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The paper presents linear predictors and causal filters for discrete-time signals featuring some different kinds of spectrum degeneracy. These predictors and filters are based on approximation of ideal noncausal transfer functions by causal transfer functions represented by polynomials of the Z-transform of the unit step signal.

The next generation of brain–machine interfaces are envisioned to couple signal transduction, filtering, and sorting on board with minimum power consumption and maximum bio‐integrability. These functional needs shall be mandatorily met in order to design efficient closed‐loop brain–machine interfaces aimed at treating and monitoring various disorders of the central and peripheral nervous system. Here, the pivotal role is highlighted that organic bioelectronics may have in the contextual development of all these three desiderata, by demonstrating a modular organic‐electronics circuit toward real‐time signal filtering. The inherent filtering capabilities of electrolyte‐gated organic transistor are tuned via adjustment of operational conditions and benchmarked in an electromyography experiment. Additionally, a whole‐organic signal processing circuitry is presented, coupling such transistors with ad hoc designed organic passive components. This provides the possibility to sort complex signals into their constitutive frequency components in real time, thereby delineating innovative strategies to devise organic‐based functional building‐blocks for brain–machine interfaces. An organic electronic platform for in situ real‐time signal processing is benchmarked against complex signals. Low‐, high‐ and band‐pass filters are achieved with organic analogs of passive circuit components and coupled to first‐stage amplifiers based on electrolyte‐gated organic transistors, fostering organic bio‐electronics integration in brain–machine interfaces, aiming at on board signal processing with lowered time and power consumption.

Planetary gearboxes are designed to reduce speed while increasing load capacity in power transmission systems. This study simulates the dynamics of the eighteen-degree-of-freedom planetary gearbox with time-varying mesh stiffness using MATLAB/Simulink software. We derive the dynamic equations to analyze the impact of worn bearings on the vibration output of the gearbox. The simulation and experimental analysis techniques are utilized for fault detection, focusing on time signals and velocity spectrums. The looseness of the bearings affects the vibrations of both the gearbox body and its gears. In this investigation, we simulate the classification of bearing looseness in the planetary gearbox and compare the results with experimental validations. This comparison demonstrates how worn bearings can influence the failure of a planetary gearbox and affect the amplitude of rotational speed frequencies. The primary aim of this research is to detect bearing failure using only the amplitude of harmonic vibration at rotational speed, without any new failure frequency excitation.

The analysis of highly irregular processes is often based on the search for a trend, an average curve representing the general pattern of the phenomenon observed. This concept seems intuitive, but determining it objectively raises a number of problems. As the moving average method is one of the most widely used for finding one-dimensional trends, we propose here to extend it to two-dimensional data sets, and to study some of the properties of these 2-D moving averages. We then show that they can be used to detect characteristic observation windows (uniform or self-adaptive), leading to structural trends in the analyzed signal. The method is first applied to functions f(x,y) whose variables have the same physical dimension (length,...). It is then generalized to cases of different dimensions (e.g. space-time signals). Applications cover a wide range of fields (turbulent structures in fluid mechanics, image analysis, characterization of surfaces, optimization of an observation process, multi-scale modelling, ...).

Background Dermatofibrosarcoma protuberans (DFSP) is a kind of low‐grade malignant spindle cell neoplasm, the diagnosis, and treatment, which have markedly attracted clinicians’ attention for its repeated recurrence. High‐resolution magnetic resonance imaging (HR‐MRI) has shown unique capabilities in diagnosis of various cutaneous tumors. Materials and methods Data of 29 patients with clinically suspected DFSPs and undergoing dynamic contrast‐enhanced (DCE) HR‐MRI preoperatively were prospectively collected. The HR‐MRI qualitative features were evaluated and compared. The DCE‐associated quantitative parameters and the time‐signal intensity curve (TIC) types were provided using DCE sequences. Results A total of 7 DFSPs, nine dermatofibromas (DF, including four cases of cellular variant [CDF]), 12 keloids, and one nodular fasciitis were enrolled. DFSP showed the largest major diameter and the deepest depth. Five DFSPs (71.4%) showed ill‐defined margins as well as infiltration of peripheral adipose. All DFSPs showed irregular shape. Most DFSPs presented hyperintensity on T2WI (71.4%) and iso‐intensity on T1WI (85.7%). Six cases (85.7%) had significant enhancement, and six cases (85.7%) had homogeneous enhancement. There were significant differences of Ktrans, Kep, Ve and iAUC values among DFSPs, DFs, and keloids, and DFSP had the highest values for these parameters. Six DFSPs (85.7%) and four CDFs (100%) showed type‐III TICs, while the other lesions showed type‐Ⅰor type‐Ⅱ TICs. Conclusions DCE‐HR‐MRI could show the growth characteristics of DFSPs, which was of great value for the diagnosis and differential diagnosis of DFSPs and was helpful for the determination of treatment options, thereby to improve the prognosis of patients.

