185 research outputs found
The AXIOM software layers
AXIOM project aims at developing a heterogeneous computing board (SMP-FPGA).The Software Layers developed at the AXIOM project are explained.OmpSs provides an easy way to execute heterogeneous codes in multiple cores. People and objects will soon share the same digital network for information exchange in a world named as the age of the cyber-physical systems. The general expectation is that people and systems will interact in real-time. This poses pressure onto systems design to support increasing demands on computational power, while keeping a low power envelop. Additionally, modular scaling and easy programmability are also important to ensure these systems to become widespread. The whole set of expectations impose scientific and technological challenges that need to be properly addressed.The AXIOM project (Agile, eXtensible, fast I/O Module) will research new hardware/software architectures for cyber-physical systems to meet such expectations. The technical approach aims at solving fundamental problems to enable easy programmability of heterogeneous multi-core multi-board systems. AXIOM proposes the use of the task-based OmpSs programming model, leveraging low-level communication interfaces provided by the hardware. Modular scalability will be possible thanks to a fast interconnect embedded into each module. To this aim, an innovative ARM and FPGA-based board will be designed, with enhanced capabilities for interfacing with the physical world. Its effectiveness will be demonstrated with key scenarios such as Smart Video-Surveillance and Smart Living/Home (domotics).Peer ReviewedPostprint (author's final draft
3D LiDAR Point Cloud Processing Algorithms
In the race for autonomous vehicles and advanced driver assistance systems (ADAS), the automotive industry has energetically pursued research in the area of sensor suites to achieve such technological feats. Commonly used autonomous and ADAS sensor suites include multiples of cameras, radio detection and ranging (RADAR), light detection and ranging (LiDAR), and ultrasonic sensors. Great interest has been generated in the use of LiDAR sensors and the value added in an automotive application. LiDAR sensors can be used to detect and track vehicles, pedestrians, cyclists, and surrounding objects. A LiDAR sensor operates by emitting light amplification by stimulated emission of radiation (LASER) beams and receiving the reflected LASER beam to acquire relevant distance information. LiDAR reflections are organized in a three-dimensional environment known as a point cloud. A major challenge in modern autonomous automotive research is to be able to process the dimensional environmental data in real time. The LiDAR sensor used in this research is the Velodyne HDL 32E, which provides nearly 700,000 data points per second. The large amount of data produced by a LiDAR sensor must be processed in a highly efficient way to be effective. This thesis provides an algorithm to process the LiDAR data from the sensors user datagram protocol (UDP) packet to output geometric shapes that can be further analyzed in a sensor suite or utilized for Bayesian tracking of objects. The algorithm can be divided into three stages: Stage One - UDP packet extraction; Stage Two - data clustering; and Stage Three - shape extraction. Stage One organizes the LiDAR data from a negative to a positive vertical angle during packet extraction so that subsequent steps can fully exploit the programming efficiencies. Stage Two utilizes an adaptive breakpoint detector (ABD) for clustering objects based on a Euclidean distance threshold in the point cloud. Stage Three classifies each cluster into a shape that is either a point, line, L-shape, or a polygon using principal component analysis and shape fitting algorithms that have been modified to take advantage of the pre-organized data from Stage One. The proposed algorithm was written in the C language and the runtime was tested on a two Windows equipped machines where the algorithm completed the processing, on average, sparing 30% of the time between UDP data packets sent from the HDL32E. In comparison to related research, this algorithm performed over seven hundred and thirty-seven times faster
An Intelligent Architecture Based on Field Programmable Gate Arrays Designed to Detect Moving Objects by Using Principal Component Analysis
This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices
Recent Advances in Embedded Computing, Intelligence and Applications
The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems
Exploitation of signal information for mobile speed estimation and anomaly detection
Although the primary purpose of the signal received by amobile handset or smartphone is to
enable wireless communication, the information extracted can be reused to provide a number
of additional services. Two such services discussed in this thesis are: mobile speed estimation
and signal anomaly detection. The proposed algorithms exploit the propagation environment
specific information that is already imprinted on the received signal and therefore do not
incur any additional signalling overhead. Speed estimation is useful for providing navigation
and location based services in areas where global navigation satellite systems (GNSS) based
devices are unusable while the proposed anomaly detection algorithms can be used to locate
signal faults and aid spectrum sensing in cognitive radio systems.
The speed estimation algorithms described within this thesis require a receiver with at least
two antenna elements and a wideband radio frequency (RF) signal source. The channel transfer
function observed at the antenna elements are compared to yield an estimate of the device
speed. The basic algorithm is a one-dimensional and unidirectional two-antenna solution.
The speed of the mobile receiver is estimated from a knowledge of the fixed inter-antenna
distance and the time it takes for the trailing antenna to sense similar channel conditions previously
observed at the leading antenna. A by-product of the algorithm is an environment
specific spatial correlation function which may be combined with theoretical models of spatial
correlation to extend and improve the accuracy of the algorithm. Results obtained via
computer simulations are provided.
The anomaly detection algorithms proposed in this thesis highlight unusual signal features
while ignoring events that are nominal. When the test signal possesses a periodic frame
structure, Kullback-Leibler divergence (KLD) analysis is employed to statistically compare
successive signal frames. A method of automatically extracting the required frame period
information from the signal is also provided. When the signal under test lacks a periodic
frame structure, information content analysis of signal events can be used instead. Clean
training data is required by this algorithm to initialise the reference event probabilities. In
addition to the results obtained from extensive computer simulations, an architecture for
field-programmable gate array (FPGA) based hardware implementations of the KLD based
algorithm is provided. Results showing the performance of the algorithms against real test
signals captured over the air are also presented.
Both sets of algorithms are simple, effective and have low computational complexity – implying
that real-time implementations on platforms with limited processing power and energy
are feasible. This is an important quality since location based services are expected to be an
integral part of next generation cognitive radio handsets
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