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Optimizing antenna arrays is essential for achieving efficient beamforming with very low sidelobe level (SLL) where adopting tapered window functions is one of the straightforward efficient techniques for achieving this goal. Recently, two-dimensional (2D) beamforming has been extensively required for many applications; therefore, this paper proposes two extrapolation techniques applied to one-dimensional (1D) tapered functions to efficiently feed 2D antenna arrays using cross-linear and adaptive radial tapering techniques. The first proposed 2D cross-linear tapering technique determines the 2D tapering coefficients by Hadamard multiplication of two right-angled grids of repeated 1D functions, while the second proposed adaptive radial tapering technique locates the antenna element in the 2D array in terms of its radial distance with respect to the array center, then converts this distance to an element index in a virtual 1D tapering window to determine the element weighting value. The adaptive radial tapering technique is optimized for achieving the minimum SLLs. The two proposed techniques are analyzed and discussed, where it is found that the adaptive radial tapering provides deeper SLLs compared to the cross-linear tapering technique. The two extrapolation techniques are examined for four window functions including triangular (Bartlett), Hamming, cosine-square, and Blackman windows, and the simulation results show that for extrapolating the Blackman window using adaptive radial tapering, a −50 dB SLL can be achieved which is independent on the array size, while cross-linear tapering provides −35 dB and −41 dB SLLs for 16 × 16 and 32 × 32 antenna arrays, respectively. [ABSTRACT FROM AUTHOR]

Deep learning-based machine learning models have shown significant results in speech recognition and numerous vision-related tasks. The performance of the present speech-to-text model relies upon the hyperparameters used in this research work. In this research work, it is shown that convolutional neural networks (CNNs) can model raw and tonal speech signals. Their performance is on par with existing recognition systems. This study extends the role of the CNN-based approach to robust and uncommon speech signals (tonal) using its own designed database for target research. The main objective of this research work was to develop a speech-to-text recognition system to recognize the tonal speech signals of Gurbani hymns using a CNN. Further, the CNN model, with six layers of 2DConv, 2DMax Pooling, and 256 dense layer units (Google's TensorFlow service) was also used in this work, as well as Praat for speech segmentation. Feature extraction was enforced using the MFCC feature extraction technique, which extracts standard speech features and features of background music as well. Our study reveals that the CNN-based method for identifying tonal speech sentences and adding instrumental knowledge performs better than the existing and conventional approaches. The experimental results demonstrate the significant performance of the present CNN architecture by providing an 89.15% accuracy rate and a 10.56% WER for continuous and extensive vocabulary sentences of speech signals with different tones. [ABSTRACT FROM AUTHOR]

Featured Application: Diabetes is a common chronic disorder defined by excessive glucose levels in the blood. A good diagnosis of diabetes may make a person's life better; otherwise, it can cause kidney failure, major heart damage, and damage to the blood vessels and nerves. As a result, diabetes classification and diagnosis are vital tasks. By using our proposed methodology, clinicians may obtain complete information about their patients using real-time monitoring. To gain new insights, they can combine historical information with current data, making it easier for them to perform more thorough and comprehensive treatments than before, and they will be able to provide proactive care, which will help to improve health outcomes and reduce hospital re-admissions. Diabetes is a long-term illness caused by the inefficient use of insulin generated by the pancreas. If diabetes is detected at an early stage, patients can live their lives healthier. Unlike previously used analytical approaches, deep learning does not need feature extraction. In order to support this viewpoint, we developed a real-time monitoring hybrid deep learning-based model to detect and predict Type 2 diabetes mellitus using the publicly available PIMA Indian diabetes database. This study contributes in four ways. First, we perform a comparative study of different deep learning models. Based on experimental findings, we next suggested merging two models, CNN-Bi-LSTM, to detect (and predict) Type 2 diabetes. These findings demonstrate that CNN-Bi-LSTM surpasses the other deep learning methods in terms of accuracy (98%), sensitivity (97%), and specificity (98%), and it is 1.1% better compared to other existing state-of-the-art algorithms. Hence, our proposed model helps clinicians obtain complete information about their patients using real-time monitoring and can check real-time statistics about their vitals. [ABSTRACT FROM AUTHOR]

