Browsing by Subject "Viterbi algorithm"

Now showing 1 - 5 of 5

Open Access
The design of finite-state machines for quantization using simulated annealing
(IEEE, 1993) Kuruoğlu, Ercan Engin; Ayanoğlu, E.
In this paper, the combinatorial optimization algorithm known as simulated annealing is used for the optimization of the trellis structure or the next-state map of the decoder finite-state machine in trellis waveform coding. The generalized Lloyd algorithm which finds the optimum codebook is incorporated into simulated annealing. Comparison of simulation results with previous work in the literature shows that this combined method yields coding systems with good performance.
Open Access
An efficient computation model for coarse grained reconfigurable architectures and its applications to a reconfigurable computer
(IEEE, 2010-07) Atak, Oğuzhan; Atalar, Abdullah
The mapping of high level applications onto the coarse grained reconfigurable architectures (CGRA) are usually performed manually by using graphical tools or when automatic compilation is used, some restrictions are imposed to the high level code. Since high level applications do not contain parallelism explicitly, mapping the application directly to CGRA is very difficult. In this paper, we present a middle level Language for Reconfigurable Computing (LRC). LRC is similar to assembly languages of microprocessors, with the difference that parallelism can be coded in LRC. LRC is an efficient language for describing control data flow graphs. Several applications such as FIR, multirate, multichannel filtering, FFT, 2D-IDCT, Viterbi decoding, UMTS and CCSDC turbo decoding, Wimax LDPC decoding are coded in LRC and mapped to the Bilkent Reconfigurable Computer with a performance (in terms of cycle count) close to that of ASIC implementations. The applicability of the computation model to a CGRA having low cost interconnection network has been validated by using placement and routing algorithms. © 2010 IEEE.
Open Access
Enhancing reliability in semantic communication: a stochastic approach to semantic-graph modeling
(2023-09) Yetim, Sadık Yağız
Semantic communication is expected to play a critical role in reducing traffic load in future intelligent large-scale sensor networks. With advances in Machine Learning (ML) and Deep Learning (DL) techniques, design of semantically-aware systems has become feasible in recent years. This thesis focuses on improving the reliability of the semantic information represented in a graph-based language that was previously developed. Inaccuracies in the representation of the semantic information can arise due to multiple factors, such as algorithmic shortcomings or sensory errors, deteriorating the performance of the semantic extractor. This thesis aims to model the temporal evolution of semantic information, represented using the graph language, to enhance its reliability. Each unique graph configuration is treated as a distinct state, leading to a Hidden Semi-Markov Model (HSMM) defined over the state space of the graph configurations. The HSMM formulation enables the integration of prior knowledge on the semantic signal into the graph sequences, enhancing the accuracy in identifying semantic innovations. Within the HSMM framework, algorithms designed for graph smoothing, semantic information fusion, and model learning are introduced. The efficacy of these algorithms in improving the reliability of the extracted semantic-graphs is demonstrated through simulations and video streams generated in the CARLA simulation environment.
Embargo
Hidden Semi-Markov Models for semantic-graph language modeling
(Elsevier Ltd, 2024-11) Yetim, Sadık Yağız; Duman, Tolga Mete; Arıkan, Orhan
Semantic communication is expected to play a critical role in reducing traffic load in future intelligent large-scale sensor networks. With advances in Machine Learning (ML) and Deep Learning (DL) techniques, design of semantically-aware systems has become feasible in recent years. This work focuses on improving the reliability of the semantic information represented in a graph-based language that has been recently proposed. Inaccuracies in the representation of the semantic information can arise due to multiple factors, such as algorithmic shortcomings or sensory errors, decreasing the performance of the semantic extractor. This study aims to model the temporal evolution of semantic information, represented using the graph language, to enhance its reliability. Each unique graph configuration is treated as a distinct state, leading to a Hidden Semi-Markov Model (HSMM) defined over the state space of the graph configurations. The HSMM formulation enables the integration of prior knowledge on the semantic signal into the graph sequences, enhancing the accuracy in identifying semantic innovations. Within the HSMM framework, algorithms designed for graph smoothing, semantic information fusion, and model learning are introduced. The efficacy of these algorithms in improving the reliability of the extracted semantic-graphs is demonstrated through simulations and video streams generated in the CARLA simulation environment.
Open Access
Low power UWB transceiver design using dynamic voltage scaling
(IEEE, 2007-03) Garg, R.; Chunjie, D.; Jinyun, Z.; Gezici, Sinan
Low power consumption is a critical issue in many UWB systems. In this paper, we investigate the application of dynamic voltage scaling (DVS) and other low power design techniques to a multiband-OFDM UWB transceiver baseband circuit design in order to reduce average power consumption of the chip. Our results show significant power savings over the conventional approach. © 2007 IEEE.