Ice detection has developed into an integral part of aerospace and wind turbine sectors with the aim of preventing hazards and component breakdown. This article presents an ice detection system consisting of a battery-free, chip-less wirelessly interrogated resonator array, and an artificial neural network for enhanced detection robustness. The designed array of split-ring resonators (SRRs), operating at 3.05 GHz, was a narrowband frequency-selective structure with a ground plane reflector which shielded resonance from the effect of installation material. For an incident wave on the array’s surface, the reflection coefficient changed with the electrical properties of the ice and water, consequently affecting the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> parameter of an interrogator antenna. The array had a surface area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13\times6.5$ </tex-math></inline-formula> cm2 and a substrate thickness of 0.79 mm and was wirelessly interrogated from a distance of 33 cm by a standard gain horn antenna. A custom LabView program was utilized for time-based data acquisition of the antenna’s reflection coefficient [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> (dB)], with results demonstrating a resonant frequency shift of 150 MHz when 30 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{L}$ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$ </tex-math></inline-formula> ) of ice was formed on the main split of SRRs. The artificial neural network then classified the reflection coefficients of the interrogator antenna to enhance ice/water differentiation. The neural network improved the classification of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> (dB) raw data to 94.67% while achieving an accuracy of 93.33% for a noisy simulated data set. The proposed battery-free artificial neural network-assisted ice sensor can be implemented in various sizes (depending on the footprint requirements of installation) with applications in aerospace and wind turbine industry. 

Battery-Free, Artificial Neural Network-Assisted Microwave Resonator Array for Ice Detection

Robotization and intelligent digital systems in the meat cutting industry: From the perspectives of robotic cutting, perception, and digital development

Robots in manufacturing alleviate hazardous environmental conditions, reduce the physical/mental stress of the workers, maintain high precision for repetitive movements, reduce errors, speed up production, and minimize production costs. Although robots have pervaded many industrial sectors and domestic environments, the experiments in the food sectors are limited to pick-and-place operations and meat processing while we are assisting new attention in gastronomy. Given the great performances of the robots, there would be many other intriguing applications to explore which could usher the transition to precision food manufacturing. This review wants open thoughts and opinions on the use of robots in different food operations. First, we reviewed the recent advances in common applications - e.g. novel sensors, end-effectors, and robotic cutting. Then, we analyzed the use of robots in other operations such as cleaning, mixing/kneading, dough manipulation, precision dosing/cooking, and additive manufacturing. Finally, the most recent improvements of robotics in gastronomy with their use in restaurants/bars and domestic environments, are examined. The comprehensive analyses and the critical discussion highlighted the needs of further scientific understanding and exploitation activities aimed to fill the gap between the laboratory-scale results and the validation in the relevant environment.

Avenues for Non-Conventional Robotics Technology Applications in the Food Industry

Simple Summary This review describes an updated description of robot technology for slaughterhouse automation. The robot technology used in the pork and beef slaughterhouse and details of the visceral laparotomy, carcass preprocessing, and deboning robot technology were introduced. A recent novel slaughter method, meat factory cell (MFC), developed for small-scale slaughterhouse automation, was reviewed, in which more detailed research is required for practical application. New sanitary legislation for the design of robots is required, and standards for system development should be established and updated for slaughterhouse automation. This study is expected to be used for the establishment of the future automation strategy and the introduction of equipment suitable for automated slaughterhouses. Abstract Recently, many slaughterhouses have begun to introduce automation and quality evaluation sensing equipment to the slaughter processing line to overcome insufficient human resources, improve the efficiency of the slaughter process, and standardize meat quality. Various processing instruments and sensing technologies may be used depending on the livestock to be slaughtered, but a standardized process design for a smart slaughterhouse remains to be established. Slaughterhouses are becoming more industrialized, leveraging data collection and analysis to drive growth and increase production. Therefore, slaughterhouse automation is essential for meeting production demand, and an optimized design suitable for the size of each company is required to maximize economical equipment and systems. We introduce robot technology used in the slaughterhouse and detail the visceral laparotomy, carcass preprocessing, and deboning robot technology. In this study, we examine slaughterhouse automation equipment and technologies, focusing on optimizing the processing lines, the direction of application, and the hygiene of robot technique. We hope this review will provide insight into slaughterhouse automation for decision making in the slaughter industry.

/pdf/robot-technology-for-pork-and-beef-meat-slaughtering-process-3mk6vl77.pdf

Robot Technology for Pork and Beef Meat Slaughtering Process: A Review

The red meat processing industry has a harsh work environment where tasks performed in abattoirs are physically and mentally demanding. In addition, the high financial costs associated with employing skilled labour, the shortage of such workers, and the rise in worldwide meat consumption, there has been a growing push towards integrating automation as a potential solution for the industry. This paper describes the complexities of implementing robotics technology in red meat processing. The complexity when processing deformable natural meat mediums is significantly sensitive to the variations of workpieces caused by mechanical properties, physical shape and the position of tissues. These differences hinder conventional robotic systems from succeeding. Experimental and commercial robotic systems in red meat processing are shown to perform cutting tasks in the deboning room, whose systems capabilities are limited by executing cuts requiring little to no adaptability during the process. The review shows that X-ray, optical probes, and ultrasonic are the most effective sensing technologies in determining the cutting trajectories prior to the task. Some experimental systems utilised tactile sensing to follow more complex cutting paths but have not yet produced a commercially viable product. The evaluation of these sensing technologies' applicability to guide a robotic system in real-time is critical to tackling more complex cuts. A combination of preoperative scanning and real-time perception for adaptive control is recommended to automate tasks in red meat cutting. Also, it is recommended that to fully automate the meat cutting process, a gradual approach should be taken by shifting abattoirs by first utilising assistive technologies such as cobots, augmented reality, and virtual reality. 

Robotics and sensing technologies in red meat processing: A review


Purpose
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.


Design/methodology/approach
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.


Findings
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.


Originality/value
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.


/pdf/smart-knife-technological-advances-towards-smart-cutting-3hy9bt4j.pdf

Smart knife: technological advances towards smart cutting tools in meat industry automation

Abstract The Arabian tahr (Arabitragus jayakari) endemic to mountains of northern Oman and the United Arab Emirates, however, the species is faced with significant threats to its population. Because of its small and dwindling population, it is listed as Endangered. Here, we sequenced and assembled the mitochondrial (mt) genome of A. jayakari into 16,485 bp with 39.6% GC content. It also contains 13 protein-coding genes, 22 tRNA genes, and two rRNA genes. Phylogenetic analysis of A. jayakari with related 12 species mt genomes showed that A. jayakari forms a monophyletic clade with Hemitragus jayakri. In the current context of a changing environment, evolutionary analysis based on mitochondrial genome will aid in identifying evolutionary changes among different species and analyzing shared gene pools to counteract threats.

https://www.tandfonline.com/doi/pdf/10.1080/23802359.2022.2090295?needAccess=true

Complete mitochondrial genome of endangered Arabian tahr (Arabitragus jayakari) and phylogenetic placement

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Smart knife: technological advances towards smart cutting tools in meat industry automation

Complete mitochondrial genome of endangered Arabian tahr (Arabitragus jayakari) and phylogenetic placement