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Subsequence join in time series is to search for couples of similar subsequences from multiple time series. The task is useful in data mining on time series; nevertheless, it is extremely difficult because of its enormous computational cost. The task should use normalized time series during the search execution and be performed under an efficient distance measure to obtain accurate resulting couples. The task is more challenging when it works in a streaming environment in which time-series data might be collected very quickly. To address this problem, we propose an efficient method of subsequence join in streaming time series under Dynamic Time Warping (DTW), supporting z-score normalization. The proposed method utilizes a technique of subsequence extraction based on major extrema of streaming time series to search for couples of similar subsequences from coevolving time series. This method can identify couples of similar subsequences of the same length or different lengths. The experimental results show that the proposed method has high performance and can bring out interesting couples of similar subsequences. In addition, this method acts as an approximation algorithm suitable for a streaming scenario where users often expect fast responses from the task of subsequence join over time-series streams of high rates.

This paper proposes a joint design strategy for enhancing individual user rates in a multi-user system by optimizing both the programmable reflecting elements (PREs) of an active reconfigurable intelligent surface (aRIS) and the transmit beamforming at a base station. Given that the aRIS's PREs are bound by discrete constraints due to low-resolution quantization, this design approach relies on large-scale mixed discrete-continuous problems, which are addressed through a new universal penalised optimization reformulations. Initially, we develop iterations based on convex quadratic solvers (CQ) to tackle the problem of maximizing the users' minimum rate (MR). Given that the computational complexity of these CQs is cubic, leading to high costs in large-scale computations, we introduce a pair of surrogate objectives. These objectives are designed in a way that their constrained optimization can be efficiently managed through iterations of closed-form expressions with scalable complexity, rendering them practical for large-scale computations. This pair of surrogate objectives comprises the maximization of the geometric mean of users' rates (GM-rate maximization) and the soft-maximization of users' MR (soft max-min rate optimization). Remarkably, they not only enhance MR but also contribute to the improvement of the sum-rate (SR). Building upon the GM-rate optimization, we further propose addressing the energy efficiency problem, which achieves a high ratio of SR to power consumption and MR to power dissipation through closed-form expressions. Comprehensive simulations are conducted to validate the efficacy of the proposed solutions.

We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.

A novel route to the fabrication of hierarchical mesoporous Nd2O3nanostructures including nanospheres and nanoporous network was described. Their structure and morphology evolution of the as-synthesized materials were determined by various techniques such as scanning electron microscopy, transmission electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, energy-dispersive X-ray spectroscopy, Fourier transforminfrared spectra, nitrogen adsorption/desorption isotherm, and a formation mechanism was proposed. The results revealed that the Nd2O3nanospheres had the diameter of 300 nm, which were composed of small primary nanoparticles (NPs) with the size of 10 nm. The nanoporous structure also formed the NPs of ca. 10 nm which were connected with each other to form a three-dimensional (3D) texture. This simple and mild approach to fabricate hierarchical mesoporous Nd2O3nanostructures could be easily scaled up and potentially extended to synthesize other oxide hierarchical structures.

The potential of terahertz imaging for improving the efficiency of Mohs's micrographic surgery in terms of tumor margin detection was previously studied. Thanks to high water content of human skin, its dielectric response to terahertz radiation can be described by the double Debye model which uses five parameters to fit experimental data. Skin tumors typically have a higher water content than normal tissues do, and this should be apparent in the parameters. The goal of this paper is to apply statistical methods to these parameters to test their power to differentiate skin cancer from normal tissue. Based on the prediction accuracy estimated using a cross-validation method, we found the best classifier was the static permittivity at low frequency $({\mmb\varepsilon}_{s})$ . By combining the most relevant parameters, we obtained a classification accuracy of 95.7%, confirming the classification capability of the parameters, thereby supporting their application to improve terahertz imaging for the purpose of skin cancer delineation.

In this paper we develop an augmented Lagrangian method to determine local optimal solutions of the reduced- and fixed-order synthesis problems. We cast these synthesis problems as optimization programs with a linear cost subject to linear matrix inequality (LMI) constraints along with nonlinear equality constraints representing a matrix inversion condition. The special feature of our algorithm is that only equality constraints are included in the augmented Lagrangian, while LMI constraints are kept explicitly in order to exploit currently available semi definite programming (SDP) codes.

In this paper, the missile pitch-axis autopilot design is revisited using a new and recently available linear parameter-varying (LPV) control technique. The missile plant model is characterized by a linear fractional transformation (LFT) representation. The synthesis task is conducted by exploiting new capabilities of the LPV method: firstly, a set of H2/H∞ criteria defined channel-wise is considered; secondly, different Lyapunov and scaling variables are used for each channel/specification which is known to reduce conserva tism; and finally, the controller gain-scheduling function is constructed as affine matrix-valued function in the polytopic co-ordinates of the scheduled parameter. All these features are examined and evaluated in turn for the missile control problem. The method is shown to provide additional flexibility to tradeoff conflicting and demanding performance and robustness specifications for the missile while preserving the practical advantage of previous single-objective LPV methods. Finally, the method is shown to perform very satisfactorily for the missile autopilot design over a wide range of operating conditions. Copyright © 2001 John Wiley & Sons, Ltd.

In this paper, the missile pitch‐axis autopilot design is revisited using a new and recently available linear parameter‐varying (LPV) control technique. The missile plant model is characterized by a linear fractional transformation (LFT) representation. The synthesis task is conducted by exploiting new capabilities of the LPV method: firstly, a set of H2/H∞criteria defined channel‐wise is considered; secondly, different Lyapunov and scaling variables are used for each channel/specification which is known to reduce conserva tism; and finally, the controller gain‐scheduling function is constructed as affine matrix‐valued function in the polytopic co‐ordinates of the scheduled parameter. All these features are examined and evaluated in turn for the missile control problem. The method is shown to provide additional flexibility to tradeoff conflicting and demanding performance and robustness specifications for the missile while preserving the practical advantage of previous single‐objective LPV methods. Finally, the method is shown to perform very satisfactorily for the missile autopilot design over a wide range of operating conditions. Copyright © 2001 John Wiley & Sons, Ltd.