Search

A novel ultra-wideband (UWB) KAYI-shaped and common KITE-shaped ground plane tri-port antenna is proposed. The proposed research work has a small size of (30 × 30 × 1.6 mm 3 ). The MIMO antenna elements are placed in a KAYI-shaped (Y-shaped) with a symmetric phase shift of 120 ∘ between the nearby MIMO antennas element improving the isolation. The antenna's gain is more than 5 dBi for the entire bands of WiMax, WLAN, and X-band satellite communication. The suggested work includes notches at 3.2 GHz, 5.2 GHz, and 8.9 GHz, respectively. The notching characteristics are made possible by L-shaped slits for the WiMax band, the inverted U-shaped slot for WLAN, while the third is created by the interaction between the L-shaped and U-shaped notching elements. Results were measured after making the prototype antenna on the FR-4 substrate. The proposed antenna has good impedance matching for 2–20 GHz and three notching characteristics with high isolation among the MIMO elements. Mean effective gain (MEG), envelope correlation coefficient (ECC), and total active reflection coefficient (TARC) are the diversity metrics of MIMO antennas which are in good comparison to the proposed antenna. The antenna is a good candidate for deployment in wireless communication and MIMO applications. [ABSTRACT FROM AUTHOR]

In this paper, a trinotch band MIMO antenna is designed for upper and lower WLAN (5.1 GHz, 5.5 GHz) i.e., (802.11a/g/n/ac/ax) and satellite X-band (8.5 GHz). The placement of closely found notches at 5.1 GHz and 5.5 GHz is addressed by taking advantage from the placement of the same notch elements at appropriate distances from each other, to split single resonance into biresonances. Similarly, the third notch is produced at 8.5 GHz via a U-shaped slot added in the antenna. 2x1-MIMO antenna with notch bands is expected with better isolation by analysis of co-configurations and cross-configurations. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) offers compression of data below the Nyquist rate, making it an attractive solution in the field of medical imaging, and has been extensively used for ultrasound (US) compression and sparse recovery. In practice, CS offers a reduction in data sensing, transmission, and storage. Compressive sensing relies on the sparsity of data; i.e., data should be sparse in original or in some transformed domain. A look at the literature reveals that rich variety of algorithms have been suggested to recover data using compressive sensing from far fewer samples accurately, but with tradeoffs for efficiency. This paper reviews a number of significant CS algorithms used to recover US images from the undersampled data along with the discussion of CS in 3D US images. In this paper, sparse recovery algorithms applied to US are classified in five groups. Algorithms in each group are discussed and summarized based on their unique technique, compression ratio, sparsifying transform, 3D ultrasound, and deep learning. Research gaps and future directions are also discussed in the conclusion of this paper. This study is aimed to be beneficial for young researchers intending to work in the area of CS and its applications, specifically to US. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the implementation of open complimentary split ring resonator (OCSRR) structure in small rectangular radiating elements to achieve triple band and dual band reconfigurability using PIN diode. The proposed design is a coplanar waveguide fed structure and consists of three small radiating elements, whereas two OCSRR structures are coupled with the two‐radiating elements. The third radiating element embedded with OCSRR is connected using a PIN diode. Under DC biasing conditions the whole design resonates at three frequencies, that is,   1.9,   3.8, and 5.5   G H z, whereas the proposed design switches to dual band operation at 2.6 and 5.5   G H z in the diode ‘OFF’ state. The proposed structure is designed and fabricated on a single sided ungrounded FR 4 substrate. Overall, a good agreement between simulated and measured results is observed. It is also observed that the radiation patterns of the proposed antenna are the same with gains of   4.5,   3.2, and 2.5   d B i at 1.9 ,   3.8, and 5.5   G H z in the PIN diode ‘ON’ state, respectively, whereas in the diode ‘OFF’ state, at 2.6   G H z the gain is 4   d B i and at 5.5   G H z the gain is 2.5   d B i. [ABSTRACT FROM AUTHOR]

