DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification

Tomato is an important and fragile crop. During the course of its development, it is frequently contaminated with bacteria or viruses. Tomato leaf diseases may be detected quickly and accurately, resulting in increased productivity and quality. Because of the intricate development environment of tomatoes and their inconspicuous disease spot features and small spot area, present machine vision approaches fail to reliably recognize tomato leaves. As a result, this research proposes a novel paradigm for detecting tomato leaf disease. The INLM (integration nonlocal means) filtering algorithm, for example, decreases the interference of surrounding noise on the features. Then, utilizing ResNeXt50 as the backbone, we create DCCAM-MRNet, a novel tomato image recognition network. Dilated Convolution (DC) was employed in STAGE 1 of the DCCAM-MRNet to extend the network\u27s perceptual area and locate the scattered disease spots on tomato leaves. The coordinate attention (CA) mechanism is then introduced to record cross-channel information and direction- and position-sensitive data, allowing the network to more accurately detect localized tomato disease spots. Finally, we offer a mixed residual connection (MRC) technique that combines residual block (RS-Block) and transformed residual block (TR-Block) (TRS-Block). This strategy can increase the network\u27s accuracy while also reducing its size. The DCCAM-classification MRNet\u27s accuracy is 94.3 percent, which is higher than the existing network, and the number of parameters is 0.11 M lesser than the backbone network ResNeXt50, according to the experimental results. As a result, combining INLM and DCCAM-MRNet to identify tomato diseases is a successful strategy

A Compact and Low Profile Loop Antenna with Six Resonant Modes for LTE Smart phone

In this paper, a novel six-mode loop antenna covering 660-1100 MHz, 1710-3020 MHz, 3370-3900 MHz, and 5150-5850 MHz has been proposed for the application of Long Term Evolution (LTE) including the coming LTE in unlicensed spectrum (LTE-U) and LTE-Licensed Assisted Access (LTE-LAA). Loop antennas offer better user experience than conventional Planar Inverted-F Antennas (PIFA), Inverted-F Antennas (IFA), and monopole antennas because of their unique balanced modes (1?, 2?, …). However, the bandwidth of loop antennas is usually narrower than that of PIFA/IFA and monopole antennas due to these balanced modes. To overcome this problem, a novel monopole/dipole parasitic element, which operates at an unbalanced monopole-like 0.25? mode and a balanced dipole-like 0.5? mode, is first proposed for loop antennas to cover more frequency bands. Benefiting from the balanced mode, the proposed parasitic element is promising to provide better user experience than conventional parasitic elements. To the authors’ knowledge, the balanced mode for a parasitic element is reported for the first time. The proposed antenna is able to provide excellent user experience while solving the problem of limited bandwidth in loop antennas. To validate the concept, one prototype antenna with the size of 75×10×5 mm3 is designed, fabricated and measured. Both simulations and experimental results are presented and discussed. Good performance is achieved

MPC-STANet: Alzheimer’s Disease Recognition Method based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism

Alzheimer\u27s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer\u27s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50

MPC-STANet: Alzheimer’s Disease Recognition Method based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism

Alzheimer\u27s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer\u27s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50

Multimode Decoupling Technique with Independent Tuning Characteristic for Mobile Terminals

The isolation between antenna elements is a key metric in some promising 5G technologies such as beamforming and in-band full-duplex (IBFD). However, multimode decoupling technology remains a great challenge especially for mobile terminals. One difficulty in achieving multi decoupling modes is that the operating modes of closely-packed decoupling elements have very strong mutual effect, which makes the tuning complicated and even unfeasible. Thus, in physical principle, a novel idea of achieving the stability of the boundary conditions of decoupling elements is proposed to solve the mutual effect problem; in physical structure, a metal boundary is adopted to realize the stability. One distinguished feature of the proposed technique is that the independent tuning characteristic can be maintained even if the number of decoupling elements increases. Therefore, wideband/multiband high isolation can be achieved by using multi decoupling elements. To validate the concept, two case studies are given. In a quad-mode decoupling design, the isolation is enhanced from 12.7 dB to > 21 dB within 22.0% bandwidth by using a 0.295?0 × 0.059?0 × 0.007?0 decoupling structure. The mechanism of decoupling technique and the mutual effect between decoupling elements are investigated

