Future automated vehicles are likely to rely on LiDAR sensors to perform automated functions. These LiDAR sensors have the potential to interfere with each other, with this mutual interference being represented by unwanted points in the point cloud. These points could affect the quality of perception of objects, such as potentially creating false objects or obscuring real objects. Hence, it is necessary to identify interference points and remove them from the point cloud, without removing points that belong to valid objects. This paper presents a novel LiDAR mutual interference mitigation algorithm that successfully mitigates interference and potentially can be performed in real time. The number of interference points when the victim and offending LiDARs were separated by 1m was reduced from over 1000 points to under 40 points. The proposed method performed better than the current state-of-the-art techniques on most metrics and was designed to ensure that consecutive points at the same range, such as those belonging to small objects at far distances, were not permanently removed from consecutive point clouds. These results ensure a removal of interference point without affecting the accuracy of perception tasks based on LiDAR data.

This article consists of a collection of slides from the author's conference presentation on the special features, system design and architectures, processing capabilities, and targeted markets for Intel's C2000 Atom family of microserver products.

In this paper we present a novel algorithm exploiting sparse properties of support vector machines (SVM) with application to image compression. The algorithm combines SVM learning with the discrete cosine transform (DCT). Unlike a classic radial basis function (RBF) networks or multilayer perceptrons that require the topology of the network to be defined before training, an SVM selects the minimum number of training points, called support vectors, that ensure modelling of the data within the given level of accuracy (a.k.a. insensitivity zone /spl epsi/). It is this property that is exploited as the basis for an image compression algorithm. Here, the SVMs learning algorithm performs the compression in a spectral domain of DCT coefficients. Results demonstrate that even though there is an extra lossy step compared with the baseline JPEG algorithm, the new algorithm dramatically increases compression for a given image quality; conversely it increases image quality for a given compression ratio. The approach presented can be readily applied for other modelling schemes that are in a form of a sum of weighted basis functions.

We present an algorithm for the application of support vector machine (SVM) learning to image compression. The algorithm combines SVMs with the discrete cosine transform (DCT). Unlike a classic radial basis function networks or multilayer perceptrons that require the topology of the network to be defined before training, an SVM selects the minimum number of training points, called support vectors, that ensure modeling of the data within the given level of accuracy (a.k.a. insensitivity zone /spl epsi/). It is this property that is exploited as the basis for an image compression algorithm. Here, the SVMs learning algorithm performs the compression in a spectral domain of DCT coefficients, i.e., the SVM approximates the DCT coefficients. The parameters of the SVM are stored in order to recover the image. Results demonstrate that even though there is an extra lossy step compared with the baseline JPEG algorithm, the new algorithm dramatically increases compression for a given image quality; conversely it increases image quality for a given compression ratio. The approach presented can be readily applied for other modeling schemes that are in a form of a sum of weighted basis functions.

Networked workstations represent an increasingly popular distributed platform for running large parallel programs. They can present a low-cost alternative to purchasing supercomputer time or additional usable computational capability. Several capabilities are desirable in order to harness workstations, including support for a widely accepted parallel programming environment, task migration, intelligent resource allocation, fault tolerance, and totally transparent support of these features. The Hector system is designed to provide these capabilities to MPI programs. The structure of the system and experiences using the system on loaded workstations to run scientific codes are described. Copyright © 1999 John Wiley & Sons, Ltd.

General-purpose programmers have come to expect a high degree of portability among widely varying architectures. Advances in run-time systems for parallel programs have been proposed in order to harness available resources as efficiently as possible. Simultaneously, advances in algorithmic methods of dynamically balancing computational load have been proposed in order to respond to variations in actual performance and therefore in run-time. The primary mechanism for harnessing idle resources effectively, task migration, can be used alongside the primary mechanism for dynamic load balancing, data redistribution. Besides the fact that the two methods can be used simultaneously to spur further increases in performance, the run-time information-gathering infrastructure necessary to detect and use idle resources can also benefit dynamically load-balanced applications. This paper describes an architecture for and preliminary implementation of a system that combines data-parallel load balancing with task-parallel load balancing. Performance test results are included as well.

Harnessing the computational capabilities of a network of workstations promises to off-load work from overloaded supercomputers onto largely idle resources overnight. Several capabilities are needed to do this, including support for an architecture-independent parallel programming environment, task migration, automatic resource allocation, and fault tolerance. The Hector distributed run-time environment is designed to present these capabilities transparently to programmers. MPI programs can be run under this environment on homogeneous clusters with no modifications to their source code needed. The design of Hector, its internal structure, and several benchmarks and tests are presented.

In order to use networks of workstations in parallel processing applications, several schemes have been devised to allow processes on different, possibly heterogeneous, platforms to communicate with one another. The Message-Passing Interface (MPI) is one such scheme that allows for message-passing across different architectures. The MPI specification does not make provisions for the migration of a process between machines. This paper describes the work required to modify, an MPI implementation to allow for task migration. It also describes "Hector", our heterogeneous computing task allocator that is used to migrate tasks automatically and improve the overall performance of a parallel program.