Abstract: In this article, a novel design of a compact planar Ultra Wideband (UWB) monopole antenna with dual band notched characteristics has been presented. The antenna has a unique structure meeting UWB standards. The proposed antenna is fed through a 50   Ω micro strip feed line, whereas a better impedance is achieved by truncating the ground plane. The dual notches are achieved by incorporating a meandered slot on the radiator patch and U‐slot in the feed line. The antenna is fabricated on FR‐ 4 substrate having a compact size of   33   ×   32   ×   1.5   m m 3. A good agreement is observed between measured and simulated results. The radiation pattern is omnidirectional in H‐plane, whereas dipole like radiation pattern is observed in the E‐plane. The gain of antenna is stable across the whole UWB except at notched bands. It is shown that any UWB can be notched at any desired frequency band by incorporating appropriate slot length. [ABSTRACT FROM AUTHOR]

Radio refractivity plays a significant role in the development and design of radio systems for attaining the best level of performance. Refractivity in the troposphere is one of the features affecting electromagnetic waves, and hence the communication system interrupts. In this work, a modified artificial neural network (ANN) based model is applied to predict the refractivity. The suggested ANN model comprises three modules: the data preparation module, the feature selection module, and the forecast module. The first module applies pre-processing to make the data compatible for the feature selection module. The second module discards irrelevant and redundant data from the input set. The third module uses ANN for prediction. The ANN model applies a sigmoid activation function and a multi-variate auto regressive model to update the weights during the training process. In this work, the refractivity is predicted and estimated based on ten years (2002–2011) of meteorological data, such as the temperature, pressure, and humidity, obtained from the Pakistan Meteorological Department (PMD), Islamabad. The refractivity is estimated using the method suggested by the International Telecommunication Union (ITU). The refractivity is predicted for the year 2012 using the database of the previous ten years, with the help of ANN. The ANN model is implemented in MATLAB. Next, the estimated and predicted refractivity levels are validated against each other. The predicted and actual values (PMD data) of the atmospheric parameters agree with each other well, and demonstrate the accuracy of the proposed ANN method. It was further found that all parameters have a strong relationship with refractivity, in particular the temperature and humidity. The refractivity values are higher during the rainy season owing to a strong association with the relative humidity. Therefore, it is important to properly cater the signal communication system during hot and humid weather. Based on the results, the proposed ANN method can be used to develop a refractivity database, which is highly important in a radio communication system. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid heuristic scheme based on two different basis functions i.e. Log Sigmoid and Bernstein Polynomial with unknown parameters is used for solving the nonlinear heat transfer equations efficiently. The proposed technique transforms the given nonlinear ordinary differential equation into an equivalent global error minimization problem. Trial solution for the given nonlinear differential equation is formulated using a fitness function with unknown parameters. The proposed hybrid scheme of Genetic Algorithm (GA) with Interior Point Algorithm (IPA) is opted to solve the minimization problem and to achieve the optimal values of unknown parameters. The effectiveness of the proposed scheme is validated by solving nonlinear heat transfer equations. The results obtained by the proposed scheme are compared and found in sharp agreement with both the exact solution and solution obtained by Haar Wavelet-Quasilinearization technique which witnesses the effectiveness and viability of the suggested scheme. Moreover, the statistical analysis is also conducted for investigating the stability and reliability of the presented scheme. [ABSTRACT FROM AUTHOR]

In this paper, a hexagonal magnet shape is proposed to have an arc profile capable of reducing torque ripples resulting from cogging torque in a single-sided axial flux permanent magnet (AFPM) machine. The arc-shaped permanent magnet increases the air-gap length effectively and makes the flux of the air-gap more sinusoidal, which decreases air-gap flux density and hence causes a reduction in cogging torque. Cogging torque is the basic source of vibration, along with the noise in PM machines, since it is the main cause of torque ripples. Cogging torque is independent of the load current and is proportional to the air-gap flux and the reluctance variation. Three-dimensional finite element analysis (FEA) is used in the JMAG-Designer to analyze the performance of the conventional and proposed hexagonal-shaped PM AFPM machines. The proposed shape is designed to reduce cogging torque, and the voltage remains the same as compared to the conventional hexagonal-shaped PM machine. Further, optimization is performed by utilizing an asymmetric overhang. Latin hypercube sampling (LHS) is used to create samples, the kriging method is applied to approximate the model, and a genetic algorithm is applied to obtain the optimum parameters of the machine. [ABSTRACT FROM AUTHOR]

Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate. [ABSTRACT FROM AUTHOR]