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Over the Internet, bit errors within the data packets translate into packet losses at the higher layers of the OSI model, yielding a packet erasure channel. Modern erasure correcting codes promise to offer a very simple and efficient solution to data transfers over these channels, opening up also other interesting applications. Amongst them one can enumerate reliable large scale content distribution,...
A novel loss less compression algorithm known as compression by sub string enumeration (CSE) is analyzed and modified. The CSE compression algorithm is a block-based, off-line method, as is the case with enumerative codes and the block-sorting compression scheme. First, we propose an encoding model that achieves asymptotic optimality for stationary ergodic sources. The codeword length attained by...
This paper investigates the modeling of hardware interface modules in cycle accurate simulators. The implemented modules are integrated into a C++ multi-processor system-on-chip (MPSoC) simulator. The implementation of hardware interface models is driven by the following desired features: easy integration into the simulation model of the MPSoC and easy configuration. Regarding the first feature, it...
Although great achievements have been obtained in the past, recent developments towards ultra high definition video have been asking for further compression efficiency gains regarding the H.264/AVC state-of-the-art. As an answer to these needs, ITU-T and MPEG started a new standardization project called High Efficiency Video Coding. This paper extends and integrates a perceptual visual model in the...
Long-span language models that capture syntax and semantics are seldom used in the first pass of large vocabulary continuous speech recognition systems due to the prohibitive search-space of sentence-hypotheses. Instead, an N-best list of hypotheses is created using tractable n-gram models, and rescored using the long-span models. It is shown in this paper that computationally tractable variational...
This paper describes a text normalization system for deletion-based abbreviations in informal text. We propose using statistical classifiers to learn the probability of deleting a given character using features based on character context, position in the word and containing syllable, and function within the word. To ensure that our system is robust to different and previously unseen abbreviations...
In this paper, we propose a new method for computing and applying language model look-ahead in a dynamic network decoder, exploiting the sparseness of backing-off n-gram language models. Only partial (sparse) look-ahead tables are computed, with a size that depends on the number of words that have an n-gram score in the language model for a specific context, rather than a constant, vocabulary dependent...
The paper contributes to system level diagnostics by two new diagnostics algorithms for faulty units identification in regular computing systems from testing results. The developed algorithms are based on the symmetric diagnostics model at system level. Effectiveness and complexity of the implemented algorithms were evaluated by experiments over several regular computing architectures (hypercube,...
Pattern searching and retrieval plays important role in task of content-based audio analysis for requirements of media database management or in surveillance systems for detecting significant audio events and keywords. In the paper, we present algorithm for spotting audio patterns in record, using Hidden Markov Models and Viterbi decoder. Also, two types of probability normalization are presented...
In this work, we develop an experimental primate test bed for a center-out reaching task to test the performance of reinforcement learning based decoders for Brain-Machine Interfaces. Neural recordings obtained from the primary motor cortex were used to adapt a decoder using only sequences of neuronal activation and reinforced interaction with the environment. From a naïve state, the system was able...
We propose a new encoder-friendly image compression strategy for high-throughput cameras and other scenarios of resource-constrained encoders. The encoder performs ℓ∞-constrained predictive coding (DPCM coupled with uniform scalar quantizer), while the decoder solves an inverse problem of ℓ2 restoration of ℓ∞-coded images. Although designed for minimum encoder complexity, the new codec outperforms...
We propose a method to improve traditional character-based PPM text compression algorithm for natural languages. Consider a text file as a sequence of alternating words and non-words, the basic idea of our algorithm is to encode non words and prefixes of words using character-based context models and encode suffixes of words using dictionary models. By using dictionary models, the algorithm can encode...
Multi-core processor Simulation Platform is always a very important tool in modern multi-core processor design for the system-level design and evaluation. In this paper, a multi-core processor simulator is proposed by modifying Simple Scalar v3.0to simulate parallelized multi-core programs. Shared memory is used for the communication between different cores, which is the communication network among...
This paper introduces an adaptive scanning order for bitplane image coding engines, which is devised from a rate-distortion optimization perspective that uses recent advances in coefficient modeling and distortion estimation. The main idea is to always select the next coefficient to be coded so that image distortion and code stream length are minimized. The sequence of visited coefficients is adapted...
This talk intends to bring out the following ideas: Understanding decoding power is important for design of relevant codes at short distances. Just as we have models for channel to understand transmit power, we need models of decoding implementation to understand decoding power. Node model: total power diverges to infinity as Pe → 0. Optimal transmit power bounded away from Shannon limit. Wire model:...
The validation of transaction level models described in System-level Description Languages (SLDLs) often relies on extensive simulation. However, traditional Discrete Event (DE) simulation of SLDLs is cooperative and cannot utilize the available parallelism in modern multi-core CPU hosts. In this work, we study the SLDL execution semantics of concurrent threads and present a multi-core parallel simulation...
Multimedia decoders mapped onto MPSoC platforms exhibit degraded video quality when the critical system resources such as buffer and processor frequency are constrained. Hence, it is essential for system designers to find the appropriate mix of resources, living within the constraints, for a desired output video quality. A naive approach to do this would be to run expensive system simulations of the...
Decoding low-density parity-check (LDPC) codes requires a lot of computation time, particularly when bit error rates as low as 10-9 are needed. In this paper, we improve the simulation speed by making use of an inexpensive graphics processing unit (GPU). A dedicated program is written to utilize the hardware resources in the GPU to decode LDPC codes in a parallel manner. Codes with rate 1/2 and length...
The hierarchical Pitman-Yor process-based smoothing method applied to language model was proposed by Gold water and by The, the performance of this smoothing method is shown comparable with the modified Kneser-Ney method in terms of perplexity. Although this method was presented four years ago, there has been no paper which reports that this language model indeed improves translation quality in the...
Minimum Error Rate Training (MERT) as an effective parameters learning algorithm is widely applied in machine translation and system combination area. However, there exists an ambiguity problem in respect to the training goal and it is hard for MERT to tackle, that is different parameters may lead to the same minimum error rate in training but greatly different performances in testing. We propose...
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