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The purpose of this paper is to design and propose a new interface for hearing‐impaired for the users who can hardly realize the environmental sound.

The authors propose the use of an augmented reality (AR) system with sound source recognition to augment human vision. In this system, sound source and position is detected by using acoustic processing.

The authors confirmed that the source and direction of sound could be effectively recognized, and that AR was implemented, and thus that the user could use this system to recognize and visualize environmental sounds. When there was only a single sound source in the surrounding environment such as at home or when doing some simple work, and especially when a source was near a user, this system provided information on the sound source and visualized the sound source to satisfy the user's need.

The system can recognize the environmental sound in realtime and inform the user of the type of sound by showing a virtual object in the user's sight. Furthermore, the user can find the direction of the sound source by using a microphone array and locate the sound source through the AR marker attached to the object.

The purpose of this study is to determine how sonic logo’s acoustic features (intensity, pitch and pace) based on melodic tunes with no voice orient the response of consumers, attract attention, elicit levels of pleasantness and calmness and transmit brand personality traits.

A within-subject experimental factorial design is applied to measure emotional arousal (indexed as electrodermal activity) and enhancement on perceptual processing (indexed as heart rate), as well as self-reported factors, namely, calmness/excitement, pleasantness and brand personality scales.

Results show a significant increase on electrodermal activity associated with fast-paced sonic logos and a decrease in heart rate in slow-paced long sonic logos. Also, fade-up, pitch-ascending fast sonic logos are defined as more exciting and descending-pitch sonic logos as more pleasant.

The use of sonic logos with no voice does limit its implications. Besides, the use of three variables simultaneously with 18 versions of sonic logos in a laboratory setting may have driven participants to fatigue; hence, findings should be cautiously applied.

First, sonic logos are best processed in a fade-up form. Second, fast pace is recommended to orient response, whereas slow pace is recommended to transmit calmness. Practitioners may opt for fast-paced sonic logos if the design is new or played in a noisy environment and opt for slow-paced sonic logos in already highly recognized sound designs.

To the best of authors’ knowledge, this study is the first to combine psychophysiological measures and self-reported scales in a laboratory experiment on how sonic logo’s acoustic features orient response, transmit emotions and personality traits.

Purpose – To provide classroom teachers with an overview of a range of assessments that can be administered either individually or to a group.

Design/methodology/approach – The chapter is organized from early literacy skill assessments (both individual and group based) to comprehension and standardized tests.

Findings – Provides detailed information on skills required for each element of reading, design of assessment, intended purpose, and process of administration.

Research limitations/implications – This is not an exhaustive list, the authors strove to highlight the most reliable and practical assessments from a large body of possible choices.

Practical implications – This is a valuable source for classroom teachers who are provided with a wide-range of assessment choices covering the breadth of reading skills with extensive details on each.

Originality/value of paper – Teachers need a range of assessments to choose from to make decisions at the individual, class and school level.

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 propose a gesture recognition method at an early stage. An accelerometer is installed in most current mobile phones, such as iPhones, Android-powered devices and video game controllers for the Wii or PS3, which enables easy and intuitive operations. Therefore, many gesture-based user interfaces that use accelerometers are expected to appear in the future. Gesture recognition systems with an accelerometer generally have to construct models with user’s gesture data before use and recognize unknown gestures by comparing them with the models. Because the recognition process generally starts after the gesture has finished, the output of the recognition result and feedback delay, which may cause users to retry gestures, degrades the interface usability.

The simplest way to achieve early recognition is to start it at a fixed time after a gesture starts. However, the degree of accuracy would decrease if a gesture in an early stage was similar to the others. Moreover, the timing of a recognition has to be capped by the length of the shortest gesture, which may be too early for longer gestures. On the other hand, retreated recognition timing will exceed the length of the shorter gestures. In addition, a proper length of training data has to be found, as the full length of training data does not fit the input data until halfway. To recognize gestures in an early stage, proper recognition timing and a proper length of training data have to be decided. This paper proposes a gesture recognition method used in the early stages that sequentially calculates the distance between the input and training data. The proposed method outputs the recognition result when one candidate has a stronger likelihood of recognition than the other candidates so that similar incorrect gestures are not output.

The proposed method was experimentally evaluated on 27 kinds of gestures and it was confirmed that the recognition process finished 1,000 msec before the end of the gestures on average without deteriorating the level of accuracy. Gestures were recognized in an early stage of motion, which would lead to an improvement in the interface usability and a reduction in the number of incorrect operations such as retried gestures. Moreover, a gesture-based photo viewer was implemented as a useful application of our proposed method, the proposed early gesture recognition system was used in a live unscripted performance and its effectiveness is ensured.

Gesture recognition methods with accelerometers generally learn a given user’s gesture data before using the system, then recognizes any unknown gestures by comparing them with the training data. The recognition process starts after a gesture has finished, and therefore, any interaction or feedback depending on the recognition result is delayed. For example, an image on a smartphone screen rotates a few seconds after the device has been tilted, which may cause the user to retry tilting the smartphone even if the first one was correctly recognized. Although many studies on gesture recognition using accelerometers have been done, to the best of the authors’ knowledge, none of these studies has taken the potential delays in output into consideration.

