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Detection, Estimation, and Modulation Theory

As new fabrication and integration technologies reduce the cost and size of micro-sensors and wireless interfaces, it becomes feasible to deploy densely distributed wireless networks of sensors and actuators. These systems promise to revolutionize biological, earth, and environmental monitoring applications, providing data at granularities unrealizable by other means. In addition to the challenges of miniaturization, new system architectures and new network algorithms must be developed to transform the vast quantity of raw sensor data into a manageable stream of high-level data. To address this, we propose a tiered system architecture in which data collected at numerous, inexpensive sensor nodes is filtered by local processing on its way through to larger, more capable and more expensive nodes.We briefly describe Habitat monitoring as our motivating application and introduce initial system building blocks designed to support this application. The remainder of the paper presents details of our experimental platform.

Habitat monitoring: Application driver for wireless communications technology

http://felix.syntheticspeech.de/publications/ParalingusiticChallenge2010_CSL.pdf

Paralinguistics in speech and language-State-of-the-art and the challenge

The world is full of sources of sound. As I write this review, I can hear the humming of the word processor, the creaking of a door in the wind, the distant rumble of an aeroplane, the passage of a car close by, a bird twittering, my neighbour talking on his doorstep, music from his son’s hi-fi, and someone speaking on the radio in the next room. Although each source generates a particular pattern of changes in air-pressure, the changes have summed together by the time they reach my ears, yet I perceive each source distinctly. What principles of perceptual grouping and segregation do listeners use to partition such mixtures of sound? Which principles are applied automatically to all sounds? Which are specialized for particular classes of sound, such as speech? In what ways have the principles been exploited in musical composition? These are the major concerns of this lengthy, scholarly, but readable book. Bregman’s approach is functional not physiological, empirical not computational. He provides a comprehensive review and interpretation of perceptual experiments up to about 1989, so his book pre-dates recent attempts to implement auditory grouping principles in computational models and to find a physiological substrate for them. One important distinction is sustained throughout the book. Bregman argues that there are two kinds of principle for auditory grouping and segregation: “schema-based’’ and “primitive”. Schema-based principles are specific to particular types of source. They are learnt by listeners, and their application is under attentional control. One example may be the use of the knowledge of the timbre of an instrument to follow its part in an ensemble. Another example may be the use of phonetic knowledge to integrate acoustic cues in speech perception. Primitive grouping principles, in contrast, are innate, learnt through evolution. They automatically exploit fundamental physical properties of sounds and sound sources. For example: the sizes of resonators generally change slowly; they often generate energy simultaneously over a wide frequency range; when they vibrate, they create energy at the discrete

Book Review: Auditory Scene Analysis: The Perceptual Organization of SoundBregmanA.S. (1990). Auditory scene analysis: The perceptual organization of sound. Cambridge, MA: The MIT Press. Pp. 773. ISBN 0-262-02297-4. £49.50.

Active noise control over adaptive distributed networks

Improvement of image quality has been highly demanding for users in digital imaging systems. A variety of image enhancement algorithms has been developed to control image enhancement factors. This paper presents a color image enhancement method that makes use of a space-variant luminance map (SVLM) for the local brightness characterization. Two-dimensional gamma correction combined with the SVLM is developed to increase the intensity at the dark region and vice versa at the bright region in the luminance domain. The enhanced luminance information is applied to the adaptive contrast enhancing process and the linear color restoration. The proposed algorithm reveals details of the input image as well as minimizes loss of the edge sharpness in the non-uniform and low illumination conditions. The developed enhancement process resulted in the lowest absolute mean brightness error (AMBE) in a low-complexity fashion. A series of experimental results are discussed in terms of visual quality, the enhancement performance, and the complexity.

A Space-Variant Luminance Map based Color Image Enhancement

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.

Experiments With Sensor Motes and Java-DSP

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

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

Real-Time Implementation of a Distributed Voice Activity Detector

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.

Homin Kwon

Papers

A Space-Variant Luminance Map based Color Image Enhancement

Experiments With Sensor Motes and Java-DSP

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

Real-Time Implementation of a Distributed Voice Activity Detector

Real-time acoustic monitoring using wireless sensor motes