Piezoelectric micromachined ultrasonic transducers: modeling the influence of structural parameters on device performance

Piezoelectric micromachined ultrasonic transducers (pMUTs), a potential alternative for conventional one-dimensional phased array ultrasonic transducers, were investigated. We used a modeling approach to study the performance of lead zirconate titanate (PZT)-driven pMUTs for the frequency range of 2-10 MHz, optimized for maximum coupling coefficient, as a function of device design. Using original tools designed for the purpose, a comprehensive build-test finite element model was developed to predict and measure the device performance. In particular, the model estimates the device coupling coefficient and the acoustic impedance, besides the readily extractable resonance frequency and bandwidth. To validate the model, a prototype device was built and tested, showing good agreement between the model predictions and experimental results. Modeling results indicate that the coupling coefficient is significantly affected by silicon membrane, PZT, and top electrode thickness as well as the top electrode design. Results also indicate considerable flexibility in maximizing the coupling coefficient while maintaining the device acoustic impedance at a level matching that of the human body. The bandwidth proved to be superior to that of conventional transducers, reaching 102% in some cases.     

Enhancing robustness and perturbation tolerance of cognitive radio engines with metacognition

A cognitive radio engine (CE) is an intelligent agent which observes the radio environment and chooses the best communication settings that best meet the application’s goal. In this process, providing reliable performance is one of the major tasks in designing CEs for wireless communication systems. The main purpose of this work is providing predictable performance and controlling the cost of intelligent algorithms based on the CE’s experience and complexity analysis respectively. In this work, we extend our meta-CE design to control the cost of computations and provide more reliable performance for providing the minimum requirement of the radio applications in different scenarios. To achieve this, we use robust training algorithm (RoTA) in two different levels alongside of the individual CE algorithms. The RoTA, enables radio to guarantee some minimum output performance based on the learning stages. RoTA uses confidence interval approximation for standard normal distribution to calculate the lower and upper bounds of CE’s expected performance to analyze the reliability of decisions. Moreover, in the case of non-stationary environments, RoTA is facilitated by forgetfulness factor to provide minimum performance guarantees. The second level of RoTA operates in meta-level to control the amount of computation complexity of intelligent algorithms in all levels with respect to the obtained performance and complexity analysis.     

Pulse position modulated space?time trellis coding for ultra-wideband impulse radio multiple-input multiple-output communication systems

The design of pulse position modulated (PPM) space-time trellis codes (STTC) for ultra-wideband impulse radio (UWB-IR) multiple-input multiple-output (MIMO) communication systems over slow and fast fading multipath channels is considered. First, A probability of error analysis is carried out to derive upper bounds on pairwise symbol error probability at high and low signal-to-noise ratios (SNRs). From the upper bounds, A new distance notion is introduced and novel design criteria for optimal (in error rate performance) M-ary PPM STTC are deduced for UWB. An optimal binary-PPM STTC is designed for two transmit antennas. Finally, simulation results of the UWB-IR MIMO system, using the optimal STTC, confirm significant improvement in bit-error-rate performance over the uncoded UWB-IR single-input single-output system and also over previously proposed space-time coding scheme for UWB, at higher SNR.     

Fuzzy-based artificial bee colony optimization for gray image segmentation

In this article, we have proposed an image segmentation algorithm FABC, which is a kind of unsupervised classification (clustering), where we combine the concept of artificial bee colony optimization (ABC) and the popular fuzzy C means (FCM) and named it as fuzzy-based ABC or FABC. In FABC, we have used fuzzy membership function to search for optimum cluster centers using ABC. FABC is more efficient than other optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and expectation maximization (EM) algorithms. FABC overcomes the drawbacks of FCM as it does not depend on the choice of initial cluster centers and it performs better in terms of convergency, time complexity, robustness and segmentation accuracy. FABC becomes more efficient as it takes the advantage of the randomized characteristics of ABC for the initialization of the cluster centers. The experiments with FABC, GA, PSO and EM have been done over various grayscale images including some synthetic, medical and texture images, and segmentation of such images is very difficult due to the low contrast, noise and other imaging ambiguities. The efficiency of FABC is proven by both quantitative and qualitative measures.     

Neural classification of Lamb wave ultrasonic weld testing signalsusing wavelet coefficients

This paper presents an ultrasonic nondestructive weld testing method based on the wavelet transform (WT) of inspection signals and their classification by a neural network (NN). The use of Lamb waves generated by an electromagnetic acoustic transducer (EMAT) as a probe allows us to test metallic welds. In this work, the case of an aluminum weld is treated. The feature extraction is made by using a method of analysis based on the WT of the ultrasonic testing signals; a classification process of the features based on a neural classifier to interpret the results in terms of weld quality concludes the process. The aim of this complete process of analysis and classification of the testing ultrasonic signals is to lead to an automated system of weld or structure testing. Results of real-world ultrasonic Lamb wave signal analysis and classifications for an aluminum weld are presented; these demonstrate the feasibility and efficiency of the proposed method     

Photoluminescence and heavy doping effects in InP

Photoluminescence (PL) studies on LPE-grown InP layers doped with selenium and having carrier concentrations from 1 × 10¹⁸ to 1 × 10²⁰ cm⁻³ have been reported in this paper. Measurements at 300 and 77 K showed that the band to band recombination peak energy shifts to values as high as 1·7 eV with increasing doping, the increase being sharp beyond 4 × 10¹⁹ cm⁻³. These results have been explained as being the result of the Burstein shift and the band-gap shrinkage.     

Expert system, fuzzy logic, and neural network applications inpower electronics and motion control

Artificial intelligence (AI) tools, such as expert systems, fuzzy logic, and neural networks are expected to usher a new era in power electronics and motion control in the coming decades. Although these technologies have advanced significantly and have found wide applications, they have hardly touched the power electronics and machine drives area. The paper describes these AI tools and their application in the area of power electronics and motion control. The body of the paper is subdivided into three sections which describe, respectively, the principles and applications of expert systems, fuzzy logic, and neural networks. The theoretical portion of each topic is of direct relevance to the application of power electronics. The example applications in the paper are taken from the published literature     

Ultrathin Highly Luminescent Two‐Monolayer Colloidal CdSe Nanoplatelets

Surface effects in atomically flat colloidal CdSe nanoplatelets (NLPs) are significantly and increasingly important with their thickness being reduced to subnanometer level, generating strong surface related deep trap photoluminescence emission alongside the bandedge emission. Herein, colloidal synthesis of highly luminescent two‐monolayer (2ML) CdSe NPLs and a systematic investigation of carrier dynamics in these NPLs exhibiting broad photoluminescence emission covering the visible region with quantum yields reaching 90% in solution and 85% in a polymer matrix is shown. The astonishingly efficient Stokes‐shifted broadband photoluminescence (PL) emission with a lifetime of ≈100 ns and the extremely short PL lifetime of around 0.16 ns at the bandedge signify the participation of radiative midgap surface centers in the recombination process associated with the underpassivated Se sites. Also, a proof‐of‐concept hybrid LED employing 2ML CdSe NPLs is developed as color converters, which exhibits luminous efficacy reaching 300 lm W_opt^?1. The intrinsic absorption of the 2ML CdSe NPLs (≈2.15 × 10⁶ cm^?1) reported in this study is significantly larger than that of CdSe quantum dots (≈2.8 × 10⁵ cm^?1) at their first exciton signifying the presence of giant oscillator strength and hence making them favorable candidates for next‐generation light‐emitting and light‐harvesting applications.