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In this paper, we demonstrate an improvement in the accuracy of a low-cost smart temperature sensor, by measurement of the nonlinear curvature correction at multiple temperature references. The sensors were positioned inside a climate chamber and connected outside to a micro-controller via a network cable. The chamber temperature was increased systematically over a wide range from -20 °C to 55 °C. A set of calibration curves was produced from the best fitting second-order polynomial curves for the offset in temperature between the sensor and reference. An improvement in accuracy of ±0.15 °C is with respect to the mentioned temperature range, compared to the significantly higher value reported of ±0.5 °C by the manufacturer for similar conditions. In summary, we demonstrate a significant improvement in the calibration of a low-cost, smart sensor frequently used in research and academic projects over a useful range of temperatures.

We discuss two algorithms to determine the value and the radial profile of the photoelastic coefficient C in glass and polymer optical fibers. We conclude that C is constant over the fiber cross-sections, with exception of silica glass fibers containing a fluorine-doped depressed cladding. In the undoped and Ge-doped parts of these silica glass fibers we find a consistent value for C that is slightly larger than in bulk silica. In the fluorine-doped trenches of the absolute value of C decreases with about 27%. In polymethyl methacrylate optical fibers, the value of C significantly varies from fiber to fiber.

This paper presents a cryogenic successive approximation register (SAR) based analog to digital converter (ADC) implemented in a standard 0.35 microm complementary metal oxide semiconductor (CMOS) process. It operates from room temperature down to 4.4 K, achieving 10.47 effective number of bits (ENOB) at room temperature. At 4.4 K, the ADC achieves 8.53 ENOB at 50 kS/s sampling rate with a current consumption of 90 microA from a 3.3 V supply. The ADC utilizes an improved comparator architecture, which performs offset cancellation by using preamplifiers designed for cryogenic operation. The conventional offset cancellation algorithm is also modified in order to eliminate the effect of cryogenic anomalies below freeze-out temperature. The power efficiency is significantly improved compared to the state of the art semiconductor ADCs operating in the same temperature range.

An ECG signal processing method with quad level vector (QLV) is proposed for the ECG holter system. The ECG processing consists of the compression flow and the classification flow, and the QLV is proposed for both flows to achieve better performance with low-computation complexity. The compression algorithm is performed by using ECG skeleton and the Huffman coding. Unit block size optimization, adaptive threshold adjustment, and 4-bit-wise Huffman coding methods are applied to reduce the processing cost while maintaining the signal quality. The heartbeat segmentation and the R-peak detection methods are employed for the classification algorithm. The performance is evaluated by using the Massachusetts Institute of Technology-Boston's Beth Israel Hospital Arrhythmia Database, and the noise robust test is also performed for the reliability of the algorithm. Its average compression ratio is 16.9:1 with 0.641% percentage root mean square difference value and the encoding rate is 6.4 kbps. The accuracy performance of the R-peak detection is 100% without noise and 95.63% at the worst case with -10-dB SNR noise. The overall processing cost is reduced by 45.3% with the proposed compression techniques.

This paper discusses ultra-low-power wireless sensor nodes intended for wearable biopotential monitoring. Specific attention is given to mixed-signal design approaches and their impact on the overall system power dissipation. Examples of trade-offs in power dissipation between analog front-ends and digital signal processing are also given. It is shown how signal filtering can further reduce the internal power consumption of a node. Such power saving approaches are indispensable as real-life tests of custom wireless ECG patches reveal the need for artifact detection and correction. The power consumption of such additional features has to come from power savings elsewhere in the system as the overall power budget cannot increase.

An ECG signal processor (ESP) is proposed for the low energy wireless ambulatory arrhythmia monitoring system. The ECG processor mainly performs filtering, compression, classification and encryption. The data compression flow consisting of skeleton and modified Huffman coding is the essential function to reduce the transmission energy consumption and the memory capacity, which are the most energy consuming part. The classification flow performs the arrhythmia analysis to alert the abnormality. The proposed ESP IC is implemented in 0.18-microm CMOS process and integrated into the wireless arrhythmia monitoring sensor platform. By integration of the ESP, the total system energy reduction is evaluated by 95.6%.

The European project NeuroProbes has introduced a new methodology to allow the fine positioning of electrodes within an implantable probe with respect to individual neurons. In this approach, probes are built with a very large number of electrodes which are electronically selectable. This feature is implemented thanks to the modular approach adopted in NeuroProbes, which will allow the implementation of integrated electronics both along the probe shaft and on the array backbone.