Turbulent boundary layers: is the wall falling or merely wobbling?

A quick passage through five centuries of turbulence research highlights the major milestones. The more recent cornerstones include Kolmogorov??s equilibrium theory of turbulence spectrum, the universal logarithmic law of wall-bounded flows, and the proliferation of direct numerical simulations. Evidence of recent fault lines in all three major achievements is presented, but also novel remedies as well as a few contemporary accomplishments are pointed out.     

Comments on the security of Chen’s authenticated encryption scheme

Chen (Computers and Electrical Engineering, vol. 30, 2004) illustrated that Tseng et al.’s authenticated encryption schemes, with message linkages for message flows, do not achieve their claimed integrity and authenticity properties. Furthermore, Chen presented some modified schemes to repair these flaws. In this paper, we show that the modified schemes proposed by Chen are not secure. In particular, we present an attack that allows a dishonest referee, in case of a dispute, to decrypt all the future and past authenticated ciphertext between the contended parties. We also present a simple fix to prevent this attack.     

Friction model for friction stir welding process simulation: Calibrations from welding experiments

The accurate 3D finite element simulation of the Friction Stir Welding (FSW) process requires a proper knowledge of both material and interface behaviors, but friction, the key phenomenon of this process, is quite difficult to model and identify. According to the extreme encountered conditions and the highly coupled nature of the material flow, simple tribological tests are not representative enough, so the welding process itself has been utilized in most analyses of the literature, although its complexity has led to use simplified numerical models and approaches. The recent development of more accurate 3D simulation software, which allows modeling the entire complexity of the FSW process, makes it possible to follow a much more rigorous inverse analysis (or calibration) approach. FSW trials are conducted on an Al 6061 aluminum plate with an unthreaded concave tool. Forces and tool temperatures are accurately recorded at steady welding state, for different welding speeds. The numerical simulations are based on an Arbitrary Lagrangian Eulerian (ALE) formulation that has been implemented in the Forge3^® F.E. software. The main feature of the numerical approach is to accurately compute the contact and frictional surface between the plate and the tool. A first study using Norton's friction model show the great sensitivity of welding forces and tool temperatures to friction coefficients, the need to take into account the changes brought to the contact surface by slight friction variations (thanks to the ALE formulation), the possibility to get very accurate calibrations on forces, and the impossibility to properly render the tool temperature profile. On the other hand, the use of Coulomb's friction model allows obtaining realistic temperature profiles and so calibrating a friction coefficient that offers an excellent agreement with experiments, on forces as much as on tool temperatures, for various welding speeds.     

Tuning fuzzy PD and PI controllers using reinforcement learning

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi–Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen’s implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.     

Rakeness with block sparse Bayesian learning for efficient ZigBee‐based EEG telemonitoring

Future healthcare systems are shifted toward long‐term patient monitoring using embedded ultra‐low power devices. In this paper, the strengths of both rakeness‐based compressive sensing (CS) and block sparse Bayesian learning (BSBL) are exploited for efficient electroencephalogram (EEG) transmission/reception over wireless body area networks. A binary sensing matrix based on the rakeness concept is used to find the most energetic signal directions. A balance is achieved between collecting energy and enforcing restricted isometry property to capture the underlying signal structure. Correct presentation of the EEG oscillatory activity, EEG wave shape, and main signal characteristics is provided using the discrete cosine transform based BSBL, which models the intra‐block correlation. The IEEE 802.15.4 wireless communication technology (ZigBee) is employed, since it targets low data rate communications in an energy efficient manner. To alleviate noise and channel multipath effects, a recursive least square based equalizer is used, with an adaptation algorithm that continually updates the filter weights using successive input samples. For the same compression ratio (CR), results indicate that the proposed system permits a higher reconstruction quality compared with the standard CS algorithm. For higher CRs, lower dimensional projections are allowed, meanwhile guaranteeing a correct reconstruction. Thus, low computational high quality data compression/reconstruction are achieved with minimal energy expenditure at the sensors nodes.     

Source localization using TDOA and FDOA measurements based on modified cuckoo search algorithm

This paper introduces a new algorithm for solving the localization problem of moving multiple disjoint sources using time difference of arrival and frequency difference of arrival. The localization of moving sources can be considered as a least-square problem. There are many algorithms used to solve this problem such as, two-step weighted least squares, constrained total least-square and practical constrained least-square. However, most of these algorithms suffer from either slow convergence or numerical instability and don’t attain Cramer–Rao lower bound. We introduce a free-gradient algorithm called cuckoo search which avoids the slow convergence problem. The cuckoo search provides a combined global and local search method. Simulation results show that the proposed algorithm achieves better performance than other algorithms and attains Cramer–Rao lower bound.     

Order selection statistical test for nonstationary AR models

A new statistical test for selecting the order of a nonstationary AR modelyk is presented based on the predictive least-squares principle. This test is of the same order as the accumulated cost function _n = _k=1 ⁿ ( _k ^* – _k )²;i.e., ^* wherey _k ^* is the predictive least-square estimate. It is constructed to show how many times the integrated AR processy _k is differenced in order to obtain a stationary AR process given that the exact order of the process is unknown.     

A Self-powered Wearable Wireless Sensor System Powered by a Hybrid Energy Harvester for Healthcare Applications

Wireless Personal Communications - In this paper, a wearable medical sensor system is designed for long-term healthcare applications. This system is used for monitoring temperature, heartbeat,...