, we propose a voice activity detector and a simple gender classifier for use in a distributed acoustic sensing system. This algorithm makes use of low-complexity audio features and a pre-trained regression tree to classify incoming speech by gender. The algorithm is implemented real-time on the Crossbow sensor motes and a series of results are given that characterize the algorithm performance and complexity. Challenges in this real-time implementation include designing the algorithm and software architecture such that the signal processing is appropriately distributed between the sensor mote and the base station. At the base station, a data fusion algorithm considers a linear combination of individual mote decisions to form a final decision

An overview of recent advances on distributed and agile sensing algorithms and implementation

We provide an overview of recent work on distributed and agile sensing algorithms and their implementation. Modern sensor systems with embedded processing can allow for distributed sensing to continuously infer intelligent information as well as for agile sensing to configure systems in order to maintain a desirable performance level. We examine distributed inference techniques for detection and estimation at the fusion center and wireless networks for the sensor systems for real time scenarios. We also study waveform-agile sensing, which includes methods for adapting the sensor transmit waveform to match the environment and to optimize the selected performance metric. We specifically concentrate on radar and underwater acoustic signal transmission environments. As we consider systems with potentially large number of sensors, we discuss the use of resource-agile implementation approaches based on multiple-core processors in order to efficiently implement the computationally intensive processing in configuring the sensors. These resource-agile approaches can be extended to also optimize sensing in distributed sensor networks.

Real-Time Implementation of a Distributed Voice Activity Detector

Wireless sensor networks have been applied successfully in real-time distributed and collaborative sensing. In these situations, each sensor is responsible for extracting pertinent information from the surrounding environment and transmitting it to other sensors and/or to the main processing station. This is done while operating under several constraints, such as low computational capabilities, limited arithmetic precision, and the need to conserve power. In this paper, we present a low-complexity voice activity detector and a gender classifier for implementation on the Crossbow sensor motes. In addition, a decision fusion algorithm that resides at the base station is also implemented. A series of experiments that characterize the performance of the algorithms under varying conditions and in different environments are presented and several of the challenges we faced in developing this real-time implementation are discussed

A sensor network for real-time acoustic scene analysis

Acoustic scene analysis can be used to extract relevant information in applications such as homeland security, surveillance and environmental monitoring. Wireless sensor networks have been of particular interest in monitoring acoustic scenes. Sensors embedded in such a network typically operate under several constraints such as low power and limited bandwidth. In this paper, we consider resource-efficient acoustic sensing tasks that extract and transmit relevant information to a central station where information assessment can be conducted. We propose a series of acoustic scene analysis tasks that are performed in a hierarchical manner. Hierarchical tasks include sound and speech discrimination, estimation of the number of speakers from the acquired sound, gender and emotional state, and ultimately voice monitoring and key word spotting. We apply support vector machine and Gaussian mixture model algorithms on sound features. A real-time implementation is accomplished using Crossbow motes interfaced with a TI DSP board. A series of experiments are presented to characterize the performance of the algorithms under different conditions.

Real-time sensing and acoustic scene characterization for security applications

Real-time acoustic scene analysis has several applications such as homeland security, surveillance, and monitoring. The development of a collaborative networking infrastructure can be valuable in scene analysis since feature parameters can be extracted locally (at the node level) and combined at the base station. In this context, distributed and agile wireless sensor networks (WSNs) have been of particular interest recently. In this paper, we propose real-time voice scene characterization algorithms for use in a wireless sensor network. Voice scene analysis is accomplished using a speech discriminator, a gender classifier, a system for recognizing the state of emotion, and an estimator of the number of speakers in an area of interest. Real-time implementations of these algorithms are accomplished using Crossbow motes and TI DSP boards, configured to operate in a wireless sensor network. A series of experiments are presented that characterize the performance of the algorithms under different conditions.

Real-Time Collaborative Monitoring in Wireless Sensor Networks

In recent years, wireless sensor networks (WSN) have shown success in distributed real-time signal processing systems. In collaborative signal processing environments, each sensor is responsible for extracting pertinent information from the surrounding environment and transmitting it to other sensors and/or to the main processing station. Often times, the sensors operate under a number of constraints, such as limited processing power and low bandwidth. In this paper we propose a collaborative signal processing framework that is implemented in an acoustic monitoring scenario. A low-complexity voice activity detector and a gender classifier are implemented on the Crossbow sensor motes. A series of experiments are presented that characterize the performance of the algorithms under varying SNR conditions and in different environments

Low-complexity sinusoidal component selection using loudness patterns

Sinusoidal modeling of audio at low-bit rates involves selecting a limited number of parameters according to a quantitative or perceptual criterion. Most perceptual sinusoidal component selection strategies are computationally intensive and not suitable for real-time applications. In this paper, a computationally efficient sinusoidal selection algorithm based on a novel hybrid loudness estimation scheme is presented. The hybrid scheme first estimates efficiently the loudness of a multi-tone signal from the loudness patterns of its constituent sinusoidal components. Then it refines this estimate by performing a full evaluation of loudness but only in select critical bands. Experimental results show that the proposed technique maintains a low perceptual sinusoidal synthesis error at a much lower computational complexity.

Work in progress - Java simulations of DSP algorithms for ion-channel sensors

The use of ion channels as sensing elements for chemical and biological agents is a rapidly developing area. Ion channels are proteins that mediate the flow of ions and molecules across membranes such as cell walls. At Arizona State University researchers have devised a silicon ion-channel sensor. Experiments have been conducted to characterize this sensor and examine its utility in various applications. This paper presents Java functions developed to demonstrate ion channel signals and their analysis using DSP functions in class. The Java functions were developed in the J-DSP visual programming environment. Students can experiment with ion-channel signals, extract features, and differentiate signals representing the presence of different analytes.

An auditory-domain based speech enhancement algorithm

Typically, speech enhancement algorithms minimize a suitable error criterion in the spectral or time domain. Although the error criterions have included perceptual properties such as masking thresholds, non-uniform frequency resolution and sensitivity of the auditory system, these are only done heuristically and the error criterion does not explicitly include an auditory model in their formulation. In this paper, we propose an auditory-domain based speech enhancement algorithm that minimizes the distortion between the auditory representation of the estimated and desired signal. Simulation results indicate that the proposed algorithm performs effectively under different noise conditions and also results in a lower average loudness error.