Scintillation in Liquid Xenon for Gamma-Ray Medical Imaging: From Single Time-over-Threshold to Multi-Time-over-Threshold PMT Signal Measurements.

In this paper, a new light event acquisition chain in a three-gamma liquid xenon prototype for medical nuclear imaging is presented. The prototype implements the Multi-Time-Over-Threshold (MTOT) method. This method surpasses the Single-Time-Over-Threshold (STOT) by precisely determining both the number of vacuum ultraviolet (VUV) photons detected by each photomultiplier tube (PMT) and their arrival times for light signal measurement. Based on both the experimental and simulated results, the MTOT method achieved a 70% improvement in reconstructing photoelectrons (PEs) and enhanced the precision of the arrival time estimation by 20-30% compared with STOT. These results will enable an upgrade of the XEMIS2 (Xenon Medical Imaging System) camera, improving its performance as the imaged activity increases.

Multipurpose Bio-Monitored Integrated Circuit in a Contact Lens Eye-Tracker.

We present the design, fabrication, and test of a multipurpose integrated circuit (Application Specific Integrated Circuit) in AMS 0.35 µm Complementary Metal Oxide Semiconductor technology. This circuit is embedded in a scleral contact lens, combined with photodiodes enabling the gaze direction detection when illuminated and wirelessly powered by an eyewear. The gaze direction is determined by means of a centroid computation from the measured photocurrents. The ASIC is used simultaneously to detect specific eye blinking sequences to validate target designations, for instance. Experimental measurements and validation are performed on a scleral contact lens prototype integrating four infrared photodiodes, mounted on a mock-up eyeball, and combined with an artificial eyelid. The eye-tracker has an accuracy of 0.2°, i.e., 2.5 times better than current mobile video-based eye-trackers, and is robust with respect to process variations, operating time, and supply voltage. Variations of the computed gaze direction transmitted to the eyewear, when the eyelid moves, are detected and can be interpreted as commands based on blink duration or using blinks alternation on both eyes.

Development of a new scleral contact lens with encapsulated photodetectors for eye tracking.

Most eye trackers nowadays are video-based, which allows for a relatively simple and non-invasive approach but also imposes several constraints in terms of necessary computing power and conditions of use (e.g., lighting, spectacles, etc.). We introduce a new eye tracker using a scleral lens equipped with photodiodes and an eyewear with active illumination. The direction of gaze is obtained from the weighted average of photocurrents (centroid) and communicated through an optical link. After discussing the optimum photodiodes configuration (number, layout) and associated lighting (collimated, Lambertian), we present prototypes demonstrating the high performances possibilities (0.11° accuracy when placed on an artificial eye) and wireless optical communication.

A sub-nJ CMOS ECG classifier for wireless smart sensor.

Body area sensor networks hold the promise of more efficient and cheaper medical care services through the constant monitoring of physiological markers such as heart beats. Continuously transmitting the electrocardiogram (ECG) signal requires most of the wireless ECG sensor energy budget. This paper presents the analog implantation of a classifier for ECG signals that can be embedded onto a sensor. The classifier is a sparse neural associative memory. It is implemented using the ST 65 nm CMOS technology and requires only 234 pJ per classification while achieving a 93.6% classification accuracy. The energy requirement is 6 orders of magnitude lower than a digital accelerator that performs a similar task. The lifespan of the resulting sensor is 191 times as large as that of a sensor sending all the data.

Twin Neurons for Efficient Real-World Data Distribution in Networks of Neural Cliques: Applications in Power Management in Electronic Circuits.

Associative memories are data structures that allow retrieval of previously stored messages given part of their content. They, thus, behave similarly to the human brain's memory that is capable, for instance, of retrieving the end of a song, given its beginning. Among different families of associative memories, sparse ones are known to provide the best efficiency (ratio of the number of bits stored to that of the bits used). Recently, a new family of sparse associative memories achieving almost optimal efficiency has been proposed. Their structure, relying on binary connections and neurons, induces a direct mapping between input messages and stored patterns. Nevertheless, it is well known that nonuniformity of the stored messages can lead to a dramatic decrease in performance. In this paper, we show the impact of nonuniformity on the performance of this recent model, and we exploit the structure of the model to improve its performance in practical applications, where data are not necessarily uniform. In order to approach the performance of networks with uniformly distributed messages presented in theoretical studies, twin neurons are introduced. To assess the adapted model, twin neurons are used with the real-world data to optimize power consumption of electronic circuits in practical test cases.