| 1. |
- Li, Fei, et al.
(författare)
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A quantum search based signal detection for MIMO-OFDM systems
- 2011
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Ingår i: 18th International Conference on Telecommunications, ICT 2011. - : IEEE. - 9781457700248 - 9781457700255 ; , s. 276-281
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Konferensbidrag (refereegranskat)abstract
- Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) is considered as candidates for future broadband wireless services. In this paper a novel signal detection scheme based on Grover's quantum search algorithm is proposed for MIMO-OFDM systems. Grover's quantum search algorithm is based on the concept and principles of quantum computing, such as quantum bit, quantum register and quantum parallelism. An analysis is given to the theoretical basis of Grover's algorithm and the performance of Grover's algorithm is evaluated. A novel signal detector based on Grover's algorithm (GD) for MIMO-OFDM system is proposed. The simulation results show that the proposed detector has more powerful properties in bit error rate than MMSE detector and VBLAST-MMSE detector. The performance of the proposed GD detector is close to optimal when the failure probability is 0.001. When the failure probability is 0.00001, the performance of GD detector declines. In this case, our proposed improved Grover's algorithm based detector is still close to the optimal ML detector. The complexity of GD and IGD is O(√N). It's much better than classical ML detector which complexity is O(N).
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| 2. |
- Li, Fei, et al.
(författare)
-
A novel neural network optimized by quantum genetic algorithm for signal detection in MIMO-OFDM systems
- 2011
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Ingår i: Computational Intelligence in Control and Automation (CICA). - : IEEE. - 9781424499038 - 9781424499021 ; , s. 170-177
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Konferensbidrag (refereegranskat)abstract
- Neural networks can easily fall into a local extremum and have slow convergence rate. Quantum Genetic Algorithm (QGA) has features of small population size and fast convergence. Based on the investigation of QGA, we propose a novel neural network model, Radial Basis Function (RBF) networks optimized by Quantum Genetic Algorithm (QGA-RBF model). Then we investigate the performance of the proposed QGA-RBF on solving MIMO-OFDM signal detection problem. A novel signal detector based on QGA-RBF for MIMO-OFDM system is also proposed. The simulation results show that the proposed detector has more powerful properties in bit error rate than QGA based detector, RBF based detector and MMSE algorithm based detector, namely a 4-6 dB gain in performance can be achieved. The performance of the proposed detector is closer to optimal, compared with the other detectors.
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| 3. |
- Li, Fei, et al.
(författare)
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Cognitive radio spectrum sharing using improved Quantum Genetic Algorithm
- 2011
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Ingår i: 2011 International Conference on Wireless Communications and Signal Processing, WCSP 2011. - : IEEE. - 9781457710100 - 9781457710094
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Konferensbidrag (refereegranskat)abstract
- A key challenge in cognitive radios (CR) network is how to adaptively and efficiently allocate transmission powers and spectrum among CR users according to the surrounding environment. In this paper, we propose a novel spectrum sharing based on our improved Quantum Genetic Algorithm (QGA) in a non-cooperative game for CR network. We improved the original QGA by quantum crossover operator, in order to overcome the shortcoming of the original QGA easily falling into a local extremum when used to optimize the continuous functions with many extrema. We used our improved QGA as a competitive strategy and conducted several simulations in two-user system and multi-user system. From simulation results, it is evident that the proposed improved QGA based spectrum sharing scheme has better convergence rate and higher sum capacity than GA based scheme even in multi-user CR system, namely up to 2bit/s/Hz increase in capacity. The simulation results also show that the population size of QGA affect sum capacity of cognitive radio system. The results demonstrated the effectiveness and the applicability of QGA in spectrum sharing in multi-user cognitive radio system.
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| 4. |
- Li, Fei, et al.
