Nikolaos Uzunoglu

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.

This paper introduces an analog integrated fractional order type-2 fuzzy PID control system. Current approaches frequently depend on energy-intensive embedded digital systems, consuming substantial energy levels ranging from a few μW to mW. To address this limitation we propose a fully analog design offering insights into the potential of analog circuits for powerefficient robust control in complex and uncertain environments. It consists of Gaussian function, min/max, Operational transcoductance amplifier circuits and Resistor-Capacitor networks for the implementation of the fractional-order components. Crafted for operation under a reduced voltage supply (0.6 V), the controller attains minimal power usage (861.8 nW), facilitating uninterrupted, extended-term functioning. Post-layout simulation results confirm the proper operation of the proposed design. The proposed system is designed and simulated using the Cadence IC Suite in a TSMC 90 nm CMOS process.

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.

A low-cost 1 kHz–400 kHz operating frequency fully-active electrode bioimpedance measurement module, based on Howland current source, is presented in this paper. It includes a buffered positive feedback Howland current source, implemented with operational amplifiers, as well as an AD8421 instrumentation amplifier, for the differential voltage measurements. Each active electrode module can be connected to others, assembling a wearable active electrode module array. From this array, 2 electrodes can be selected to be driven from a THS413 fully differential amplifier, activating a mirrored Howland current source. This work performs a complete circuit analysis, verified with MATLAB and SPICE simulations of the current source’s transconductance and output impedance over the frequency range between 1 kHz and 1 MHz. Resistors’ tolerances, possible mismatches, and the operational amplifiers’ non-idealities are considered in both the analysis and simulations. A comparison study between four selected operational amplifiers (ADA4622, OPA2210, AD8034, and AD8672) is additionally performed. The module is also hardware-implemented and tested in the lab for all four operational amplifiers and the transconductance is measured for load resistors of 150 Ω, 660 Ω, and 1200 Ω. Measurements showed that, using the AD8034 operational amplifier, the current source’s transconductance remains constant for frequencies up to 400 KHz for a 150 Ω load and 250 kHz for a 1200 Ω load, while lower performance is achieved with the other 3 operational amplifiers. Finally, transient simulations and measurements are performed at the AD8421 output for bipolar measurements on the 3 aforementioned load resistor values.

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).

The induction of acoustic-mechanical oscillations to virus particles by illuminating them with microwave signals is analyzed theoretically. Assuming the virus particle being of spherical shape, its capsid consisting primarily of glycoproteins, a viscous fluid model is adopted while the outside medium of the sphere is taken to be ideal fluid. The electrical charge distribution of virus particle is assumed to be spherically symmetric with a variation along the radius. The generated acoustic-mechanical oscillations are computed by solving a boundary value problem analytically, making use of the Green’s function approach. Resonance conditions to achieve maximum energy transfer from microwave radiation to acoustic oscillation to the particle is investigated. Estimation of the feasibility of the technique to compete virus epidemics either for sterilization of spaces and/or use for future therapeutic applications is examined briefly.

A microwave free space reflectometry technique with swept frequency measurements for the profiling of wall structures and the detection of hidden (covered) layers has been applied to the Hagia Sophia byzantine monument. Experimental measurement results are presented and compared with three-dimensional (3D) simulated results, exhibiting fair agreement in some (though not all) aspects. Based on the experimental results, the possibility of clear discrimination between regions with and without covered mosaic layers, and hence the detection of such layers, is demonstrated.