In this paper a number of LT Spice simulations have been carried out on an Electrical Impedance Tomography (EIT) system, which includes the whole analog and digital circuitry as well as the subject to be examined (phantom model). The aim of this study is to show how the analog and digital parts, the electrodes and the subject’s physical properties may impact the measurements and the quality of the reconstructed image. This could provide a useful tool for designing an EIT system. Special attention has been given to the current source’s output impedance and swing, to the noise produced by the circuits and to the Analog to Digital Converters (ADCs) resolution and sampling rate. Furthermore, some 3D phantom subjects have been modeled and simulated as equivalent circuits, merged with the EIT simulated hardware, in order to observe how changes on their properties interact with the whole circuitry and affect the final result. Observations show that mirrored current sources with $z_{out}$ > 350 k $\Omega$ and sufficiently high ADC acquisition sampling rate ( $f_{sample}\ge 16f_{in}$ ) can result to accurate impedance measurements and therefore quality image reconstruction within a frequency span of at least 10 to 100 kHz. Moreover, possible hardware failures (electrode disconnections and imbalanced contact impedances) can be detected with a simple examination of the first extracted image and measurement set, so that by direct modification of the reconstruction process, a corrected result can be obtained.

The wide range of unmanned aerial system (UAS) applications has led to a substantial increase in their numbers, giving rise to a whole new area of systems aiming at detecting and/or mitigating their potentially unauthorized activities. The majority of these proposed solutions for countering the aforementioned actions (C-UAS) include radar/RF/EO/IR/acoustic sensors, usually working in coordination. This work introduces a small UAS (sUAS) acoustic detection system based on an array of microphones, easily deployable and with moderate cost. It continuously collects audio data and enables (a) the direction of arrival (DOA) estimation of the most prominent incoming acoustic signal by implementing a straightforward algorithmic process similar to triangulation and (b) identification, i.e., confirmation that the incoming acoustic signal actually emanates from a UAS, by exploiting sound spectrograms using machine-learning (ML) techniques. Extensive outdoor experimental sessions have validated this system’s efficacy for reliable UAS detection at distances exceeding 70 m.

This paper presents a new analog front-end classification system that serves as a wake-up engine for digital back-ends, targeting embedded devices for epileptic seizure prediction. Predicting epileptic seizures is of major importance for the patient’s quality of life as they can lead to paralyzation or even prove fatal. Existing solutions rely on power hungry embedded digital inference engines that typically consume several µW or even mW. To increase the embedded device’s autonomy, a new approach is presented combining an analog feature extractor with an analog Gaussian mixture model-based binary classifier. The proposed classification system provides an initial, power-efficient prediction with high sensitivity to switch on the digital engine for the accurate evaluation. The classifier’s circuit is chip-area efficient, operating with minimal power consumption (180 nW) at low supply voltage (0.6 V), allowing long-term continuous operation. Based on a real-world dataset, the proposed system achieves 100% sensitivity to guarantee that all seizures are predicted and good specificity (69%), resulting in significant power reduction of the digital engine and therefore the total system. The proposed classifier was designed and simulated in a TSMC 90 nm CMOS process, using the Cadence IC suite.

Dynamic lung imaging is a major application of Electrical Impedance Tomography (EIT) due to EIT’s exceptional temporal resolution, low cost and absence of radiation. EIT however lacks in spatial resolution and the image reconstruction is very sensitive to mismatches between the actual object’s and the reconstruction domain’s geometries, as well as to the signal noise. The non-linear nature of the reconstruction problem may also be a concern, since the lungs’ significant conductivity changes due to inhalation and exhalation. In this paper, a recently introduced method of moment is combined with a sparse Bayesian learning approach to address the non-linearity issue, provide robustness to the reconstruction problem and reduce image artefacts. To evaluate the proposed methodology, we construct three CT-based time-variant 3D thoracic structures including the basic thoracic tissues and considering 5 different breath states from end-expiration to end-inspiration. The Graz consensus reconstruction algorithm for EIT (GREIT), the correlation coefficient ($CC$), the root mean square error ($RMSE$) and the full-reference ($FR$) metrics are applied for the image quality assessment. Qualitative and quantitative comparison with traditional and more advanced reconstruction techniques reveals that the proposed method shows improved performance in the majority of cases and metrics. Finally, the approach is applied to single-breath online in-vivo data to qualitatively verify its applicability.

Electrical impedance tomography is a low-cost, safe, and high temporal resolution medical imaging modality which finds extensive application in real-time thoracic impedance imaging. Thoracic impedance changes can reveal important information about the physiological condition of patients’ lungs. In this way, electrical impedance tomography can be a valuable tool for monitoring patients. However, this technique is very sensitive to measurement noise or possible minor signal errors, coming from either the hardware, the electrodes, or even particular biological signals. Thus, the design of a good performance electrical impedance tomography hardware setup which properly interacts with the tissue examined is both an essential and a challenging concept. In this paper, we adopt an extensive simulation approach, which combines the system’s analogue and digital hardware, along with equivalent circuits of 3D finite element models that represent thoracic cavities. Each thoracic finite element model is created in MATLAB based on existing CT images, while the tissues’ conductivity and permittivity values for a selected frequency are acquired from a database using Python. The model is transferred to a multiport RLC network, embedded in the system’s hardware which is simulated at LT SPICE. The voltage output data are transferred to MATLAB where the electrical impedance tomography signal sampling and digital processing is also simulated. Finally, image reconstructions are performed in MATLAB, using the EIDORS library tool and considering the signal noise levels and different electrode and signal sampling configurations (ADC bits, sampling frequency, number of taps).

This paper introduces a multiloop stabilized low-dropout regulator with a DC power supply rejection ratio of 85 dB and a phase margin of 80°. It is suitable for low-power, low-voltage and area-efficient applications since it consumes less than 100 μA. The dropout voltage is only 400 mV and the power supply rails are 1 V. Furthermore, a full mathematical analysis is conducted for stability and noise before the circuit verification. To confirm the proper operation of the implementation process, voltage and temperature corner variation simulations are extracted. The proposed regulator is designed and verified utilizing the Cadence IC Suite in a TSMC 90 nm CMOS process.