Low energy clustering in BAN based on fuzzy simulated evolutionary computation

© 2015 ICST. A low energy clustering method of body area networks based on fuzzy simulated evolutionary computation is proposed in this paper. To reduce communication energy consumption, we also designed a fuzzy controller to dynamically adjust the crossover and mutation probability. Simulations are conducted by using the proposed method, the clustering methods based on the particle swarm optimization and the method based on the quantum evolutionary algorithm. Results show that the energy consumption of the proposed method decreased compared with the other two methods, which means that the proposed method significantly improves the energy efficiency

Energy efficient duty cycle design based on quantum immune clonal evolutionary algorithm in body area networks

© 2015 ICST. Duty cycle design is an important topic in body area networks. As small sensors are equipped with the limited power source, the extension of network lifetime is generally achieved by reducing the network energy consumption, for instance through duty cycle schemes. However, the duty cycle design is a highly complex NP-hard problem and its computational complexity is too high with exhaustive search algorithm for practical implementation. In order to extend the network lifetime, we proposed a novel quantum immune clonal evolutionary algorithm (QICEA) for duty cycle design while maintaining full coverage in the monitoring area. The QICEA is tested, and a performance comparison is made with simulated annealing (SA) and genetic algorithm (GA). Simulation results show that compared to the SA and the GA, the proposed QICEA can extending the lifetime of body area networks and enhancing the energy efficiency effectively

Modified elite chaotic artificial fish swarm algorithm for PAPR reduction in OFDM systems

© 2014 IEEE. Orthogonal frequency division multiplexing (OFDM) is a leading technology in the field of broadband wireless communications. In OFDM systems, a high peak-to-average power ratio (PAPR) is a critical issue, which may cause a nonlinear distortion and reduce power efficiency. To reduce the PAPR, partial transmit sequences (PTS) technique can be applied to the transmit data. However, the phase factor sequence selection in PTS technique is a non-linear optimization problem and it suffers from high complexity and memory use when there is a large number of non-overlapping sub-blocks in one symbol. In this paper a novel modified elite chaotic artificial fish swarm algorithm for PTS method (MECAFSA-PTS) is proposed to generate the optimum phase factors. The MECAFSA-PTS method is evaluated with extensive simulations and its performance is compared with quantum evolutionary and selective mapping algorithms. Our results show that the proposed MECAFSA-PTS algorithm is efficient in PAPR reduction

A Modified Shuffled Frog Leaping Algorithm for PAPR Reduction in OFDM Systems

© 2015 IEEE. Significant reduction of the peak-to-average power ratio (PAPR) is an implementation challenge in orthogonal frequency division multiplexing (OFDM) systems. One way to reduce PAPR is to apply a set of selected partial transmission sequence (PTS) to the transmit signals. However, PTS selection is a highly complex NP-hard problem and the computational complexity is very high when a large number of subcarriers are used in the OFDM system. In this paper, we propose a new heuristic PTS selection method, the modified chaos clonal shuffled frog leaping algorithm (MCCSFLA). MCCSFLA is inspired by natural clonal selection of a frog colony, it is based on the chaos theory. We also analyze MCCSFLA using the Markov chain theory and prove that the algorithm can converge to the global optimum. Simulation results show that the proposed algorithm achieves better PAPR reduction than using others genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms

Pseudogap, Superconducting Energy Scale, and Fermi Arcs in Underdoped Cuprate Superconductors

Through the measurements of magnetic field dependence of specific heat in $La_{2-x}Sr_xCuO_4$ in zero temperature limit, we determined the nodal slope $v_\Delta$ of the quasiparticle gap. It is found that $v_\Delta$ has a very similar doping dependence of the pseudogap temperature $T^*$ or value $\Delta_p$. Meanwhile the virtual maximum gap at ($\pi,0$) derived from $v_\Delta$ is found to follow the simple relation $\Delta_q=0.46k_BT^*$ upon changing the doping concentration. This strongly suggests a close relationship between the pseudogap and superconductivity. It is further found that the superconducting transition temperature is determined by both the residual density of states of the pseudogap phase and the nodal gap slope in the zero temperature limit, namely, $T_c \approx \beta v_\Delta \gamma_n(0)$, where $\gamma_n(0)$ is the extracted zero temperature value of the normal state specific heat coefficient which is proportional to the size of the residual Fermi arc $k_{arc}$. This manifests that the superconductivity may be formed by forming a new gap on the Fermi arcs near nodes below $T_c$. These observations mimic the key predictions of the SU(2) slave boson theory based on the general resonating-valence-bond (RVB) picture.Comment: 6 pages, 6 figures, to be published in Phys. Rev.

HII region G46.5-0.2: the interplay between ionizing radiation, molecular gas and star formation

HII regions are particularly interesting because they can generate dense layers of gas and dust, elongated columns or pillars of gas pointing towards the ionizing sources, and cometary globules of dense gas, where triggered star formation can occur. Understanding the interplay between the ionizing radiation and the dense surrounding gas is very important to explain the origin of these peculiar structures, and hence to characterize triggered star formation. G46.5-0.2 (G46), a poorly studied galactic HII region located at about 4 kpc, is an excellent target to perform this kind of studies. Using public molecular data extracted from the Galactic Ring Survey (13CO J=1-0) and from the James Clerk Maxwell Telescope data archive (12CO, 13CO, C18O J=3-2, HCO+ and HCN J=4-3), and infrared data from the GLIMPSE and MIPSGAL surveys, we perform a complete study of G46, its molecular environment and the young stellar objects placed around it. We found that G46, probably excited by an O7V star, is located close to the edge of the GRSMC G046.34-00.21 molecular cloud. It presents a horse-shoe morphology opening in direction of the cloud. We observed a filamentary structure in the molecular gas likely related to G46 and not considerable molecular emission towards its open border. We found that about 10' towards the southwest of G46 there are some pillar-like features, shining at 8 um and pointing towards the HII region open border. We propose that the pillar-like features were carved and sculpted by the ionizing flux from G46. We found several young stellar objects likely embedded in the molecular cloud grouped in two main concentrations: one, closer to the G46 open border consisting of Class II type sources, and other one mostly composed by Class I type YSOs located just ahead the pillars-like features, strongly suggesting an age gradient in the YSOs distribution.Comment: Accepted for publication in The Astronomical Journal (April 14, 2015). Some figures were degraded to reduce file siz

Concurrent bandits and cognitive radio networks

We consider the problem of multiple users targeting the arms of a single multi-armed stochastic bandit. The motivation for this problem comes from cognitive radio networks, where selfish users need to coexist without any side communication between them, implicit cooperation or common control. Even the number of users may be unknown and can vary as users join or leave the network. We propose an algorithm that combines an $\epsilon$-greedy learning rule with a collision avoidance mechanism. We analyze its regret with respect to the system-wide optimum and show that sub-linear regret can be obtained in this setting. Experiments show dramatic improvement compared to other algorithms for this setting