With the evolution of sensor-empowered systems in fields ranging from smart cities (e.g., green energy) and industry (e.g., machines condition monitoring) to personal and social health and wellness (e.g., wearable dev...
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ISBN:
(纸本)9781728138862
With the evolution of sensor-empowered systems in fields ranging from smart cities (e.g., green energy) and industry (e.g., machines condition monitoring) to personal and social health and wellness (e.g., wearable devices and electroencephalography, EEG), it is vital to consider constraints related to these sensors, including power consumption and noise. Consequently, empowering intelligence (e.g. prediction of states or outcomes) in such systems becomes of high importance in order to better deal with such constraints and to reduce the effects of dimensionality and volume of sensed data. Here, we propose a sensor selection algorithm that minimizes the sensor space relying on the similarities in how sensors sense a given environment. This algorithm resulted in a significant reduction in the sensor space without notable loss of accuracy when compared against the complete set of the sensing space. We applied the proposed algorithm to two sensor-based time-series datasets of different fields of application.
Pooling mobile devices in vicinity as a cooperative community offers an opportunity to enable multipath transmission for multi-homed mobile devices, even when there is no multiple access coverage. Nonetheless, the ava...
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ISBN:
(纸本)9781479913510
Pooling mobile devices in vicinity as a cooperative community offers an opportunity to enable multipath transmission for multi-homed mobile devices, even when there is no multiple access coverage. Nonetheless, the available bandwidth provided by relays can be highly varying due to a range of factors such as wireless channel fading and dynamic local traffic load at relays. As a result, it is challenging to maintain a stable multipath aggregate throughput over relays. In this paper, we propose an enhancement module within the application layer for a cooperative community in the Long Term Evolution (LTE) network. Our extension is based on the standardized multipath transport control protocol (MPTCP) [1]. Based on relay bandwidth monitoring, a dynamic relay selection algorithm is developed for adding and deleting paths so as to ensure a stable aggregate throughput in a highly varying environment. The proposed relay selection algorithm is based on a fully polynomial-time subset-sum approximation [2]. Extensive simulations are conducted to evaluate the proposed solution in different background traffic patterns. The simulation results well demonstrate the strengths in minimizing throughput outage, the number of active subflows, and performance variation.
A Synchronous Ethernet Network consists of network element (NE) which recover clock from the underlying Ethernet PHY and the quality of clock is conveyed using Ethernet Synchronous Messaging Channel with QL informatio...
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ISBN:
(纸本)9781510863279
A Synchronous Ethernet Network consists of network element (NE) which recover clock from the underlying Ethernet PHY and the quality of clock is conveyed using Ethernet Synchronous Messaging Channel with QL information embedded in QL-TLV. This enables clock selection for the downstream node. This paper describes a method to detect Timing Loop in synchronous network by modifying the current clock selection algorithm by adding another TLV to ESMC. A new parameter `hop count' define this new TLV which is incremented by `1' on each hop as the ESMC messages propagate on a synchronous ethernet network. This paper also shows the modified clock selection algorithm which each NEs uses to select the best available clock from a set of clocks. Also this paper captures the MTIE/TDEV graph for the frequency error/noise respectively with the proposed solution.
In microarray analysis, the selection of informative gene is an essential issue for tissue classification and successful treatment because of its ability to improve the accuracy and decrease computational complexity. ...
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ISBN:
(纸本)9781479900312
In microarray analysis, the selection of informative gene is an essential issue for tissue classification and successful treatment because of its ability to improve the accuracy and decrease computational complexity. However, the gene subsets selected by the same method often vary significantly with some variations of the samples in the same data set. Thus, the stability of the selected genes is an aspect as important as the predictive ability to quantify a feature selection algorithm. This work is an attempt to improve the stability of feature selection methods from the point of view of sample importance. We propose an efficient mean deviation-based sample weighting algorithm to improve the stability of feature selection methods by assigning a weight to each sample according to the mean deviation of its local value in giving samples. We perform a series of experiments with four frequently studied public data sets, and the experimental results validate that the proposed method improves the stability of common feature selection algorithms such as ReliefF without sacrificing the classification performance. Furthermore, more stable gene feature sets obtained by the proposed method than the state-of-the-art ensemble method and margin-based sample weighting algorithm.
Microplastic pollution is now everywhere, from the highest elevation of Mount Everest to the deepest dives of the Mariana Trench. The use of synthetic polymers has contributed to the transformation of our modern world...
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Microplastic pollution is now everywhere, from the highest elevation of Mount Everest to the deepest dives of the Mariana Trench. The use of synthetic polymers has contributed to the transformation of our modern world. Nonetheless, the growing presence of plastics in the natural environment constitutes a sense of urgency to develop sustainable solutions to address the global plastic pollution crisis. Approximately 80 percent of all the plastics ever produced have been discarded. Our relationship with plastic needs rethinking. Achieving a circular economy requires a holistic approach to redesign the systems that make, use and reuse plastics. Evaluating the life cycle of plastic products is key to effective advancements in the collection, separation and sorting systems of mixed plastic waste streams. This thesis critically reviews bulk recycling methods used in industry. Recycling provides significant opportunities for recovering material resources through mechanical and/or chemical processes, which in return reduces petrochemical usage, greenhouse gas emissions, and the quantity of waste requiring disposal. Yet, recovering the resource value of mixed plastic waste streams presents critical challenges in materials identification and recycling process design. Bulk recycling models consider the selection of the best sequence for isolating target materials from a mixed material stream. This thesis models the separation of single and multiple target materials using density separation techniques to minimize the total processing volume and cost. Furthermore, a selection algorithm is implemented to calculate the processing costs of each pass, determine the minimum cost of separation, and present the optimal separation sequence.
