In this paper, an electrical capacitance tomography (ECT) system for real-time measurement of solid contaminants in gas pipelines is presented. It consists of a ring of eight electrodes evenly distributed in the circular cross section of the probe. The speed-up enhancement is achieved using a field programmable gate array (FPGA) for the post-processing part of the system to accelerate the intensive matrix multiplications which are required in the image reconstruction algorithm. Experimental results on field-collected solid contaminants demonstrated the capability of the system to build in real-time two-dimensional cross-sectional images of the contaminants while giving an estimated measurement of their concentration. This allows the flow regime of the contaminants in the pipeline to be identified. Results also show that using Altera's Stratix V FPGA, 305 KLEs are required to achieve image reconstruction throughput up to 3233 frames/s for image size of 64 × 64 pixels. Simulation results were also conducted using finite-element method solver to assess the ECT probe for various image reconstruction algorithms (i.e., Linear back projection, Landweber, and modified Landweber algorithms). The results indicate a good matching with the experimental results.

Objectives: Alzheimer's disease is one of the most fastest growing and costly diseases in the world today. It affects the livelihood of not just patients, but those who take care of them, including care givers, nurses, and close family members. Current progression monitoring techniques are based on MRI and PET scans which are inconvenient for patients to use. In addition, more intelligent and efficient methods are needed to predict what the current stage of the disease is and strategies on how to slow down its progress over time. Technology or Method: In this paper, machine learning was used with S-parameter data obtained from 6 antennas that were placed around the head to noninvasively capture changes in the brain in the presence of Alzheimer's disease pathology. Measurements were conducted for 9 different human models that varied in head sizes. The data was processed in several machine learning algorithms. Each algorithm's prediction and accuracy score were generated, and the results were compared to determine which machine learning algorithm could be used to efficiently classify different stages of Alzheimer's disease. Results: Results from the study showed that overall, the logistic regression model had the best accuracy of 98.97% and efficiency in differentiating between 4 different stages of Alzheimer's disease. Clinical or Biological Impact: The results obtained here provide a transformative approach to clinics and monitoring systems where machine learning can be integrated with noninvasive microwave medical sensors and systems to intelligently predict the stage of Alzheimer's disease in the brain.

Alzheimer's disease is the most common form of neurodegenerative disease and a leading cause of dementia today. A pathological effect of Alzheimer's disease is brain atrophy, which is the progressive shrinkage of the brain volume and weight. Another effect of Alzheimer's disease is the enlargement of lateral ventricles in the brain. Currently, MRI and CT scanners can detect and show images of the brain during different stages of Alzheimer's disease. However, its limited accessibility, high costs, and static structure make it inconvenient for some to use. This paper presents the design and novel application of a wearable device comprising of flexible microwave antennas, with an operating frequency range of 800 MHz to 2.5 GHz, that detects the progression of brain atrophy and lateral ventricle enlargement in patients with Alzheimer's at the earliest stage possible. The operating principle of the antennas are simulated in near field using CST and the device is experimentally validated using lamb brain samples and samples representing cerebral spinal fluid (CSF). The measured reflection coefficients and transmission coefficients were found to correlate with changes in brain volume and changes in CSF volume successfully, thus giving an indication of the progression of Alzheimer's disease in a patient.

This paper describes a novel approach of detecting different stages of Alzheimer’s disease (AD) and imaging beta-amyloid plaques and tau tangles in the brain using RF sensors. Dielectric measurements were obtained from grey matter and white matter regions of brain tissues with severe AD pathology at a frequency range of 200 MHz to 3 GHz using a vector network analyzer and dielectric probe. Computational models were created on CST Microwave Suite using a realistic head model and the measured dielectric properties to represent affected brain regions at different stages of AD. Simulations were carried out to test the performance of the RF sensors. Experiments were performed using textile-based RF sensors on fabricated phantoms, representing a human brain with different volumes of AD-affected brain tissues. Experimental data was collected from the sensors and processed in an imaging algorithm to reconstruct images of the affected areas in the brain. Measured dielectric properties in brain tissues with AD pathology were found to be different from healthy human brain tissues. Simulation and experimental results indicated a correlated shift in the captured reflection coefficient data from RF sensors as the amount of affected brain regions increased. Finally, images reconstructed from the imaging algorithm successfully highlighted areas of the brain affected by plaques and tangles as a result of AD. The results from this study show that RF sensing can be used to identify areas of the brain affected by AD pathology. This provides a promising new non-invasive technique for monitoring the progression of AD.

This paper describes the design, development, and testing of flexible hybrid silicone-textile sensors and a flexible switching circuit that were integrated into a wearable system for monitoring neurodegenerative diseases. A total of 6 planar monopole antenna sensors were fabricated that propagates at two separate resonant frequencies: 800 MHz and 2.1 GHz respectively. In addition, 2 switching circuits, each having 3 switches and 4 SMA breakout boards, were assembled and placed on the wearable neurodegeneration monitoring system. Each switching circuit connects 3 sensors to a single port on a vector network analyzer (VNA) that is used to generate and receive microwave signals. Experiments were performed using the wearable device with the developed sensors and switching circuit on phantoms mimicking two common physiological changes in the brain caused by neurodegenerative diseases: 1) brain atrophy and 2) lateral ventricle enlargement. The dual nature of the sensors' resonance allows it to detect both brain atrophy and lateral ventricle enlargement separately at different operating frequency. This provides the advantage of minimizing the number of sensor elements needed to monitor neurodegenerative disease. The use of a switching circuit also allows for quick and convenient measurements by choosing which sensors are active for ports 1 and 2 on the VNA respectively. In addition to being low-cost, the flexibility of the materials used in fabrication allows the sensors and switching circuit to be conformal to the patient's head. Results from the experiments indicates that the sensors and switching circuit were working successfully when integrated into the wearable device.

Electrical tomography techniques for process imaging are very prominent for industrial applications, such as the oil and gas industry and chemical refineries, owing to their ability to provide the flow regime of a flowing fluid within a relatively high throughput. Among the various techniques, electrical capacitance tomography (ECT) is gaining popularity due to its non-invasive nature and its capability to differentiate between different phases based on their permittivity distribution. In recent years, several hardware designs have been provided for ECT systems that have improved its resolution of measurements to be around attofarads (aF, 10 ⁻¹⁸ F), or the number of channels, that is required to be large for some applications that require a significant amount of data. In terms of image acquisition time, some recent systems could achieve a throughput of a few hundred frames per second, while data processing time could be achieved in only a few milliseconds per frame. This paper outlines the concept and main features of the most recent front-end and back-end electronic circuits dedicated for ECT systems. In this paper, multiple-excitation capacitance polling, a front-end electronic technique, shows promising results for ECT systems to acquire fast data acquisition speeds. A highly parallel field-programmable gate array (FPGA) based architecture for a fast reconstruction algorithm is also described. This article is part of the themed issue ‘Supersensing through industrial process tomography’.