186 research outputs found
Incorporate ACO routing algorithm and mobile sink in wireless sensor networks
Today, science and technology is developing, particularly the internet of things (IoT), there is an increasing demand in the sensor field to serve the requirements of individuals within modern life. Wireless sensor networks (WSNs) was created to assist us to modernize our lives, saving labor, avoid dangers, and that bring high efficiency at work. There are many various routing protocols accustomed to increase the ability efficiency and network lifetime. However, network systems with one settled sink frequently endure from a hot spots issue since hubs close sinks take a lot of vitality to forward information amid the transmission method. In this paper, the authors proposed combining the colony optimization algorithm ant colony optimization (ACO) routing algorithm and mobile sink to deal with that drawback and extend the network life. The simulation results on MATLAB show that the proposed protocol has far better performance than studies within the same field
On the regularization of solution of an inverse ultraparabolic equation associated with perturbed final data
In this paper, we study the inverse problem for a class of abstract
ultraparabolic equations which is well-known to be ill-posed. We employ some
elementary results of semi-group theory to present the formula of solution,
then show the instability cause. Since the solution exhibits unstable
dependence on the given data functions, we propose a new regularization method
to stabilize the solution. then obtain the error estimate. A numerical example
shows that the method is efficient and feasible. This work slightly extends to
the earlier results in Zouyed et al. \cite{key-9} (2014).Comment: 19 pages, 4 figures, 1 tabl
Analysis of probability of non-zero secrecy capacity for multi-hop networks in presence of hardware impairments over Nakagami-m fading channels
In this paper, we evaluate probability of non-zero secrecy capacity of multi-hop relay networks over Nakagamim fading channels in presence of hardware impairments. In the considered protocol, a source attempts to transmit its data to a destination by using multi-hop randomize-and-forward (RF) strategy. The data transmitted by the source and relays are overheard by an eavesdropper. For performance evaluation, we derive exact expressions of probability of non-zero secrecy capacity (PoNSC), which are expressed by sums of infinite series of exponential functions and exponential integral functions. We then perform Monte Carlo simulations to verify the theoretical analysis.Web of Science25478277
Human activity learning and segmentation using partially hidden discriminative models
Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance. Traditional approaches to this problem typically rely on supervised learning and generative models such as the hidden Markov models and its extensions. While activity data can be readily acquired from pervasive sensors, e.g. in smart environments, providing manual labels to support supervised training is often extremely expensive. In this paper, we propose a new approach based on semi-supervised training of partially hidden discriminative models such as the conditional random field (CRF) and the maximum entropy Markov model (MEMM). We show that these models allow us to incorporate both labeled and unlabeled data for learning, and at the same time, provide us with the flexibility and accuracy of the discriminative framework. Our experimental results in the video surveillance domain illustrate that these models can perform better than their generative counterpart, the partially hidden Markov model, even when a substantial amount of labels are unavailable.<br /
Neural-BO: A Black-box Optimization Algorithm using Deep Neural Networks
Bayesian Optimization (BO) is an effective approach for global optimization
of black-box functions when function evaluations are expensive. Most prior
works use Gaussian processes to model the black-box function, however, the use
of kernels in Gaussian processes leads to two problems: first, the kernel-based
methods scale poorly with the number of data points and second, kernel methods
are usually not effective on complex structured high dimensional data due to
curse of dimensionality. Therefore, we propose a novel black-box optimization
algorithm where the black-box function is modeled using a neural network. Our
algorithm does not need a Bayesian neural network to estimate predictive
uncertainty and is therefore computationally favorable. We analyze the
theoretical behavior of our algorithm in terms of regret bound using advances
in NTK theory showing its efficient convergence. We perform experiments with
both synthetic and real-world optimization tasks and show that our algorithm is
more sample efficient compared to existing methods
Experimental study of the long-term shortening of reinforced concrete columns under maintaining concentric axial load
This paper presents the results of an experimental study to measure the shortening of reinforced concrete (RC) columns under long-term maintaining concentric axial load. Long-term axial deformation due to shrinkage and creep of the concrete were recorded beside deformation due to mechanical load. Eight RC cylinder - columns (content of reinforcement 1.5% and 2%) with diameter of 150 mm and height of 600 mm were tested during the period of 600 days to determine their shortening. The experimental results showed that the long-term deformation of RC columns occurs primarily during the first year of loading. The deformation creep of concrete is much greater than the shrinkage deformation. The reinforcement content has a significant effect on the long-term deformation of concrete columns
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