Allelic variation at the Vrn-A1, Vrn-B1, Vrn-D1, Vrn-B3 and Ppd-D1a loci of Pakistani spring wheat cultivars

Flowering time in bread wheat (Triticum aestivum L.) is controlled by vernalization and photoperiod response, and earliness per se genes. The genetic basis of flowering time has not been investigated in Pakistani bread wheat. This study was, therefore, conducted to determine the allelic composition at Vrn-A1, Vrn-B1, Vrn-D1, Vrn-B3 and Ppd-D1a loci of 59 Pakistani spring bread wheat cultivars. These cultivars, along with 4 isogenic lines for vernalization genes were characterized with previously reported DNA markers designed for detecting allelic variation at 4 Vrn (Vernalization) and 1 Ppd (Photoperiod) loci. Spring habit Vrn-A1a allele was found in 36% cultivars either alone or with spring habit Vrn-B1 and Vrn-D1 alleles. Two wheat cultivars had the dominant Vrn-A1c allele, whereas none of the cultivars had Vrn-A1b. Spring habit Vrn-B1 was the most frequent allele (64%) present either alone or with Vrn-A1a, Vrn-A1c and Vrn-D1. Spring habit Vrn-D1 was found in 61% cultivars. Vrn-D1 was singly found in 25% cultivars and along with Vrn-B1 in 29% cultivars. Dominant Vrn-B3 was absent in all cultivars studied. All cultivars except Era had the photoperiod insensitive allele Ppd-D1a. We did not find any association between the flowering time and Vrn allelic composition of the studied cultivars. This indicated that the partial vernalization requirement of cultivars with Vrn-B1 and Vrn-D1 alleles is probably fulfilled during Pakistani growing season. Earliness per se and the photoperiod sensitive loci other than Ppd-D1 need to be investigated to further understand the genetic basis of flowering time in Pakistani wheat

Design and Development of Ultrabroadband, High-Gain, and High-Isolation THz MIMO Antenna with a Complementary Split-Ring Resonator Metamaterial

The need for high-speed communication has created a way to design THz antennas that operate at high frequencies, speeds, and data rates. In this manuscript, a THz MIMO antenna is designed using a metamaterial. The two-port antenna design proposed uses a complementary splitring resonator patch. The design results are also compared with a simple patch antenna to show the improvement. The design shows a better isolation of 50 dB. A broadband width of 8.3 THz is achieved using this complementary split-ring resonator design. The percentage bandwidth is 90%, showing an ultrabroadband response. The highest gain of 10.34 dB is achieved with this design. Structural parametric optimization is applied to the complementary split-ring resonator MIMO antenna design. The designed antenna is also optimized by applying parametric optimization to different geometrical parameters. The optimized design has a 20 μm ground plane, 14 μm outer ring width, 6 μm inner ring width, and 1.6 μm substrate thickness. The proposed antenna with its broadband width, high gain, and high isolation could be applied in high-speed communication devices

SARS-CoV-2 detecting rapid metasurface-based sensor

We have proposed a novel approach to detect COVID-19 by detecting the ethyl butanoate which high volume ratio is present in the exhaled breath of a COVID-19 infected person. We have employed a refractive index sensor (RIS) with the help of a metasurface-based slotted T-shape perfect absorber that can detect ethyl butanoate present in exhaled breath of COVID-19 infected person with high sensitivity and in-process SARS-CoV-2. The optimized structure of the sensor is obtained by varying several structure parameters including structure length and thickness, slotted T-shape resonator length, width, and thickness. Sensor’s performance is evaluated based on numerous factors comprising of sensitivity, Q factor, detection limit, a figure of merit (FOM), detection accuracy, and other performance defining parameters. The proposed slotted T-shape RIS achieved the largest sensitivity of 2500 nm/RIU, Q factor of 131.06, a FOM of 131.58 RIU-1 , detection limit of 0.0224 RIU

6G Network Architecture Using FSO-PDM/PV-OCDMA System with Weather Performance Analysis

