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Table Of Integrals Series And Products

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

In this paper we use the algorithm SeqSLAM to address the question, how little and what quality of visual information is needed to localize along a familiar route? We conduct a comprehensive investigation of place recognition performance on seven datasets while varying image resolution (primarily 1 to 512 pixel images), pixel bit depth, field of view, motion blur, image compression and matching sequence length. Results confirm that place recognition using single images or short image sequences is poor, but improves to match or exceed current benchmarks as the matching sequence length increases. We then present place recognition results from two experiments where low-quality imagery is directly caused by sensor limitations; in one, place recognition is achieved along an unlit mountain road by using noisy, long-exposure blurred images, and in the other, two single pixel light sensors are used to localize in an indoor environment. We also show failure modes caused by pose variance and sequence aliasing, and discuss ways in which they may be overcome. By showing how place recognition along a route is feasible even with severely degraded image sequences, we hope to provoke a re-examination of how we develop and test future localization and mapping systems.

Vision-based place recognition : how low can you go?

Channel coding may be viewed as the best-informed and most potent component of cellular communication systems, which is used for correcting the transmission errors inflicted by noise, interference, and fading. The powerful turbo code was selected to provide channel coding for mobile broad band data in the 3G UMTS and 4G long term evolution cellular systems. However, the 3GPP standardization group has recently debated whether it should be replaced by low density parity check (LDPC) or polar codes in 5G new radio, ultimately reaching the decision to adopt the LDPC code family for enhanced mobile broad band (eMBB) data and polar codes for eMBB control. This paper summarizes the factors that influenced this debate, with a particular focus on the application specific integrated circuit (ASIC) implementation of the decoders of these three codes. We show that the overall implementation complexity of turbo, LDPC, and polar decoders depends on numerous other factors beyond their computational complexity. More specifically, we compare the throughput, error correction capability, flexibility, area efficiency, and energy efficiency of ASIC implementations drawn from 110 papers and use the results for characterizing the advantages and disadvantages of these three codes as well as for avoiding pitfalls and for providing design guidelines.

Survey of Turbo, LDPC, and Polar Decoder ASIC Implementations

Spatiotemporal bag-of-features for early wildfire smoke detection

The alternate direction method of multipliers (ADMM) approach has been recently considered for LDPC decoding. It has been approved to enhance the error rate performance compared with conventional message passing (MP) techniques in both the waterfall and error floor regions at the cost of a higher computation complexity. In this letter, a formulation of the ADMM decoding algorithm with modified computation scheduling is proposed. It increases the error correction performance of the decoding algorithm and reduces the average computation complexity of the decoding process thanks to a faster convergence. Simulation results show that this modified scheduling speeds up the decoding procedure with regards to the ADMM initial formulation while enhancing the error correction performance. This decoding speed-up is further improved when the proposed scheduling is teamed with a recent complexity reduction method detailed in Wei et al. IEEE Commun. Lett. , 2015.

Fast Converging ADMM-Penalized Algorithm for LDPC Decoding

Smart functions begin to be integrated into some camera acquisition systems for CCTV applications. In this regard, smoke detection based on the compressed video is a highly desirable functionality for the monitoring of forest that present a high risk of fire. In this paper, we propose a fast and early smoke detection method that measures the local fractal feature of smoke areas based on the Discrete Cosine Transform (DCT) coefficients. The latter are generally computed at the compression step which is concomitant in its acquisition. The two coding video standards MJPEG and MPEG2 are considered as they are widely available in the operating cameras. The novelty of our approach consists in resorting to a recursive DCT in order to improve the detection performance.

Early smoke detection in forest areas from DCT based compressed video

In this paper, we address the problem of vehicle localization in urban environments. We rely on visual odometry, calculating the incremental motion, to track the position of the vehicle and on place recognition to correct the accumulated drift of visual odometry, whenever a location is recognized. The algorithm used as a place recognition module is SeqSLAM, addressing challenging environments and achieving quite remarkable results. Specifically, we perform the long-term navigation of a vehicle based on the fusion of visual odometry and SeqSLAM. The template library for this latter is created online using navigation information from the visual odometry module. That is, when a location is recognized, the corresponding information is used as an observation of the filter. The fusion is done using the EKF and the UKF, the well-known nonlinear state estimation methods, to assess the superior alternative. The algorithm is evaluated using the KITTI dataset and the results show the reduction of the navigation errors by loop-closure detection. The overall position error of visual odometery with SeqSLAM is 0.22% of the trajectory, which is much smaller than the navigation errors of visual odometery alone 0.45%. In addition, despite the superiority of the UKF in a variety of estimation problems, our results indicate that the UKF performs as efficiently as the EKF at the expense of an additional computational overhead. This leads to the conclusion that the EKF is a better choice for fusing visual odometry and SeqSlam in a long-term navigation context.

https://hal.archives-ouvertes.fr/hal-01741007/document

Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments.

Alternate direction method of multipliers (ADMM) technique has recently been proposed for LDPC decoding. Even though it improves the error rate performance compared with traditional message passing (MP) techniques, it shows a higher computation complexity. In this article, the ADMM decoding algorithm is first described. Then, its computation complexity is analyzed. Finally, an optimized version which benefits from the multi-core processors architecture as well as the ADMM algorithm s parallelism is presented. The optimized version of the ADMM decoder can achieve up to 30 Mbps for standardized LDPC codes on a laptop x86 processor. Therefore, it could guide an efficient GPU implementation for real-time and high-throughput decoding systems requiring correction performances beyond MP-Sum Product Algorithm (SPA) capabilities.

/pdf/multicore-implementation-of-ldpc-decoders-based-on-admm-6ldmr3su25.pdf

Multicore implementation of LDPC decoders based on ADMM algorithm

The object of this work is to address impulsive noise cancellation for digital broadband power line communication scheme. This paper presents details of impulsive noise model based on its statistical properties and defined broadband OFDM technique. By adding a noise power (SINR) estimator a new adaptive noise canceller based on Matsuo algorithm is proposed. Presented simulation results show that the new proposed adaptive technique permits an improvement, not only in BER, but also in reducing processing time and so implementation complexity of the noise canceller.

Fethi Tlili

Papers

Fast Converging ADMM-Penalized Algorithm for LDPC Decoding

Early smoke detection in forest areas from DCT based compressed video

Visual Odometry and Place Recognition Fusion for Vehicle Position Tracking in Urban Environments.

Multicore implementation of LDPC decoders based on ADMM algorithm

Adaptive technique for impulsive noise cancellation in broad-band power line communication system