A Highly Integrated MIMO Antenna UnitA Highly Integrated MIMO Antenna Unit A: Differential/Common Mode Design

Abstract—A novel concept of antenna design, termed as differential/common mode (DM/CM) design, is proposed to achieve highly integrated multi-input multi-output (MIMO) antenna unit in mobile terminals. The inspiration comes from a dipole fed by a differential line which can be considered as a differential mode (DM) feed. What will happen if the DM feed is transformed into a common mode (CM) feed? Some interesting features are found in this research. By symmetrically placing one DM antenna and one CM antenna together, a DM/CM antenna can be achieved. Benefitting from the coupling cancellation of anti-phase currents and the different distributions of the radiation currents, a DM/CM antenna can obtain high isolation and complementary patterns, even if the radiators of the DM and CM antennas are overlapped. Therefore, good MIMO performance can be realized in a very compact volume. To validate the concept, a miniaturized DM/CM antenna unit is designed for mobile phones. 24.2 dB isolation and complementary patterns are achieved in the dimension of 0.330λ0×0.058λ0×0.019λ0 at 3.5 GHz. One 8×8 MIMO antenna array is constructed by using four DM/CM antenna units and shows good overall performance. The proposed concept of DM/CM design may also be promising for other applications that need high isolation between closely-packed antenna elements and wide-angle pattern coverage

Machine learning-based early diagnosis of autism according to eye movements of real and artificial faces scanning

BackgroundStudies on eye movements found that children with autism spectrum disorder (ASD) had abnormal gaze behavior to social stimuli. The current study aimed to investigate whether their eye movement patterns in relation to cartoon characters or real people could be useful in identifying ASD children.MethodsEye-tracking tests based on videos of cartoon characters and real people were performed for ASD and typically developing (TD) children aged between 12 and 60 months. A three-level hierarchical structure including participants, events, and areas of interest was used to arrange the data obtained from eye-tracking tests. Random forest was adopted as the feature selection tool and classifier, and the flattened vectors and diagnostic information were used as features and labels. A logistic regression was used to evaluate the impact of the most important features.ResultsA total of 161 children (117 ASD and 44 TD) with a mean age of 39.70 ± 12.27 months were recruited. The overall accuracy, precision, and recall of the model were 0.73, 0.73, and 0.75, respectively. Attention to human-related elements was positively related to the diagnosis of ASD, while fixation time for cartoons was negatively related to the diagnosis.ConclusionUsing eye-tracking techniques with machine learning algorithms might be promising for identifying ASD. The value of artificial faces, such as cartoon characters, in the field of ASD diagnosis and intervention is worth further exploring

Genome-wide identification and analysis of heterotic loci in three maize hybrids

Heterosis, or hybrid vigour, is a predominant phenomenon in plant genetics, serving as the basis of crop hybrid breeding, but the causative loci and genes underlying heterosis remain unclear in many crops. Here, we present a large-scale genetic analysis using 5360 offsprings from three elite maize hybrids, which identifies 628 loci underlying 19 yield-related traits with relatively high mapping resolutions. Heterotic pattern investigations of the 628 loci show that numerous loci, mostly with complete–incomplete dominance (the major one) or overdominance effects (the secondary one) for heterozygous genotypes and nearly equal proportion of advantageous alleles from both parental lines, are the major causes of strong heterosis in these hybrids. Follow-up studies for 17 heterotic loci in an independent experiment using 2225 F2 individuals suggest most heterotic effects are roughly stable between environments with a small variation. Candidate gene analysis for one major heterotic locus (ub3) in maize implies that there may exist some common genes contributing to crop heterosis. These results provide a community resource for genetics studies in maize and new implications for heterosis in plants