This paper presents a novel 160 Gbps free space optics (FSO) communication system for 6G applications. Polarization division multiplexing (PDM) is integrated with an optical code division multiple access (OCDMA) technique to form a PDM-OCDMA hybrid. There are two polarization states: one is X-polarization generated from adjusting the azimuthal angle of a light source at 0° while the other is Y-polarization which is generated by adjusting the azimuthal angle of a light source at 90°. Each polarization state is used for the transmission of four independent users. Each channel is assigned by permutation vector (PV) codes and carries 20 Gbps data. Four different weather conditions are considered for evaluating the performance of our proposed model. These weather conditions are clear air (CA), foggy conditions (low fog (LF), medium fog (MF), and heavy fog (HF)), dust storms (low dust storm (LD), moderate dust storm (MD), heavy dust storm (HD)), and snowfall (wet snow (WS) and dry snow (DS)). Bit error rate (BER), Q-factors, maximum propagation range, channel capacity, and eye diagrams are used for evaluating the performance of the proposed model. Simulation results assure successful transmission of 160 Gbps overall capacity for eight channels. The longest FSO range is 7 km which occurred under CA while the minimum is achieved under HD, which is 0.112 km due to large attenuation caused by HD. Within fog conditions, the maximum propagation distances are 1.525 km in LF, 1.05 km in MF, and 0.85 km in HF. Likewise, under WS and DS, the proposed system can support transmission distances of 1.15 km and 0.28 km, respectively. All these transmission distances are achieved at BER less than 10−5

Optical Transmission Plasmonic Color Filter withWider ColorGamut Based on X-Shaped Nanostructure

Extraordinary Optical Transmission Plasmonic Color Filters (EOT-PCFs) with nanostructures have the advantages of consistent color, small size, and excellent color reproduction, making them a suitable replacement for colorant-based filters. Currently, the color gamut created by plasmonic filters is limited to the standard red, green, blue (sRGB) color space, which limits their use in the future. To address this limitation, we propose a surface plasmon resonance (SPR) color filter scheme, which may provide a RGB-wide color gamut while exceeding the sRGB color space. On the surface of the aluminum film, a unique nanopattern structure is etched. The nanohole functions as a coupled grating that matches photon momentum to plasma when exposed to natural light. Metals and surfaces create surface plasmon resonances as light passes through the metal film. The plasmon resonance wavelength can be modified by modifying the structural parameters of the nanopattern to obtain varied transmission spectra. The International Commission on Illumination (CIE 1931) chromaticity diagram can convert the transmission spectrum into color coordinates and convert the spectrum into various colors. The color range and saturation can outperform existing color filters.Funding: This project has received funding from Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538

Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm

For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).This project has received funding from Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538

Crops leaf diseases recognition: a framework of optimum deep learning features

Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning. Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm. The best selected features are finally classified using machine learning classifiers such as SVM, and named a few more for final classification results. The proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant Village. The proposed architecture achieved an accuracy of 100.0%, 92.9%, and 99.2%, respectively. A comparison with recent techniques is also performed, revealing that the proposed method achieved improved accuracy while consuming less computational time

Metasurface-Inspired Flexible Wearable MIMO Antenna Array for Wireless Body Area Network Applications and Biomedical Telemetry Devices

This article presents a sub-6GHz ISM-band flexible wearable MIMO antenna array for wireless body area networks (WBANs) and biomedical telemetry devices. The array is based on metasurface inspired technology. The antenna array consists of 2×2 matrix of triangular-shaped radiation elements that were realized on 0.8 mm thick Rogers RT/duroid 5880 substrate. Radiation characteristics of the array are enhanced by isolating the surface current interaction between the individual radiators in the array. This is achieved by inserting an electromagnetic bandgap (EBG) decoupling structure between the radiating elements. The radiating elements were transformed into a metasurface by etching sub-wavelength slots inside them. The periodic arrangement of slots acts like resonant scatterers that manipulate the electromagnetic response of the surface. Results confirm that by employing the decoupling structure and sub-wavelength slots the isolation between the radiators is significantly improved (>34.8 dB). Moreover, there is an improvement in the array’s fractional bandwidth, gain and the radiation efficiency. The optimized array design for operation over 5.0-6.6 GHz has an average gain and efficiency of 10 dBi and 83%, respectively. Results show that the array’s performance is not greatly affected by a certain amount of bending. In fact, the antenna maintains a gain between 8.65-10.5 dBi and the efficiency between 77-83%. The proposed MIMO antenna array is relatively compact, can be easily fabricated on one side of a dielectric material, allows easy integration with RF circuitry, is robust, and maintains its characteristics with some bending. These features make it suitable for various wearable applications and biomedical telemetry devices