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Surface acoustic wave (SAW) sensors have attracted great attention worldwide for a variety of applications in measuring physical, chemical and biological parameters. However, stability has been one of the key issues which have limited their effective commercial applications. To fully understand this challenge of operation stability, this paper aims to systematically review mechanisms, stability issues and future challenges of SAW sensors for various applications.

This review paper starts with different types of SAWs, advantages and disadvantages of different types of SAW sensors and then the stability issues of SAW sensors. Subsequently, recent efforts made by researchers for improving working stability of SAW sensors are reviewed. Finally, it discusses the existing challenges and future prospects of SAW sensors in the rapidly growing Internet of Things-enabled application market.

A large number of scientific articles related to SAW technologies were found, and a number of opportunities for future researchers were identified. Over the past 20 years, SAW-related research has gained a growing interest of researchers. SAW sensors have attracted more and more researchers worldwide over the years, but the research topics of SAW sensor stability only own an extremely poor percentage in the total researc topics of SAWs or SAW sensors.

Although SAW sensors have been attracting researchers worldwide for decades, researchers mainly focused on the new materials and design strategies for SAW sensors to achieve good sensitivity and selectivity, and little work can be found on the stability issues of SAW sensors, which are so important for SAW sensor industries and one of the key factors to be mature products. Therefore, this paper systematically reviewed the SAW sensors from their fundamental mechanisms to stability issues and indicated their future challenges for various applications.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

This paper aims to give an overview about the state of wireless passive surface acoustic wave (SAW) gas sensor used in the detection of chemical vapor. It also discusses a variety of different architectures including delay line and array sensor for gas detection, and it is considered that this technology has a good application prospect.

The authors state the most of the wireless passive SAW methods used in gas sensing, such as CO2, CO, CH4, C2H4, NH3, NO2, et al., the sensor principles, design procedures and technological issues are discussed in detail; their advantages and disadvantages are also summarized. In conclusion, it gives a prospect of wireless passive SAW sensor applications and proposes the future research field might lie in the studying of many kinds of harmful gases.

In this paper, the authors will try to cover most of the important methods used in gas sensing and their recent developments. Although wireless passive SAW sensors have been used successfully in harsh environments for the monitoring of temperature or pressure, the using in chemical gases are seldom reported. This review paper gives a survey of the present state of wireless passive SAW sensor in gas detection and suggests new and exciting perspectives of wireless passive SAW gas sensor technology.

The authors will review most of the methods used in wireless passive SAW sensor and discuss the current research status and development trend; the potential application in future is also forecasted.

The authors will review most of the methods used in wireless passive SAW sensor and discuss the current research status and development trend; the potential application in future is also forecasted.

Some marking systems are very simple, such as barcoded labels, others are more sophisticated incorporating a degree of programmability. However, in recent years the need for much more than simple identification and marking has emerged and more interest is being paid to devices which are capable of holding ever‐larger amounts of data securely. Though security tagging alone constitutes a large proportion of any potential market in its own right, security, in terms of both accidental erasure and confidentiality, is always important. This paper examines various methods for secure tagging of manufactured parts including RF tags, thermochromic changes and UV absorption.

Assembly automation systems without parts tracking are rapidly becoming a rarity. Data requirements range from simple inprocess status (e.g. accept/reject, model) to full product traceability (detailed data for each part produced, referenced to serial number and production dates). Most fall somewhere between the two, such as management information (number of parts produced, downtime, number of rejects, etc.) or statistical quality control (sampled analog test data). Some use a combination of all these categories. Others requirements include implementation of cell control or CIM, tied to a plantwide computer network.

Radio frequency identification tags for passive sensing have attracted wide attention in the area of Internet of Things (IoT). Among them, some tags can sense the property change of objects without an integrated sensor, which is a new trend of passive sensing based on tag. The purpose of this paper is to review recent research on passive self-sensing tags (PSSTs).

The PSSTs reported in the past decade are classified in terms of sensing mode, composition and the ways of power supply. This paper presents operation principles of PSSTs and analyzes the characteristics of them. Moreover, the paper focuses on summarizing the latest sensing parameters of PSSTs and their matching equipment. Finally, some potential applications and challenges faced by this emerging technique are discussed.

PSST is suitable for long-term and large-scale monitoring compared to conventional sensors because it gets rid of the limitation of battery and has relatively low cost. Also, the static information of objects stored in different PSSTs can be identified by a single reader without touch.

This paper provides a detailed and timely review of the rapidly growing research in PSST.

Buildings use approximately 40% of global energy and are responsible for almost a third of the worldwide greenhouse gas emissions. They also utilise about 60% of the world’s electricity. In the last decade, stringent building regulations have led to significant improvements in the quality of the thermal characteristics of many building envelopes. However, similar considerations have not been paid to the number and activities of occupants in a building, which play an increasingly important role in energy consumption, optimisation processes, and indoor air quality. More than 50% of the energy consumption could be saved in Demand Controlled Ventilation (DCV) if accurate information about the number of occupants is readily available (Mysen et al., 2005). But due to privacy concerns, designing a precise occupancy sensing/counting system is a highly challenging task. While several studies count the number of occupants in rooms/zones for the optimisation of energy consumption, insufficient information is available on the comparison, analysis and pros and cons of these occupancy estimation techniques. This paper provides a review of occupancy measurement techniques and also discusses research trends and challenges. Additionally, a novel privacy preserved occupancy monitoring solution is also proposed in this paper. Security analyses of the proposed scheme reveal that the new occupancy monitoring system is privacy preserved compared to other traditional schemes.

Music sentiment analysis helps to promote the diversification of music information retrieval methods. Traditional music emotion classification tasks suffer from high manual workload and low classification accuracy caused by difficulty in feature extraction and inaccurate manual determination of hyperparameter. In this paper, the authors propose an optimized convolution neural network-random forest (CNN-RF) model for music sentiment classification which is capable of optimizing the manually selected hyperparameters to improve the accuracy of music sentiment classification and reduce labor costs and human classification errors.

A CNN-RF music sentiment classification model is designed based on quantum particle swarm optimization (QPSO). First, the audio data are transformed into a Mel spectrogram, and feature extraction is conducted by a CNN. Second, the music features extracted are processed by RF algorithm to complete a preliminary emotion classification. Finally, to select the suitable hyperparameters for a CNN, the QPSO algorithm is adopted to extract the best hyperparameters and obtain the final classification results.

The model has gone through experimental validations and achieved a classification accuracy of 97 per cent for different sentiment categories with shortened training time. The proposed method with QPSO achieved 1.2 and 1.6 per cent higher accuracy than that with particle swarm optimization and genetic algorithm, respectively. The proposed model had great potential for music sentiment classification.

The dual contribution of this work comprises the proposed model which integrated two deep learning models and the introduction of a QPSO into model optimization. With these two innovations, the efficiency and accuracy of music emotion recognition and classification have been significantly improved.

The paper deals with the design and creation of an intelligent user interface augmenting the user experience in everyday environments, by providing an immersive audio environment. We highlight the potential of augmenting the visual real environment in a personalized way, thanks to context modeling techniques. The LISTEN project, a system for an immersive audio augmented environment applied in the art exhibition domain, provides an example of modeling and personalization methods affecting the audio interface in terms of content and organization. In addition, the different evolution steps of the system and the outcomes of the accompanying user tests are here reported.