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The purpose of this paper is to present the multi‐sensory brand‐experience concept in relation to the human mind and senses. It also seeks to propose a sensory marketing (SM) model of the multi‐sensory brand‐experience hypothesis.

This paper applies exploratory and explanatory approaches to investigating the multi‐sensory brand‐experience concept within the context of discovery. The qualitative study is built on primary and secondary data sources, including personal interviews with experts and managers.

The multi‐sensory brand‐experience hypothesis suggests that firms should apply sensorial strategies and three explanatory levels within an SM model. It allows firms through means as sensors, sensations, and sensory expressions to differentiate and position a brand in the human mind as image.

A theoretical implication is that the multi‐sensory brand‐experience hypothesis emphasizes the significance of the human mind and senses in value‐generating processes. Another theoretical implication is that the hypothesis illustrates the shortcomings of the transaction and relationship marketing models in considering the multi‐sensory brand‐experience concept. It is worth conducting additional research on the multi‐sensory interplay between the human senses in value‐generating processes.

The findings offer additional insights to managers on the multi‐sensory brand‐experience concept. This research opens up opportunities for managers to identify emotional/psychological linkages in differentiating, distinguishing and positioning a brand as an image in the human mind.

The main contribution of this research lies in developing the multi‐sensory brand‐experience hypothesis within a SM model. It fills a major gap in the marketing literature and research in stressing the need to rethink conventional marketing models.

Sensor networks have found wide applications in the monitoring of environmental events such as temperature, earthquakes, fire and pollution. A major challenge with sensor network hardware is their limited available energy resource, which makes the low power design of these sensors important. This paper aims to present a low power sensor which can detect sound waveform signatures.

A novel mixed signal hardware is presented to correlate the received sound signal with a specific sound signal template. The architecture uses pulse width modulation and a single bit digital delay line to propagate the input signal over time and analog current multiplier units to perform template matching with low power usage.

The proposed method is evaluated for a chainsaw signature detection application in forest environments, under different supply voltage values, input signal quantization levels and also different template sample points. It is observed that an appropriate combination of these parameters can optimize the power and accuracy of the presented method.

The proposed mixed signal architecture allows voltage and power reduction compared with conventional methods. A network of these sensors can be used to detect sound signatures in energy limited environments. Such applications can be found in the detection of chainsaw and gunshot sounds in forests to prevent illegal logging and hunting activities.

This paper aims to present a system framework for classifying different models of military aircrafts, which is based on the sound they produce.

The technique is based on extracting a compact feature set, of only two features, extracted from the frequency domain of the aircrafts’ sound signals produced by their engines, namely, the spectral centroid and the signal bandwidth. These features are then introduced to an artificial neural network to classify the aircraft signals.

The current system identifies the aircraft type among four military aircrafts: Mirage 2000, F-16 Fighting Falcon, F-4 Phantom II and F-104 Starfighter. The experimental results show that the aforementioned types of aircrafts can be accurately classified up to 96.2 per cent via the proposed method.

The proposed system can be used as a low-cost assistive tool to the already existing radar systems to avoid cases of missed detection or false alarm. More importantly, the same method can be used for aircrafts that use stealth technology that cannot be detected using radar devices.

The proposed method constitutes a novel approach to classifying military aircrafts based on their sound signature. It utilizes only two spectral features extracted from the sound of the aircraft engine; these features are then introduced to a neural network classifier.

Examines the use of acoustic emission techniques for monitoring part mating during the assembly process. The frequency recorded during a peg insertion is compared with known frequencies of successful peg insertion by a microcomputer. This allows unsuccessful alignment to be readjusted which being monitored by a digital sound analyzer. Outlines the concept of part mating which is based on the peg‐in‐hole theory developed by Simunovic and describes an acoustic emission monitoring system. Concludes that acoustic monitoring provides a relatively low cost, low complexity system for part mating monitoring but may have limitation in manufacturing environments where there is excessive background noise or machine part vibration.

A binaural sonar sensor for blind persons which models the bat sonar is described. System performance with field plots are presented along with signal analysis on objects forming targets. The distal spatial resolution is little more than one wavelength at the lowest frequency of 50 kHz. The operating bandwidth is 50 kHz producing the power to discriminate between objects. Distance and direction information is obtained over a field of view of 50 degrees within one frequency sweep. Blind persons have demonstrated mobility akin to sighted mobility. This knowledge is of value in designing robots.

The key characteristics that eventually came to be considered to be Australian ‘heavy metal’ emerged between 1965 and 1973. These include distortion, power, intensity, extremity, loudness and aggression. This exploration of the origins of heavy metal in Australia focusses on the key acts which provided its domestic musical foundations, and investigates how the music was informed by its early, alcohol-fuelled early audiences, sites of performance, media and record shops. Melbourne-based rock guitar hero Lobby Loyde’s classical music influence and technological innovations were important catalysts in the ‘heaviness’ that would typify Australian proto-metal in the 1960s. By the early 1970s, loud and heavy rock was firmly established as a driving force of the emerging pub rock scene. Extreme volume heavy rock was taken to the masses was Billy Thorpe & The Aztecs in the early 1970s whose triumphant headline performance at the 1972 Sunbury Pop Festival then established them as the most popular band in the nation. These underpinnings were consolidated by three bands: Sydney’s primal heavy prog-rockers Buffalo (Australia’s counterpart to Britain’s Black Sabbath), Loyde’s defiant Coloured Balls and the highly influential AC/DC, who successfully crystallised heavy Australian rock in a global context. This chapter explores how the archaeological foundations for Australian metal are the product of domestic conditions and sensibilities enmeshed in overlapping global trends. In doing so, it also considers how Australian metal is entrenched in localised musical contexts which are subject to the circulation of international flows of music and ideas.