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 0.6 V), the overall power consumption is 72 µW. The classifier consists of two main components, the learning and the classification blocks, both of which are based on the mathematical equations of the hardware-friendly algorithm. Based on a real-world dataset, the proposed classifier achieves only 1.4% less average accuracy than a software-based implementation of the same model. Both design procedure and all post-layout simulations are conducted in the Cadence IC Suite, in a TSMC 90 nm CMOS process.

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.

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. To evaluate the proposed architecture, a comprehensive comparison with popular analog classifiers is performed, using real-life diabetes dataset. All model architectures were trained using Python and compared with the software-based classifiers. The circuit implementations were performed using the TSMC 90 nm CMOS process technology and the Cadence IC Suite was utilized for the design, schematic and post-layout simulations. The proposed classifiers achieved sensitivity of ≥ 96.7% and specificity of ≥ 89.7%.

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. METHODS: We used the results of a high-throughput Caenorhabditis elegans methicillin-resistant Staphylococcus aureus (MRSA) liquid infection assay to develop ML models for compound prioritization and quality control. RESULTS: The compound prioritization model achieved an AUC of 0.795 with a sensitivity of 81% and a specificity of 70%. When applied to a validation set of 22,768 compounds, the model identified 81% of the active compounds identified by high-throughput screening (HTS) among only 30.6% of the total 22,768 compounds, resulting in a 2.67-fold increase in hit rate. When we retrained the model on all the compounds of the HTS dataset, it further identified 45 discordant molecules classified as non-hits by the HTS, with 42/45 (93%) having known antimicrobial activity. CONCLUSION: Our ML approach can be used to increase HTS efficiency by reducing the number of compounds that need to be physically screened and identifying potential missed hits, making HTS more accessible and reducing barriers to entry.

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-step quadratic-norm ( L2) and iterative total variation ( L1) regularization techniques are exploited to solve the inverse problem. RESULTS: Simulation and experimental tests on a circular reconstruction domain show satisfactory performance in deriving conductivity distribution, achieving a Correlation Coefficient ( CC) up to 0.863 for 70 dB voltage SNR and 0.842 for 40 dB voltage SNR. The proposed methodology with L2-norm regularization provided better results than traditional iterative Gauss-Newton's approach, whereas with L1-norm regularization it showed promising performance. Moreover, 3D reconstructions on a cylindrical cavity demonstrated superior results near the electrodes' planes compared to those of the conventional linearized approach. Finally, application to EIT medical data for dynamic lung imaging successfully revealed the breath-cycle conductivity changes. CONCLUSION: The results show that the proposed method can be effective for both 2D and 3D EIT and applicable to many applications. SIGNIFICANCE: Strong conductivity variations are successfully tackled with a very good Correlation Coefficient. In contrast to conventional EIT solutions based on weak-form and linearization on small conductivity changes, the proposed method requires only one step to converge with L2-norm regularization. The proposed method with L1-norm regularization also achieves good reconstruction quality with a low number of iterations.

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. 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 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. 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.

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. MATLAB and SIMULINK simulation results present the potential capabilities of the synthesizer architectures and validate the accuracy of the developed theoretical framework.

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. The core of the radiometer is an accurate impedance measurement and automatic matching circuit of 0.05 Ω accuracy to compensate for any load variations. The radiometer permits temperature measurements with accuracy of ±0.1°K, over a tested physiological range of 28° C-40° C in saline phantoms whose electric properties match those of tissue. Because 1.5 T magnetic resonance imaging (MRI) scanners also operate at 64 MHz, we demonstrate the feasibility of integrating our radiometer with an MRI scanner to monitor RF power deposition and temperature dosimetry, obtaining coarse, spatially resolved, absolute thermal maps in the physiological range. We conclude that RF radiometry offers promise as a direct, noninvasive method of monitoring tissue heating during MRI studies and thereby providing an independent means of verifying patient-safe operation. Other potential applications include titration of hyper- and hypo-therapies.