Compares methods for choosing motion vectors for motion-compensated video compression. The primary focus is on videophone and videoconferencing applications, where very low bit rates are necessary, where the motion is usually limited, and where the frames must be coded in the order they are generated. the authors provide evidence, using established benchmark videos of this type, that choosing motion vectors to minimize codelength subject to (implicit) constraints on quality yields substantially better rate-distortion tradeoffs than minimizing notions of prediction error. They illustrate this point using an algorithm within the p/spl times/64 standard. They show that using quadtrees to code the motion vectors in conjunction with explicit codelength minimization yields further improvement. They describe a dynamic-programming algorithm for choosing a quadtree to minimize the codelength.< >
We make a case that, even with severe efficiency constraints, taking the number of bits to code each motion vector into account when estimating motion for video compression results in significantly better performance at low bit rates, using simulation studies on established benchmark image sequences. In particular, we examine an algorithm that differs from a "vanilla" implementation of the H.261 standard by choosing motion vectors to minimize a cost function of prediction error and the number of bits to code a particular motion vector, where the coefficients of the cost function are adapted on-line using the Widrow-Hoff (1960) rule. We show that this algorithm performs comparably to a variety of more idealized, computationally intensive methods we examined in earlier papers and substantially better than the original "vanila" method, which ignores the number of bits to code the motion vector when choosing it.
A new method gives compression comparable with the JPEG lossless mode, with about five times the speed. FELICS is based on a novel use of two neighboring pixels for both prediction and error modeling. For coding, the authors use single bits, adjusted binary codes, and Golomb or Rice codes. For the latter they present and analyze a provably good method for estimating the single coding parameter.< >
Finding repetitive structures in genomes is important to understand their biological functions. Many modern genomic sequence data compressors also highly rely on finding the repeats over the sequences. The notion of maximal repeats captures all the repeats in a space-efficient way. Prior works on maximal repeat finding used either a suffix tree or a suffix array along with other auxiliary data structures. Their space usage is 19-50 times as large as the text size with the best engineering efforts, prohibiting their usability on massive data such as the whole human genome. Our technique is based on the Burrows-Wheeler Transform and wavelet trees. For genomic sequences stored using one byte per base, the space usage of our method is less than double of the sequence size. Our space-efficient method keeps the timing performance fast. In fact, our method is orders of magnitude faster than the prior methods for processing massive texts such as the whole human genome, since the prior methods must use external memory. For the first time, our method enables a normal computer with 8GB internal memory (actual internal memory usage is less than 6GB) to find all the maximal repeats in the whole human genome in less than 17 hours.
<div class="section abstract"><div class="htmlview paragraph">LIDAR-based autonomous mobile robots (AMRs) are gradually being used for gas detection in industries. They detect tiny changes in the composition of the environment in indoor areas that is too risky for humans, making it ideal for the detection of gases. This current work focusses on the basic aspect of gas detection and avoiding unwanted accidents in industrial sectors by using an AMR with LIDAR sensor capable of autonomous navigation and MQ2 a gas detection sensor for identifying the leakages including toxic and explosive gases, and can alert the necessary personnel in real-time by using simultaneous localization and mapping (SLAM) algorithm and gas distribution mapping (GDM). GDM in accordance with SLAM algorithm directs the robot towards the leakage point immediately thereby avoiding accidents. Raspberry Pi 4 is used for efficient data processing and hardware part accomplished with PGM45775 DC motor for movements with 2D LIDAR allowing 360° mapping. The adoption of LIDAR-based AMRs for gas detection is expected to increase in the future, as more industries realize the benefits of this technology.</div></div>
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