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This paper aims to discuss the general principles behind the microwave sensing and demonstrates the potential of cavity microwave resonator device in real-time monitoring for: environmental monitoring with the focus on wastewater pollution, a system for oil/gas/water content evaluation in a dynamic pipeline, a system for real-time determination of bacteria concentration and a method for non-invasive glucose determination.

Microwave sensing is a rapidly developing technology which has been successfully used for various industrial applications including water level measurements, material moisture content, in construction industry for non-invasive evaluation of structures and even in the healthcare industry for non-invasive real-time monitoring of glucose in diabetic patients. Novel microwave cavities designed and tested for specific applications are presented.

The paper provides experimental results of testing the novel microwave sensing systems in a range of industrial and healthcare applications and discusses the potential of these systems for real-time monitoring of processes and parameters.

The concept of real-time microwave sensing was successfully tested, but further experiments are required to account for possible interference mechanisms before it can be used commercially on a large-scale.

It is suggested that a novel approach to wastewater monitoring, namely using specially designed microwave cavity sensors, could lead to a successful development of an advanced platform capable of providing for a real-time detection of water content with superior sensitivity. Also, a system for real-time multiphase fluid composition monitoring is reported, which is essential for sustainable oil industry operation.

The paper illustrated the potential of microwave sensing as a real-time monitoring platform for a broad spectrum of commercial applications, with a focus on system developed by the authors, namely, for the monitoring of a multiphase fluid flow in a dynamic oil pipeline, for real-time monitoring of nutrients concentration in wastewater and for healthcare industry, in particular for real-time non-invasive determination of the glucose levels and bacteria concentration.

This paper investigates the feasibility of using the emission intensity of low‐pressure argon and nitrogen gas discharges as the sensing mechanism for a microwave electric field optical sensor probe in microwave resonant cavities. The emission is coupled to a photodiode for detection through an optical fibre due to the difficulty in using conventional optoelectronic devices in close proximity to microwave cavities. The discharge emission intensity is monitored at a range of different input powers to the cavity. The proposed designs for the electric field sensing probe are also included.

The purpose of this paper is to provide a detailed review of radiation dosimetry techniques based on optical fibre dosimeters. It presents a comprehensive bibliography of the current research activities in the area.

A range of published work on optical fibre radiation dosimeters are presented, with the merits and limitations discussed. Each radiation dosimetry technique is discussed in turn, providing examples of dosimeters using such techniques reviewed. The main focus is on gamma radiation although other radiation dosimeters are considered.

This paper provides information on the wide range of research activity into radiation dosimeters. The dose ranges of these dosimeters are presented, along with the advantages and disadvantages of different dosimetry techniques.

A comprehensive review of published research in the area of solid radiation dosimetry is presented in this paper. It provides an individual with a review of the various techniques used and most recent research in that field.

Modern meat processing requires automation and robotisation to remain sustainable and adapt to future challenges, including those brought by global infection events. Automation of all or many processes is seen as the way forward, with robots performing various tasks instead of people. Meat cutting is one of these tasks. Smart novel solutions, including smart knives, are required, with the smart knife being able to analyse and predict the meat it cuts. This paper aims to review technologies with the potential to be used as a so-called “smart knife” The criteria for a smart knife are also defined.

This paper reviews various technologies that can be used, either alone or in combination, for developing a future smart knife for robotic meat cutting, with possibilities for their integration into automatic meat processing. Optical methods, Near Infra-Red spectroscopy, electrical impedance spectroscopy, force sensing and electromagnetic wave-based sensing approaches are assessed against the defined criteria for a smart knife.

Optical methods are well established for meat quality and composition characterisation but lack speed and robustness for real-time use as part of a cutting tool. Combining these methods with artificial intelligence (AI) could improve the performance. Methods, such as electrical impedance measurements and rapid evaporative ionisation mass spectrometry, are invasive and not suitable in meat processing since they damage the meat. One attractive option is using athermal electromagnetic waves, although no commercially developed solutions exist that are readily adaptable to produce a smart knife with proven functionality, robustness or reliability.

This paper critically reviews and assesses a range of sensing technologies with very specific requirements: to be compatible with robotic assisted cutting in the meat industry. The concept of a smart knife that can benefit from these technologies to provide a real-time “feeling feedback” to the robot is at the centre of the discussion.

