Machine learning-assisted high-throughput screening for Anti-MRSA compounds.

Background: Antimicrobial resistance is a major public health threat, and new agents are needed. Computational approaches have been proposed to reduce the cost and time needed for compound screening.

Aims: A machine learning (ML) model was developed for the in silico screening of low molecular weight molecules.

Low-Power Analog Integrated Architecture of the Voting Classification Algorithm for Diabetes Disease Prediction.

A low-power (∼ 600nW), fully analog integrated architecture for a voting classification algorithm is introduced. It can effectively handle multiple-input features, maintaining exceptional levels of accuracy and with very low power consumption. The proposed architecture is based on a versatile Voting algorithm that selectively incorporates one of three key classification models: Bayes or Centroid, or, the Learning Vector Quantization model; all of which are implemented using Gaussian-likelihood and Euclidean distance function circuits, as well as a current comparison circuit.

A Low-Power Analog Integrated Implementation of the Support Vector Machine Algorithm with On-Chip Learning Tested on a Bearing Fault Application.

A novel analog integrated implementation of a hardware-friendly support vector machine algorithm that can be a part of a classification system is presented in this work. The utilized architecture is capable of on-chip learning, making the overall circuit completely autonomous at the cost of power and area efficiency. Nonetheless, using subthreshold region techniques and a low power supply voltage (at only 0.

Nanopower Integrated Gaussian Mixture Model Classifier for Epileptic Seizure Prediction.

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.

A Point-Matching Method of Moment with Sparse Bayesian Learning Applied and Evaluated in Dynamic Lung Electrical Impedance Tomography.

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.

Magnetic Field Sensors' Calibration: Algorithms' Overview and Comparison.

The calibration of three-axis magnetic field sensors is reviewed. Seven representative algorithms for in-situ calibration of magnetic field sensors without requiring any special piece of equipment are reviewed. The algorithms are presented in a user friendly, directly applicable step-by-step form, and are compared in terms of accuracy, computational efficiency and robustness using both real sensors' data and artificial data with known sensor's measurement distortion.

An Efficient Point-Matching Method-of-Moments for 2D and 3D Electrical Impedance Tomography Using Radial Basis Functions.

Objective: The inverse problem of computing conductivity distributions in 2D and 3D objects interrogated by low-frequency electrical signals, which is called Electrical Impedance Tomography (EIT), is treated using a Method-of-Moment technique.

Methods: A Point-Matching-Method-of-Moment technique is used to formulate a global integral equation solver. Radial Basis Functions are adopted to express the conductivity distribution.

Single-Bit All-Digital Frequency Synthesis Using Homodyne Sigma-Delta Modulation.

All-digital frequency synthesis using bandpass sigma-delta modulation to achieve spectrally clean single-bit output is presented and mathematically analyzed resulting in a complete model to predict the stability and output spectrum. The quadrature homodyne filter architecture is introduced resulting in efficient implementations of carrier-frequency-centered bandpass filters for the modulator. A multiplierless version of the quadrature homodyne filter architecture is also introduced to reduce complexity while maintaining a clean in-band spectrum.

Exact spectrum and time-domain output of flying-adder frequency synthesizers.

The spectrum and time-domain output of the flying- adder frequency synthesizer are derived analytically. The amplitude and phase of the average-frequency component are derived in closed forms. The theoretical results are verified by spectral measurements of an FPGA implementation and by numerical simulation.

Absolute Temperature Monitoring Using RF Radiometry in the MRI Scanner.

Temperature detection using microwave radiometry has proven value for noninvasively measuring the absolute temperature of tissues inside the body. However, current clinical radiometers operate in the gigahertz range, which limits their depth of penetration. We have designed and built a noninvasive radiometer which operates at radio frequencies (64 MHz) with ∼100-kHz bandwidth, using an external RF loop coil as a thermal detector.

Diophantine frequency synthesis.

A methodology for fine-step, fast-hopping, low-spurs phase-locked loop based frequency synthesis is presented. It uses mathematical properties of integer numbers and linear Diophantine equations to overcome the constraining relation between frequency step and phase-comparator frequency that is inherent in conventional phase-locked loop based frequency synthesis. The methodology leads to fine-step, fast-hopping, modular-structured frequency synthesizers with potentially very low spurs, especially in the vicinity of the carrier.