(författare)
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Improved quantum genetic algorithm for competitive spectrum sharing in cognitive radios
- 2012
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Ingår i: 2011 International Conference in Electrics, Communication and Automatic Control Proceedings. - New York, NY : Springer. - 9781441988485 - 9781441988492 ; , s. 1203-1209
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Konferensbidrag (refereegranskat)abstract
- This chapter investigates a kind of whole new approach to spectrum sharing in cognitive radio (CR) system, quantum-inspired approach. First, we improved Quantum Genetic Algorithm (QGA) by adding quantum crossover operator to overcome the shortcomings of the original QGA, easily falling into a local extremum when used to optimize the continuous functions with many extrema. We then propose a novel spectrum sharing scheme in noncooperative game for CR system based on our improved QGA. We use QGA as competitive strategies and provide simulation results that confirm the proposed schemes have better convergence rate and higher sum capacity than genetic algorithm (GA)-based scheme, namely at most 1 bit/s/Hz increase in capacity. The results demonstrate the effectiveness and the applicability of QGA in spectrum sharing in CR system.
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| 5. |
- Abu-Hamam, Anas, 1977-, et al.
(författare)
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Understanding Web Users Behaviour From A Web Video Camera
- 2003
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- To develop a Real-Time system, which can capture and track the web user’s head motion. Based on the head motion information, the system should be able to identify whether the user is interested in the current homepage or not. A future system property would be to find out in which area of the homepage the user’s interest is moving.
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| 6. |
- Cheng, Chunling, et al.
(författare)
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High speed data streams clustering algorithm based on improved SS tree
- 2012
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Ingår i: Innovative Computing Information and Control Express Letters, Part B. - : ICIC International. - 2185-2766. ; 3:1, s. 207-212
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Tidskriftsartikel (refereegranskat)abstract
- In high speed networks, data streams show rapid, bursting and continuous characteristics, which makes real-time clustering of data streams be a difficulty. An improved SS tree structure is designed in this paper to keep the summarized information of data streams. Then, a high speed data streams clustering algorithm based on improved SS tree is proposed. In order to process the bursting streams in time, caching and piggyback mechanisms are used. The chaining buffers in the improved SS tree are used to temporarily store the data stream objects which cannot be processed immediately, and then the contents in buffers will be piggybacked together with the following data. To meet high arrival of data streams, two-phase clustering framework is adopted. Pre-aggregation phase produces local micro-clusters. After that, local micro-clusters take part in the global clustering phase based on the improved SS tree. Experimental results show that the proposed algorithm has better clustering accuracy in high-speed networks. The improved SS tree can effectively cluster high speed data streams and has a good applicability.
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| 7. |
- Georgakis, Apostolos, 1975-, et al.
(författare)
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Document distances using the Zipf distribution and a novel metric
- 2003
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- A novel metric is proposed in the present report for the evaluation of the goodness-of-fit criterion between the distribution functions of two samples. We extend the usage of the proposed criterion for the case of the generalized Zipf distribution. Detailed mathematical analysis of the proposed metric, which is embodied in a hypothesis testing, is also provided.
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| 8. |
- Georgakis, Apostolos, 1975-, et al.
(författare)
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Web prefetching through automatic categorization
- 2004
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The present report provides a novel transparent and speculative algorithm for content based web page prefetching. The proposed algorithm relies on a user profile that is dynamically generated when the user is browsing the Internet and is updated over time. The objective is to reduce the user perceived latency by anticipating future actions. In doing so the adaboost algorithm is used in order to automatically annotate the outbound links of a page to a predefined set of “labels”. Afterwards, the links that correspond to labels relevant to the user’s preferences are pre-fetched in an effort to reduce the perceived latency when the user is surfing the Internet. A comparison between the proposed algorithm against two other pre-fetching algorithms yield improved cache-hit rates given a moderate bandwidth overhead.
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| 9. |
- Källberg, David, 1982-, et al.
(författare)
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Statistical modeling for image matching in large image databases
- 2011
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Ingår i: 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing. - : IEEE. - 9780769545806 ; , s. 648-652
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Konferensbidrag (refereegranskat)abstract
- Matching a query (reference) image to an image extracted from a database containing (possibly) transformed image copies is an important retrieval task. In this paper we present a general method based on matching densities of the corresponding image feature vectors by using the Bregman distances. We consider statistical estimators for some quEDratic entropy-type characteristics. In particular, the quEDratic Bregman distances can be evaluated in image matching problems whenever images are modeled by random feature vectors in large image databases. Moreover, this method can be used for average case analysis for optimization of joining large databases. © 2011 IEEE.
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