Nowadays, the usage of renewable energy resources (RER) is growing rapidly, but at the same time, the effective utilization of RER is also a challenging task. For the better usage of RER and the reduction of loss, the dual battery storage is proposed. The main aim of this work is to focus on the design and implementation of a reliable and renewable power generating system under a robust situation, along with a battery storage system. The perturb and observe (P&O) maximum power point tracking (MPPT) technique has been applied to improve the solar photovoltaic power production. In addition, the dual battery storage system is being introduced to improve the life cycle of the primary storage system. The proposed dual storage system is highly preferable for remote, location-based application systems, space applications and military operations. In the dual battery storage system, the batteries are working effectively with a good lifespan, when compared with the existing methods. To determine the state of charge (SOC) and depth of discharge (DOC), those batteries' input charging and discharging levels were monitored closely. MATLAB Simulink (R2013) is used for simulation; finally, a real-time, three-phase inverter was designed and validated. Under this dual battery storage mode, the life time of battery is improved. [ABSTRACT FROM AUTHOR]

In this paper, a new beamforming technique for planar two-dimensional arrays is proposed for optimizing the sidelobe levels (SLLs) by using recursive multibeam pattern subtraction (MPS) technique. The proposed MPS beamformer is demonstrated and its convergence to lower SLL values is investigated and controlled. The performance analysis has shown that the proposed MPS beamformer can effectively reduce the SLL down to less than −50 dB relative to the mainlobe level utilizing the major sidelobes information in the radiation pattern. In addition, the proposed MPS beamformer can be applied to any planar array geometry such as rounded corners rectangular arrays provided that the original array pattern contains sidelobe peaks. The comparison with recent related techniques has shown that the proposed beamformer provides faster convergence time. On the other hand, the proposed technique provides lower sidelobe levels which cannot be achieved by efficient tapering windows for planar two-dimensional arrays. Finally, the scanning performance of the proposed MPS beamformer is demonstrated and the simulation results show solid and consistent SLL levels over the whole angular range from the broadside to endfire directions of the array. [ABSTRACT FROM AUTHOR]

Antenna arrays have become an essential part of most wireless communications systems. In this paper, the unwanted sidelobes in the symmetric linear array power pattern are reduced efficiently by utilizing a faster simultaneous sidelobes processing algorithm, which generates nulling sub-beams that are adapted to control and maintain steep convergence toward lower sidelobe levels. The proposed algorithm is performed using adaptive damping and heuristic factors which result in learning curve perturbations during the first few loops of the reduction process and is followed by a very steep convergence profile towards deep sidelobe levels. The numerical results show that, using the proposed adaptive sidelobes simultaneous reduction algorithm, a maximum sidelobe level of −50 dB can be achieved after only 10 iteration loops (especially for very large antenna arrays formed by 256 elements, wherein the processing time is reduced to approximately 25% of that required by the conventional fixed damping factor case). On the other hand, the generated array weights can be applied to practical linear antenna arrays under mutual coupling effects, which have shown very similar results to the radiation pattern of the isotropic antenna elements with very deep sidelobe levels and the same beamwidth. [ABSTRACT FROM AUTHOR]

In this paper, an efficient sidelobe levels (SLL) reduction and spatial filtering algorithm is proposed for linear one-dimensional arrays. In this algorithm, the sidelobes are beamspace processed simultaneously based on its orientation symmetry to achieve very deep SLL at much lower processing time compared with recent techniques and is denoted by the sidelobes simultaneous reduction (SSR) algorithm. The beamwidth increase due to SLL reduction is found to be the same as that resulting from the Dolph-Chebyshev window but at considerably lower average SLL at the same interelement spacing distance. The convergence of the proposed SSR algorithm can be controlled to guarantee the achievement of the required SLL with almost steady state behavior. On the other hand, the proposed SSR algorithm has been examined for spatial selective sidelobe filtering and has shown the capability to effectively reduce any angular range of the radiation pattern effectively. In addition, the controlled convergence capability of the proposed SSR algorithm allows it to work at any interelement spacing distance, which ranges from tenths to a few wavelength distances, and still provide very low SLL. [ABSTRACT FROM AUTHOR]

