Recycling automotive, electronic, and other end-of-life waste liberates large quantities of metals, which can be returned to the supply chain. Sorting the nonferrous metals, however, is not straightforward. Common methods range from laborious hand-sorting to expensive and environmentally deleterious wet processes. The goal is to move toward dry processes, such as induction sensors and vision systems, which can identify and sort nonferrous scrap efficiently and economically. In this paper, we present a new classification method using magnetic induction spectroscopy (MIS) to sort three high-value metals that make up the majority of the nonferrous fraction-copper, aluminum, and brass. Two approaches are investigated: the first uses MIS with a set of geometric features returned by a vision system, where metal fragments are matched to known test pieces from a training set. The second approach uses MIS only . A surprisingly effective classifier can be constructed by combining the MIS frequency components in a manner determined by how eddy currents circulate in the metal fragment. An average precision and recall (purity and recovery rate) of around 92% was shown. This has significant industrial relevance, as the MIS-only classifier is simple, scalable, and straightforward to implement on existing commercial sorting lines.

Biological tissues have a complex impedance, or bio-impedance, profile which changes with respect to frequency. This is caused by dispersion mechanisms which govern how the electromagnetic field interacts with the tissue at the cellular and molecular level. Measuring the bio-impedance spectra of a biological sample can potentially provide insight into the sample’s properties and its cellular structure. This has obvious applications in the medical, pharmaceutical and food-based industrial domains. However, measuring the bio-impedance spectra non-destructively and in a way which is practical at an industrial scale presents substantial challenges. The low conductivity of the sample requires a highly sensitive instrument, while the demands of industrial-scale operation require a fast high-throughput sensor of rugged design. In this paper, we describe a multi-frequency magnetic induction spectroscopy (MIS) system suitable for industrial-scale, non-contact, spectroscopic bio-impedance measurement over a bandwidth of 156 kHz–2.5 MHz. The system sensitivity and performance are investigated using calibration and known reference samples. It is shown to yield rapid and consistently sensitive results with good long-term stability. The system is then used to obtain conductivity spectra of a number of biological test samples, including yeast suspensions of varying concentration and a range of agricultural produce, such as apples, pears, nectarines, kiwis, potatoes, oranges and tomatoes.

The magnetic dipole polarizability tensor is an object-specific property possessing information about the size, shape, and material. This information could be used by electromagnetic induction sensors typically used for demining operations to discriminate between buried mines and clutter, reducing false alarm rates, and improving demining throughput and safety. This paper presents a methodology capable of obtaining the spectroscopic tensors of small metallic objects and low metal anti-personnel mine surrogates. The experimental results are validated against simulated and analytical solutions to ensure that the obtained tensor truly represents the absolute object tensor. Absolute tensors for a number of typical clutter items and mine surrogates are presented, with significant variance observed between those of mine and clutter.

SUMMARY: Surgical robotics is a growing discipline, continuously expanding with an influx of new ideas and research. However, it is important that the development of new devices take account of past mistakes and successes. A structured approach is necessary, as with proliferation of such research, there is a danger that these lessons will be obscured, resulting in the repetition of mistakes and wasted effort and energy. There are several research paths for surgical robotics, each with different risks and opportunities and different methodologies to reach a profitable outcome. The main emphasis of this paper is on a methodology for ‘applied research’ in surgical robotics. The methodology sets out a hierarchy of criteria consisting of three tiers, with the most important being the bottom tier and the least being the top tier. It is argued that a robotic system must adhere to these criteria in order to achieve acceptability. Recent commercial systems are reviewed against these criteria, and are found to conform up to at least the bottom and intermediate tiers, the most important first two tiers, and thus gain some acceptability. However, the lack of conformity to the criteria in the top tier, and the inability to conclusively prove increased clinical benefit, is shown to be hampering their potential in gaining wide establishment.

The need to recover and recycle material towards building a circular economy is increasingly a global imperative. Non-ferrous metals in particular are highly recyclable and can be extracted using processes such as eddy current separation. However, their further separation into recyclable groups based on metal or alloy continues to pose a challenge. Recently, we proposed a new technique to discriminate between non-ferrous metals: Magnetic induction spectroscopy (MIS) measures how a metal fragment scatters an excitation magnetic field over different frequencies. MIS is related to conductivity, which can be used to classify the fragment according to this property. In this paper, we demonstrate for the first time the use of MIS with machine learning to classify non-ferrous scrap metals drawn from commercial waste streams. Two approaches are explored: (1) MIS over a bandwidth from 3 kHz to 90 kHz, and (2) the combination of MIS with physical colour of the metal samples. We show that MIS alone can obtain purity and recovery rates >80% for most metal groups and waste streams, rising to >93% for stainless steel. The exception was the Zorba waste stream where the mix of aluminium alloys within the sample set led to poor conductivity contrasts. The introduction of colour substantially improved results in this case, increasing purity and recovery rates by 20-35 percentage points. Of the machine learning models tested, we found that random forest, extra trees and support vector machine algorithms consistently achieved the highest performance.

Integration of low-frequency electrical impedance and broadband electromagnetic sensing with microfluidic devices enables high-throughput analysis of cell size, membrane properties, and intracellular characteristics.