This paper presents an 18 bit 5 MS/s SAR ADC. It has a dynamic range of 100.2 dB, SNR of 99 dB, INL of ±2 ppm and DNL of ±0.4 ppm. It has currently the lowest noise floor of any monolithic Nyquist converter relative to the full scale input (21.9 nV/√Hz, ±5V full scale) known to the author, all of this is achieved with an ADC core power of 30.52 mW giving a Schreier figure of merit of 179.3 dB [1]. Architectural choices such as the use of a residue amplifier are outlined that enable the high sample rate, low noise and power efficiency. The design is implemented on 0.18 μm CMOS with MIM capacitors and both 1.8 V and 5 V MOS devices. An LVDS interface is used to transfer the ADC result off chip.

This paper presents a new simulation and processing methodology based on open source tools to produce high fidelity Synthetic Aperture Radar (SAR) simulations of ground vehicles of varying types, as well as analysis of an applied Automatic Target Recognition (ATR) technique. This work is based around the RaySAR open source model and the outputs have been configured for both monostatic and bistatic geometries. Input CAD models of various military and civilian vehicles are used to produce the SAR imagery. This output imagery was then used to train a Tiny You Only Look Once (YOLO) Convolutional Neural Net (CNN) classifier. The classification success of the CNN applied was showed to produce significantly accurate results and the whole pipeline of processing enabled rapid evaluation of potential ATR methods against targets of choice.

The unobtrusive monitoring of vital signals and behaviour can be used to gather intelligence to support the care of people living with dementia. This can provide insights into the persons wellbeing and the neurogenerative process, as well as enable them to continue to live safely at home, thereby improving their quality of life. Within this context, this study investigated the deployability of non-contact respiration rate (RR) measurement based on an Ultra-Wideband (UWB) radar System-on-Chip (SoC). An algorithm was developed to simultaneously and continuously extract the respiration signal, together with the confidence level of the respiration signal and the target position, without needing any prior calibration. The radar-measured RR results were compared to the RR results obtained from a ground truth measure based on the breathing sound, and the error rates were within 8% with a mean value of 2.4%. The target localisation results match to the radarto-chest distances with a mean error rate of 5.4%. The tested measurement range was up to 5m. The results suggest that the algorithm could perform sufficiently well in non-contact stationary respiration rate detection.

A complex switched capacitor sigma-delta ADC is described. The ADC is used in the VLIF RX path of a GSM/GPRS/EDGE phone and has an SNDR of 90dB in a 180kHz bandwidth, with the VLIF centered at 123kHz. The use of a complex noise transfer function allows for a more optimal use of noise shaping. The ADC is 2 nd order, 1-bit, with a sampling rate of 52MHz implemented in 90nm CMOS.