Recently, antenna array radiation pattern synthesis and adaptation has become an essential requirement for most wireless communication systems. Therefore, this paper proposes a new recursive sidelobe level (SLL) reduction algorithm using a sidelobe sequential damping (SSD) approach based on pattern subtraction, where the sidelobes are sequentially reduced to the optimum required levels with near-symmetrical distribution. The proposed SSD algorithm is demonstrated, and its performance is analyzed, including SLL reduction and convergence behavior, mainlobe scanning, processing speed, and performance under mutual coupling effects for uniform linear and planar arrays. In addition, the SSD performance is compared with both conventional tapering windows and optimization techniques, where the simulation results show that the proposed SSD approach has superior maximum and average SLL performances and lower processing speeds. In addition, the SSD is found to have a constant SLL convergence profile that is independent on the array size, working effectively on any uniform array geometry with interelement spacing less than one wavelength, and deep SLL levels of less than −70 dB can be achieved relative to the mainlobe level, especially for symmetrical arrays. [ABSTRACT FROM AUTHOR]

In this paper, a new conformal array structure and beamforming technique are proposed to provide efficient communication performance for unmanned aerial vehicles (UAVs) and space vehicles. The proposed array is formed by conformally stacking cylindrical, conical, and concentric circular (CSC4) arrays which are all coaxially aligned with the same axis of the conformed body and with uniform interelement spacing. The array elements are then fed by a weighting vector that has an adaptive cosine tapered profile where the maximum amplitude coefficient is oriented with the mainlobe direction to improve the scanning capabilities of the array and increase the array effective area. In addition, for very large, conformed body structures such as space vehicles, a frontal mainlobe-oriented partial CSC4 array beamforming technique is proposed to efficiently utilize the large CSC4 structure, reduce the processing requirements for mainlobe electronic steering, and to provide very low sidelobe levels with reduced backlobe levels. Simulation results show that the proposed CSC4 design can provide wide scanning angles of up to ± 70 ° angular range in the θ -direction with only ± 1 ° change in the beamwidth, without increasing array size and with achievable sidelobe level of −45 dB and backlobe levels less than −10 dB. [ABSTRACT FROM AUTHOR]

Recent advances in communication systems provide an enabling technology for aircraft connection and safety. A promising communication system that uses stratospheric platforms provides an efficient and improved communication performance and can be an efficient solution for establishing communication networks for aviation. Therefore, in this paper, a novel communication network based on stratospheric basestation (SB) is proposed to provide fifth-generation (5G) and beyond services for civil aviation aircrafts to improve global flight connectivity, control, and safety. The proposed aircraft–SB network is demonstrated, and its coverage geometry is modelled and investigated. As the 5G and beyond networks use millimeter wave frequency bands (mm-wave), the performance of different atmospheric losses including gaseous absorption, rain, and fog/cloud is analyzed to investigate the system's practical feasibility at different 5G proposed frequencies ranging from 3.5 to 66 GHz through a flight model including three distinct stages which are takeoff/landing, climbing/descending, and cruise stages. Also, handover scenarios in the proposed aircraft–SB network are investigated and analyzed at the proposed 5G frequencies. In addition, the aircraft–SB 5G network is compared to the most recent low-Earth orbit (LEO) Internet satellites where the proposed system is expected to provide low latency, less atmospheric attenuation, longer aircraft–SB link duration, and very low handover rate. [ABSTRACT FROM AUTHOR]