The fast evolution of battery functioned devices has caused approaches for decreasing power consumption in the memories is substantial. In this paper, a new proposal of SRAM with 8 transistors (8T) has been designed and also the cell itself is tested for its unique data overwriting and read propagation delays around 13.33% (read ‘1’) and 3.58% (read ‘0’) less compared to a conventional model. As the technology is attenuating, cell stability and increasing noise margin have become two crucial topics for the design metrics of SRAM, where our proposed cell appears with great stability on low voltage operation. Widespread simulation results authenticate the cogency and competency of the proposed 8T SRAM model using Cadence and 45nm predictive technology model (PTM).
This article presents a modelling and sliding mode control (SMC) of a doubly fed induction generator (DFIG) integrated into wind energy system for independent control of stator reactive and stator active powers. For a comparative study, the independent control of reactive and active powers is ensured in the first step by neuro-sliding mode controllers (NSMC) and the second step by neuro-second order sliding mode controllers (NSOSMC). Finally, the performance of the system is tested and compared by simulation in terms of robustness tests, and reference tracking tests based on Matlab/Simulink software.
In the presented research two Deep Neural Network (DNN) models for face image analysis were developed. The first one detects eyes, nose and mouth and it is based on a moderate size Convolutional Neural Network (CNN) while the second one identifies 68 landmarks resulting in a novel Face Alignment Network composed of a CNN and a recurrent neural network. The Face Parts Detector inputs face image and outputs the pixel coordinates of bounding boxes for detected facial parts. The Face Alignment Network extracts deep features in CNN module while in the recurrent module it generates 68 facial landmarks using not only this deep features, but also the geometry of facial parts. Both methods are robust to varying head poses and changing light conditions.
The objective of E-shaped patch antenna with hexagonal slot is to operate in the ISM band for different kind of applications, such as WLAN, GPS, and various modern wireless systems. The posit antenna is designed using FR4 substrate having a dielectric constant of 4.4 with a thickness of 1.6 mm. Probe feed technique is used for this antenna design. A parametric study was included to determine the effect of design approaches and the antenna performance. The realization of the designed antenna was analyzed in term of boost (gain), return loss, and radiation pattern. The design was upsurged to confirm the best achievable result. This antenna resonates at three different frequencies at 1.6 GHz, 3.24 GHz, and 5.6 GHz with a reflection coefficient less than -10 dB and VSWR<2.
This paper presents the general concept of the nonlinear control of the asynchronous machine. The decoupling between the flux and the speed is realized by the input-output linearization technique. In this work, a nonlinear adaptive control method has been applied to the asynchronous machine and we give some initial results on the adaptive fuzzy logic control of nonlinear systems, linearized by state feedback. The adaptations of the parameters are used as a technique for robustifying the exact cancellation of the nonlinear terms, which is called for the linearization technique. The performance of the proposed nonlinear adaptive control scheme is demonstrated by simulation results. These results show that the proposed method achieves the desired dynamic performance.
Convolutional neural networks (CNN) is a contemporary technique for computer vision applications, where pooling implies as an integral part of the deep CNN. Besides, pooling provides the ability to learn invariant features and also acts as a regularizer to further reduce the problem of overfitting. Additionally, the pooling techniques significantly reduce the computational cost and training time of networks which are equally important to consider. Here, the performances of pooling strategies on different datasets are analyzed and discussed qualitatively. This study presents a detailed review of the conventional and the latest strategies which would help in appraising the readers with the upsides and downsides of each strategy. Also, we have identified four fundamental factors namely network architecture, activation function, overlapping and regularization approaches which immensely affect the performance of pooling operations. It is believed that this work would help in extending the scope of understanding the significance of CNN along with pooling regimes for solving computer vision problems.
This paper presents a new technique for UHF RFID antenna integration with a great impact on the automotive industry. The antenna has been optimized in HFSS and the prototype has been laminated and measured, in between two thin glass slices. The design takes into account the aspects related to the automotive safety glass, used for the windscreens in front of the automobiles. The paper proposes the use of aluminum sheets for the active element of the antenna together with three glass layers as substrates. This approach exhibits excellent results and could be applicable for vehicle long-range identification (AVI) or for cars tracking and localization.
SET is important in the field of nanoelectronics since a decade. This paper presents electrical characteristic of Single-Electron Transistor (SET) with Aluminum Island using Neural Network. The I-V characteristic of the Single-Electron Transistor (SET) is predicted according to different parameters (VG, T, VD, C, and R). The simulation process is based on analytical transistor model and neural network transistor model. Single Electron Transistor (SET) is the simplest device in which the effect of Coulomb blockade can be observed.
A novel technique for deep learning of image classifiers is presented. The learned CNN models higher offer better separation of deep features (also known as embedded vectors) measured by Euclidean proximity and also no deterioration of the classification results by class membership probability. The latter feature can be used for enhancing image classifiers having the classes at the model’s exploiting stage different from from classes during the training stage. While the Shannon information of SoftMax probability for target class is extended for mini-batch by the intra-class variance, the trained network itself is extended by the Hadamard layer with the parameters representing the class centers. Contrary to the existing solutions, this extra neural layer enables interfacing of the training algorithm to the standard stochastic gradient optimizers, e.g. AdaM algorithm. Moreover, this approach makes the computed centroids immediately adapting to the updating embedded vectors and finally getting the comparable accuracy in less epochs.
In this research note the satisficing newsvendor problem is considered which is defined as the maximization of the probability of exceeding the expected profit multiplied by a positive constant. This constant called optimism coefficient can be chosen by the firm’s management either based on their preference or the market conditions. The coefficient indicates whether there is a low or high optimistic decision maker. For the general demand distribution the results are significantly dependent on this coefficient.