Sparse representation of signals based on a redundant dictionary is a new signal representation theory. Recent research activities in this field have concentrated mainly on the study of dictionary design and sparse decomposition algorithms. Currently, the application of sparse representation on an Automatic Identification System (AIS) signal still requires further investigations. In this paper, a novel sparse representation of the AIS signal is proposed based on an adaptive redundant dictionary. Considering the characteristics of the AIS signal, an adaptive redundant dictionary is constructed using the K singular value decomposition (K-SVD) algorithm. Furthermore, an effective pursuit algorithm is proposed to obtain the sparse representation of AIS signal using the adaptive dictionary. The binary AIS message is demodulated from the sparse representation of AIS signal. The experimental results indicate that the sparse representation of the AIS signal has high accuracy and the reconstructive error rate can be under 10%; thus, the reconstructive precision is simultaneously guaranteed. The processing time of the proposed sparse representation algorithm is less than 26.7 ms which satisfies the requirements of AIS real-time signal processing. It shows that introducing the signal sparse representation in a real-time signal system obtains a satisfactory result.

This article investigates the adaptive impulsive synchronization of delayed chaotic system with full unknown parameters. Aiming at this problem, we propose a new adaptive strategy, in which both the adaptive–impulsive controller and the parameters adaptive laws are designed via the discrete‐time signals from the drive system. The corresponding theoretical proof is given to guarantee the effectiveness of the proposed strategy. Moreover, the concrete adaptive strategies are achieved for delayed Hopfield neural network, optical Ikeda system and the well‐known delayed Lü chaotic system. As expected, numerical simulations show the effectiveness of the proposed strategy. This method has potential applications in parameters estimation, secure communication, and cryptanalysis when only discrete signals are transmitted in communication channel. © 2014 Wiley Periodicals, Inc. Complexity 21: 43–51, 2016

To use the existing Automatic Identification System (AIS) shore stations for positioning so that the AIS can be used as an additional land-based positioning system for coastal vessels is a cutting-edge research topic, responding to the call of the International Maritime Organization (IMO). In order to use the ship-borne AIS for positioning function, a holographic detection of AIS real-time signal based on sparse representation is presented in this paper. Considering the working environment and the requirement of AIS real-time signal processing, a novel fast noise resistance Orthogonal Matching Pursuit (OMP) algorithm is presented. Furthermore, the choice and detection of the timestamp of the reconstructed signal is analyzed and carried out which will be used in the ranging system. The experiment results indicate that the proposed fast noise resistance OMP algorithm can greatly reduce the processing time, and the difference in processing time increases with the number of iterations. The improvement in noise immunity is also obvious, and the error rate reduces at about 9% under the same SNR. The timestamp of the reconstructed signals can be detected successfully. It shows that the holographic detection of AIS real-time signal is achieved satisfactorily.

In this paper, the problem of aliasing and folding effects in spectrum of sampled signals in view of Information Theory is discussed. To this end, the information content of deterministic continuous time signals, which are continuous functions, is formulated first. Then, this notion is extended to the sampled versions of these signals. In connection with it, new signal objects that are partly functions but partly not are introduced. It is shown that they allow to interpret correctly what the Whittaker– Shannon reconstruction formula in fact does. With help of this tool, the spectrum of the sampled signal is correctly calculated. The result achieved demonstrates that no aliasing and folding effects occur in the latter. Finally, it is shown that a Banach–Tarski-like paradox can be observed on the occasion of signal sampling.

In solar tracking system, general stepper motor is used to control the stepwise movement and low speed of solar panel in vertical motion. Degree of rotation is directly proportional to stepwise movement (stepping method) of stepper motor. But it is cumbersome process to hold a solar panel at a particular vertical position depending on the sun’s position using low cost stepper motors. So, geared DC motor is implemented into stepper motor for low-speed applications using Stepping Method ( GDCSM ). Degree per step movement of geared DC motor is identified using Step angle switching delay time signal (A) of microcontroller. Speed of geared DC motor is controlled by passing fixed time interval between the pulses where the pulses have fixed width. Controlling speed is implemented by using Step delay time signal (D) of microcontroller. Combining effect of switching delay time signals A and D represents the Stepping Method. Stepping method resembles the step movement and controlling speed of the stepper motor. Speed of 10RPM & 30RPM geared DC motors is operated at 10V DC power supply. Microcontroller ATmega 328P with switching delay time signals is used to control the geared DC motors. Performance of 10RPM & 30RPM GDCSM is analyzed with stepper motors in terms of Relative slip degree error per revolution ( RE S ) and acceptable slippage degree tolerance under open loop condition. Change in consuming voltage tolerance ( Δ ) during rotation of GDCSM is another parameter which is used to maintain the constant actual total step count per revolutions of GDCSM. At 40 ms of step angle delay time (A) , 10RPM GDCSM has 120 steps per revolution . At 25 ms of A , 30RPM GDCSM has 55 steps per revolution . Speed of 10RPM & 30RPM GDCSM is controllable up to 2RPM when its RE S or acceptable slippage degree tolerance value is less than or equal to 1%. If Δ value of GDCSM is less than or equal to − 0.4%, then it behaves like stepper motor . GDCSM is suitable to hold and control vertical position of solar tracking system with low speed and step movement. Performance of geared DC motor experimentally showed better result than commercial available stepper motors like 28BYJ-48 or STP-43d1027-01.