The Reflective Middleware for Acoustic Management (ReM-AM), based on the Middleware for Cloud Learning Environments (AmICL), aims to improve the interaction between users and agents in a Smart Environment (SE) using acoustic services, in order to consider the unpredictable situations due to the sounds and vibrations. The middleware allows observing, analyzing, modifying and interacting in every state of a SE from the acoustics. This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management.

This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management. In this paper are defined the different domains of knowledge required for the management of the sounds in SEs, which are modeled using ontologies.

This work proposes an acoustics and sound ontology, a service-oriented architecture (SOA) ontology, and a data analytics and autonomic computing ontology, which work together. Finally, the paper presents three case studies in the context of smart workplace (SWP), ambient-assisted living (AAL) and Smart Cities (SC).

Future works will be based on the development of algorithms for classification and analysis of sound events, to help with emotion recognition not only from speech but also from random and separate sound events. Also, other works will be about the definition of the implementation requirements, and the definition of the real context modeling requirements to develop a real prototype.

In the case studies is possible to observe the flexibility that the ReM-AM middleware based on the ODA paradigm has by being aware of different contexts and acquire information of each, using this information to adapt itself to the environment and improve it using the autonomic cycles. To achieve this, the middleware integrates the classes and relations in its ontologies naturally in the autonomic cycles.

The main contribution of this work is the description of the ontologies required for future works about acoustic management in SE, considering that what has been studied by other works is the utilization of ontologies for sound event recognition but not have been expanded like knowledge source in an SE middleware. Specifically, this paper presents the theoretical framework of this work composed of the AmICL middleware, ReM-AM middleware and the ODA paradigm.

In order to succeed in an action under the Equal Pay Act 1970, should the woman and the man be employed by the same employer on like work at the same time or would the woman still be covered by the Act if she were employed on like work in succession to the man? This is the question which had to be solved in Macarthys Ltd v. Smith. Unfortunately it was not. Their Lordships interpreted the relevant section in different ways and since Article 119 of the Treaty of Rome was also subject to different interpretations, the case has been referred to the European Court of Justice.

A real potential exists for library use of voice technologies: as aids to the disabled or illiterate library user, as front‐ends for general library help systems, in online systems for commands or control words, and in many of the hands‐busy‐eyes‐busy activities that are common in libraries. Initially, these applications would be small, limited processes that would not require the more fluent human‐machine communication that we might hope for in the future. Voice technologies will depend on and benefit from new computer systems, advances in artificial intelligence and expert systems to facilitate their use and enable them to better circumvent present input and output problems. These voice systems will gradually assume more importance, improving access to information and complementing existing systems, but they will not likely revolutionize or dominate human‐machine communications or library services in the near future.

This paper aims to clarify the relationship between multiple intelligence (MI) and entrepreneurial opportunity recognition. It discusses to what extent each dimension of MI is considered as an activation driver of entrepreneurial opportunities recognition. This paper also aims to expand the domain of entrepreneurial opportunities recognition via MI by considering the mediating role of neuromarketing perception, adopting a combined perspective of intelligence, entrepreneurship and also neuromarketing to provide a future direction for the creation of interdisciplinary insights in the area of entrepreneurship.

This paper opted for literature synthesis to define key concepts surrounding MI and entrepreneurial opportunities recognition. Besides, it also attempted to identify an influential mediator in explaining the entrepreneurial opportunities recognition phenomenon. Consequently, this paper identified the gaps in current research to draw upon a more holistic conceptual framework. The rationale for the research was justified within the body of research.

This paper suggested research propositions based on the literature synthesis in view of MI and entrepreneurial opportunities recognition. More specifically, it has proposed a conceptual framework, explaining the relationship between a multi-dimensional view of both MI and entrepreneurial opportunities recognition. It is envisaged that the mediating role of neuromarketing perception incorporated in this conceptual work will improve the predictive value of the proposed framework and offer additional insights about factors that advance entrepreneurial opportunities recognition.

This paper suffers from the obvious limitation of lacking empirical investigation. However, it does provide a theoretical rationale for the argument that entrepreneurial opportunities recognition can be advanced if MI are identified and associated with neuromarketing dimensions. Perhaps the most important direction for future research is further extension and validation of this framework by performing an empirical investigation to produce newer insights into this phenomenon.

This conceptual work is different from previous studies on the grounds it has considered unexplored issues in explaining entrepreneurial opportunities recognition. To bridge the critical knowledge gap of the entrepreneurial opportunities recognition phenomenon, a mediating effect of neuromarketing perception is also integrated within the model. The proposed model was neither formulated nor tested empirically in previous studies locally or perhaps globally, therefore it stands out as an original contribution incorporating MI and entrepreneurial opportunities recognition phenomenon while considering the brain activity through neuromarketing perception.