As CMOS technology downscales, higher noise levels, wider threshold variation, and low supply voltage will force designers to contend with high rates of soft logical errors and many defective devices. A probabilistic design framework based on Markov random fields (MRF) has been previously proposed to address dynamic fault and noise vulnerability of ultimate digital CMOS circuitry. The idea is to use additional transistors and feedback loops to achieve significant noise immunity and ensure correct logic operations at low VDD. However, the extra reliability achieved in previously published work came at a cost of high transistor counts. In this paper, we present techniques to reduce the transistor count of larger multi-level combinational circuits built within the MRF framework by using variable sharing, implied dependence and supergates. Using these techniques we show an average reduction of approximately 28% in transistor counts over a range of combinational benchmark circuits built within the MRF framework compared to the best previously published results.

As devices are scaled to the nanoscale regime, it is clear that future nanodevices will be plagued by higher soft error rates and reduced noise margins. Traditional implementations of error correcting codes (ECC) can add to the reliability of systems but can be ineffective in highly noisy operating conditions. This paper proposes an implementation of ECC based on the theory of Markov random fields (MRF). The MRF probabilistic model is mapped onto CMOS circuitry, using feedback between transistors to reinforce the correct joint probability of valid logical states. We show that our MRF approach provides superior noise immunity for memory systems that operate under highly noisy conditions

—Soft errors are going to play an increasingly critical role in logic design, as power consumption drives CMOS scaling to ever lower supply voltages and device sizes. Both of these downscaling approaches contribute to significantly increased logic signal noise variance. This paper introduces a new methodology for predicting noise probability distributions for both equilibrium and non-equilibrium logic states of advanced CMOS inverters operated at low supply voltage. These distributions are essential to computing the probability of soft errors. The radiation-induced SER is not considered here.

Over the past few years, there has been considerable interest in the application of geometric invariance to the problem of object recognition[2, 3, 4, 5, 6]. While most work has focused on the problem of discovering and characterizing new geometric invariants, several recognition systems, based on invariants have been implemented. A key example is the LEWIS[7] system. LEWIS exploits projective invariants of planar objects to enable object indexing and classification. Experience with LEWIS and its limitations, motivated the MORSE 6 The MORSE project, started in January 1994, has the goal of providing a Cq-+ environment for the implementation of a system for recognizing 3D objects based on invariant class descriptions. MORSE embodies invariant representations for geometric classes of 3D objects such as: rotational symmetry, translational symmetry and polyhedra. The architecture is designed to support image segmentation, classbased grouping, model library management and scene reasoning. The LEWIS system has also been re-implemented using the MORSE infrastructure to provide for recognition of planar objects. To motivate the architectural design, it will prove useful to review and contrust the key steps in object recognition for two geometric classes: rotational symmetry and structures repeated by translation. Then we will describe how these steps are mapped onto the implemented architecture. Both classes require the segmentation of image features from regions of interest. In MORSE, edgel segmentation is carried out using a modified Canny edge detector and connected edgel chains are linked topologically to form a connected network of boundary segments. The two classes have different feature grouping stages, as follows.

Our understanding of object recognition can address the needs of only the most stylised applications. There is no prospect of the automated motorcars of Dickmanns et al. knowing what is in front of them anytime soon; searchers for pictures of the pope kissing a baby must search on a combination of text, guesswork and patience; current vision based HCl research relies on highly structured backgrounds; and we may safely guess that the intelligence community is unlikely to be able to dispense with image analysts anytime soon. This volume contains a series of contributions that attack important problems in recognition.

The systems and concepts described in this paper document the evolution of the geometric invariance approach to object recognition over the last five years, Invariance overcomes one of the fundamental difficulties in recognising objects from images: that the appearance of an object depends on viewpoint. This problem is entirely avoided if the geometric description is unaffected by the imaging transformation. Such invariant descriptions can be measured from images without any prior knowledge of the position, orientation and calibration of the camera. These invariant measurements can be used to index a library of object models for recognition and provide a principled basis for the other stages of the recognition process such as feature grouping and hypothesis verification. Object models can be acquired directly from images, allowing efficient construction of model libraries without manual intervention.A significant part of the paper is a summary of recent results on the construction of invariants for 3D objects from a single perspective view. A proposed recognition architecture is described which enables the integration of multiple general object classes and provides a means for enforcing global scene consistency.Various criticisms of the invariant approach are articulated and addressed.

In any object recognition system a major and primary task is to associate those image features, within an image of a complex scene, that arise from an individual object. The key idea here is that a geometric class defined in 3D induces relationships in the image which must hold between points on the image outline (the perspective projection of the object). The resulting image constraints enable both identification and grouping of image features belonging to objects of that class. The classes include surfaces of revolution, canal surfaces (pipes) and polyhedra. Recognition proceeds by first recognising an object as belonging to one of the classes (for example a surface of revolution) and subsequently identifying the object (for example as a particular vase). This differs from conventional object recognition systems where recognition is generally targetted at particular objects. These classes also support the computation of 3D invariant descriptions including symmetry axes, canonical coordinate frames and projective signatures. The constraints and grouping methods are viewpoint invariant, and proceed with no information on object pose. We demonstrate the effectiveness of this class-based grouping on real, cluttered scenes using grouping algorithms developed for rotationally symmetric surfaces, canal-surfaces and polyhedra.< >

Over the last several years the concept of model-supported exploitation (MSE) has evolved to a point where relatively simple computer vision algorithms can extract significant intelligence information from aerial images in a robust and reliable manner. Information extraction is enabled by the use of detailed 3D site models which provide an extensive context for the application of image analysis algorithms. This paper reviews the basic MSE concept and illustrates the approach using three operational concepts taken from the RADIUS project, quick-look, detection and counting and focussed change detection

It is known that rotationally symmetric surfaces can be recognized from their outlines alone, using cross-ratios of bitangent intersections. A successful implementation of this technique is demonstrated using a novel bitangent finder which works on images of real scenes. The stability of the cross-ratios is reported and compared to affine invariants. The recognition technique is shown to extend to the case of straight homogeneous generalized cylinders.<>

Electrons can flow from the field plate of a silicon gate MOS, through the gate oxide, to the contacts or the channel, if the field plate is brought into avalanche. This leads to shifts in the transistor threshold voltage, and premature discharge of floating nodes. The characteristics of p-type polycrystalline silicon films in MOS avalanche have been examined. Avalanche injected electron currents in the gate oxide up to 1 mA/cm2 have been measured at 500 KHz. Trapping of electrons occurs at a distance of 100 Å from the oxide interface, and appears to depend strongly on the boron content of the gate oxide.