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.

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.

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.

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.