The purpose of this paper is to provide a review of the industrial sensing applications of electromagnetic radiation (EMR) with an emphasis on wavelengths other than visible light. The paper is in two parts. This, the first, considers radiations with shorter wavelengths than visible light, i.e. γ radiation, X‐rays and ultra‐violet (UV).

The paper discusses the sensing applications of short wavelength EMR through reference to the techniques employed, products and their uses.

The paper shows that γ radiation, X‐rays and UV radiation are used in a wide range of industrial sensors for the measurement of physical variables, chemical compounds and gases. The phenomena employed include absorption, backscatter, photoionisation, fluorescence and reflection. Applications are extremely varied and embrace a diversity of industries.

The paper provides a detailed, technical review of the sensing uses of short wavelength EMR.

Welding process is a complicated process influenced by many interference factors, a single sensor cannot get information describing welding process roundly. This paper simultaneously uses different sensors to get different information about the welding process, and uses multi‐sensor information fusion technology to fuse the different information. By using multi‐sensors, this paper aims to describe the welding process more precisely.

Electronic and welding pool image information are, respectively, obtained by arc sensor and image sensor, then electronic signal processing and image processing algorithms are used to extract the features of the signals, the features are then fused by neural network to predict the backside width of weld pool.

Comparative experiments show that the multi‐sensor fusion technology can predict the weld pool backside width more precisely.

The multi‐sensor fusion technology is used to fuse the different information obtained by different sensors in a gas tungsten arc welding process. This method gives a new approach to obtaining information and describing the welding process.

The purpose of this paper is to provide a brief review of sensing with neutrons.

The paper discusses neutron sources, sensing techniques and a number of established and emerging applications.

The paper shows that neutron‐based sensing is used to determine a range of physical and chemical variables in the process, minerals, chemicals, security and military industries.

The paper provides a concise overview of the technology and uses of neutron‐based sensing.

The purpose of this paper is to present classification schemes for the crystallization state of proteins utilizing image processing.

Two classification schemes shown here are combined sequentially.

The correct ratio of experimental result using the method presented here is approximately 70 per cent.

The paper is a contribution to automated evaluation crystal growth, combining two classifiers based on specific visual feature, sequentially.

The status of welding process is difficult to monitor because of the intense disturbance during the process. The purpose of this paper is to use multiple sensors to obtain information about the process from different aspects and use multi‐sensor information fusion technology to fuse the information, to obtain more precise information about the process than using a single sensor alone.

Arc sensor, visual sensor, and sound sensor were used simultaneously to obtain weld current, weld voltage, weld pool's image, and weld sound about the pulsed gas tungsten‐arc welding (GTAW) process. Then special algorithms were used to extract the signal features of different information. Fuzzy measure and fuzzy integral method were used to fuse the extracted signal features to predict the penetration status about the welding process.

Experiment results show that fuzzy measure and fuzzy integral method can effectively utilize the information obtained by different sensors and obtain better prediction results than a single sensor.

Arc sensor, visual sensor, and sound sensor are used in pulsed GTAW at the same time to obtain information, and fuzzy measure and fuzzy integral method are used to fuse the different features in welding process for the first time; experiment results show that multi‐sensor information can obtain better results than single sensor, this provides a new method for monitoring welding status and to control the welding process more precisely.

Image‐based localisation has been widely investigated in mobile robotics. However, traditional image‐based localisation approaches do not work when the environment appearance changes. The purpose of this paper is to propose a new system for image‐based localisation, which enables the approach to work also in highly dynamic environments.

The proposed technique is based on the use of a distributed vision system (DVS) composed of a set of cameras installed in the environment and of a camera mounted on a mobile robot. The localisation of the robot is achieved by comparing the current image grabbed by the robot with the images grabbed, at the same time, by the DVS. Finding the DVS's image, most similar to the robot's image, gives a topological localisation of the robot.

Experiments reported in the paper proved the system to be effective, even exploiting a pre‐existent DVS not designed for this application.

Whilst, aware that DVSs, as the one used in this work, are not diffuse nowadays, this work is significant because a novel idea is proposed for dealing with dynamic environments in the image‐based localisation approach and the idea is validated with experiments. Camera Sensor networks currently are an emerging technology and they may be introduced in several daily environments in the future.