This paper reports a 176×144-pixel smart image sensor designed and fabricated in a 0.35µm CMOS-OPTO process. The chip implements a massively parallel focal-plane processing array which can output different simplified representations of the scene at very low power. The array is composed of pixel-level processing elements which carry out analog image processing concurrently with photosensing. These processing elements can be grouped into fully-programmable rectangular-shape areas by loading the appropiate interconnection patterns into the registers at the edge of the array. The targeted processing can be thus performed block-wise. Readout is done pixel-by-pixel in a random access fashion. On-chip 8b ADC is provided. The image processing primitives implemented by the chip, experimentally tested and fully functional, are scale space and Gaussian pyramid generation, fully-programmable multiresolution scene representation -including foveation -and block-wise energy-based scene representation. The power consumption associated to the capture, processing and A/D conversion of an image flow at 30fps, with full-frame processing but reduced frame size output, ranges from 2.7mW to 5.6mW, depending on the operation to be performed.

This paper describes a system capable of detecting smoke at the very beginning of a forest fire with a precise spatial resolution. The system is based on a wireless vision sensor network. Each sensor monitors a small area of vegetation by running on-site a tailored vision algorithm to detect the presence of smoke. This algorithm examines chromaticity changes and spatio-temporal patterns in the scene that are characteristic of the smoke dynamics at early stages of propagation. Processing takes place at the sensor nodes and, if that is the case, an alarm signal is transmitted through the network along with a reference to the location of the triggered zone -without requiring complex GIS systems. This method improves the spatial resolution on the surveilled area and reduces the rate of false alarms. An energy efficient implementation of the sensor/processor devices is crucial as it determines the autonomy of the network nodes. At this point, we have developed an ad hoc vision algorithm, adapted to the nature of the problem, to be integrated into a single-chip sensor/processor. As a first step to validate the feasibility of the system, we applied the algorithm to smoke sequences recorded with commercial cameras at real-world scenarios that simulate the working conditions of the network nodes. The results obtained point to a very high reliability and robustness in the detection process.

This paper presents an application-specific integrated circuit (ASIC) aimed for an alternative design of a digital 3-D magnetometer for space applications, with a significant reduction in mass and volume while maintaining a high sensitivity. The proposed system uses magnetic field sensors based on anisotropic magnetoresistances and a rad-hard mixed-signal ASIC designed in a standard 0.35 µm CMOS technology. The ASIC performs sensor-signal conditioning and analogue-to-digital conversion, and handles calibration tasks, system configuration, and communication with the outside. The proposed system provides high sensitivity to low magnetic fields, down to 3 nT, while offering a small and reliable solution under extreme environmental conditions in terms of radiation and temperature.Index Terms-Aerospace electronics, anisotropic magnetoresistance (AMR), CMOS mixed-signal application-specific integrated circuit (ASIC), magnetometer, radiation hardened by design (RHBD).