Dynamic mode decomposition (DMD) yields a linear, approximate model of a system's dynamics that is built from data. This paper seeks to reduce the order of this model by identifying a reduced set of modes that bes...
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Dynamic mode decomposition (DMD) yields a linear, approximate model of a system's dynamics that is built from data. This paper seeks to reduce the order of this model by identifying a reduced set of modes that best fit the output. A model selection algorithm from statistics and machine learning known as least angle regression (LARS) is adopted. LARS is modified to be complex-valued, and LARS is used to select DMD modes. The resulting algorithm is referred to as least angle regression for dynamic mode decomposition (LARS4DMD). Sparsity-promoting dynamic mode decomposition (DMDSP), a popular mode-selection algorithm, serves as a benchmark for comparison. LARS4DMD has the advantage that the sparsity parameter required for DMDSP is not needed. Numerical results from a Poiseuille flow test problem show that LARS4DMD yields reduced-order models that have comparable performance to DMDSP. Use of the LARS4DMD algorithm on particle image velocimetry data of a rotating fin confirms this conclusion on experimental data. Results further suggest that LARS4DMD may be slightly more robust to noise in the experimental data.
Wireless sensor networks (WSNs) demand the implementation of energy-aware techniques and low-complexity protocols in all layers. Recently, a MIMO-based structure has been proposed to offer enhanced energy savings in W...
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Wireless sensor networks (WSNs) demand the implementation of energy-aware techniques and low-complexity protocols in all layers. Recently, a MIMO-based structure has been proposed to offer enhanced energy savings in WSNs. In this paper, we examine and compare MIMO-based WSN with a multihop transmission in terms of energy efficiency. The results depend on the network density, the channel conditions, and the distance to the destination node. We reach analytical expressions to calculate threshold values of these parameters, which determine the areas where the MIMO-based structure outperforms multihop transmission. Moreover, we present a detailed analysis of the dissipated power during a sensor node_s operation, to prove that as microelectronics develops, the MIMO-based architecture will outperform the equivalent multihop structure for most of the cases examined. Finally, we implement a simple cooperative node selection algorithm to achieve higher energy gains in the MIMO approach, and we examine how this algorithm affects the calculated thresholds. Copyright (C) 2008 G. Bravos and A. G. Kanatas.
Next generation wireless communications are expected to rely on integrated networks consisting of multiple wireless technologies. Heterogeneous networks based on Wireless Local Area Networks (WLANs) and Wireless Wide ...
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Next generation wireless communications are expected to rely on integrated networks consisting of multiple wireless technologies. Heterogeneous networks based on Wireless Local Area Networks (WLANs) and Wireless Wide Area Networks (WWANs) can combine their respective advantages on coverage and data rates, offering a high Quality of Service (QoS) to mobile users. In such environment, multi-interface terminals should seamlessly switch from one network to another in order to obtain improved performance or at least to maintain a continuous wireless connection. Therefore, network selection algorithm is important in providing better performance to the multi-interface terminals in the integrated networks. In this paper, we propose a cost-based vertical handover decision algorithm that triggers the Vertical Handover (VHO) based on a cost function for WWAN/WLAN integrated networks. For the cost function, we focus on developing an analytical model of the expected cost of WLAN for the mobile users that enter the double-coverage area while having a connection in the WWAN. Our simulation results show that the proposed scheme achieves better performance in terms of power consumption and throughput than typical approach where WLANs are always preferred whenever the WLAN access is available. Copyright (C) 2009
Volterra-series are very suitable for modelling nonlinear processes, since they cover a large class of nonlinear systems. In the past, it has often been pointed out that Volterra-series are not ideal for identificatio...
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Volterra-series are very suitable for modelling nonlinear processes, since they cover a large class of nonlinear systems. In the past, it has often been pointed out that Volterra-series are not ideal for identification due to the large number of parameters, which have to be estimated. In this paper, a new identification method will be presented which reduces the number of estimated parameters to a reasonable size by introducing basis functions. Further reduction can be achieved using a selection algorithm, which selects the most suitable basis functions from a large class of different basis functions. In simulation examples, it will be shown that many nonlinear systems can be identified with less than 20 parameters.
This paper considers a special case of the standard transportation problem obtained by restricting the number of origins to two. Necessary and sufficient conditions are established which lead to a direct construction ...
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This paper considers a special case of the standard transportation problem obtained by restricting the number of origins to two. Necessary and sufficient conditions are established which lead to a direct construction of the optimal solution. Using an extension of a recent selection algorithm, an algorithm is developed to solve this special case of the transportation problem in O(n)
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