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Watermelon (Citrullus lanatus) is the third largest fruit crop in the world in term of production. However, it is susceptible to several viruses. Watermelon vine decline (WVD), caused by whitefly-transmitted squash vein yellowing virus (SqVYV), is a disease that has caused over $60 million in losses in the US and continues to occur regularly in southeastern states. Understanding the molecular mechanisms underlying resistance to SqVYV is important for effective disease management. A time-course transcriptomic analysis was conducted on resistant (392291-VDR) and susceptible (Crimson Sweet) watermelon genotypes inoculated with SqVYV. Significantly higher levels of SqVYV were observed over time in the susceptible compared to the resistant genotype. The plasmodesmata callose binding protein (PDCB) gene, which is responsible for increased callose deposition in the plasmodesmata, was more highly expressed in the resistant genotype than in the susceptible genotype before and after inoculation, suggesting the inhibition of cell-to-cell movement of SqVYV. The potential role of the RNA interference (RNAi) pathway was observed in the resistant genotype based on differential expression of eukaryotic initiation factor (eIF), translin, DICER, ribosome inactivating proteins, RNA-dependent RNA polymerase (RDR), and Argonaute (AGO) genes after inoculation. The significant differential expression of hormone-related genes, including those involved in the ethylene, jasmonic acid, auxin, cytokinin, gibberellin, and salicylic acid signaling pathways, was observed, emphasizing their regulatory roles in the defense response. Genes regulating pectin metabolism, cellulose synthesis, cell growth and development, xenobiotic metabolism, and lignin biosynthesis were overexpressed in the susceptible genotype, suggesting that alterations in cell wall integrity and growth processes result in disease symptom development. These findings will be helpful for further functional studies and the development of SqVYV-resistant watermelon cultivars. [ABSTRACT FROM AUTHOR]

Along with regular position-related applications, observations from navigation satellite constellations can also be utilised for atmospheric studies. The present study deals with the 3D water vapour tomography carried out for the first time using Indian GPS stations set up in the Himalayan region. A voxel-based approach is applied to retrieve the vertical profile of precipitable water vapour (PWV). Horizontal 4×4 grids have been formed between 29°–31° latitude and 77.5°–79.5° longitude with 0.5° spatial resolution. To construct a tomography model, voxels are formed over these grids up to 10 km with 0.5 km vertical resolution. Water vapour density (WVD) is estimated for different days to observe its variations under different atmospheric conditions. The results show that the estimated vertical variation of water vapour closely matches the AIRS WVD profile. To compare the results, AIRS WVD observations are interpolated at the same altitude where the tomographic estimations are available. The average root mean square (RMS) error, standard deviation (SD), and mean absolute error (MAE) between estimated water vapour density and AIRS WVD are observed to be 1.52, 1.32 and 2.02 g/m3, respectively. The coefficient of determination (R2) value is observed to be close to 0.9, reflecting an excellent linearity between estimated and observed values. The results also show that the WVD is high up to the 5 km voxel and it is between 0 and 5 g/m3 above 5 km altitude. A good estimation of vertical water vapour profile has been obtained using CORS observations and can be used for further atmospheric analyses in this region. [ABSTRACT FROM AUTHOR]

Watermelon (Citrullus lanatus) is the third largest fruit crop in the world in term of production. However, it is susceptible to several viruses. Watermelon vine decline (WVD), caused by whitefly-transmitted squash vein yellowing virus (SqVYV), is a disease that has caused over $60 million in losses in the US and continues to occur regularly in southeastern states. Understanding the molecular mechanisms underlying resistance to SqVYV is important for effective disease management. A time-course transcriptomic analysis was conducted on resistant (392291-VDR) and susceptible (Crimson Sweet) watermelon genotypes inoculated with SqVYV. Significantly higher levels of SqVYV were observed over time in the susceptible compared to the resistant genotype. The plasmodesmata callose binding protein (PDCB) gene, which is responsible for increased callose deposition in the plasmodesmata, was more highly expressed in the resistant genotype than in the susceptible genotype before and after inoculation, suggesting the inhibition of cell-to-cell movement of SqVYV. The potential role of the RNA interference (RNAi) pathway was observed in the resistant genotype based on differential expression of eukaryotic initiation factor (eIF), translin, DICER, ribosome inactivating proteins, RNA-dependent RNA polymerase (RDR), and Argonaute (AGO) genes after inoculation. The significant differential expression of hormone-related genes, including those involved in the ethylene, jasmonic acid, auxin, cytokinin, gibberellin, and salicylic acid signaling pathways, was observed, emphasizing their regulatory roles in the defense response. Genes regulating pectin metabolism, cellulose synthesis, cell growth and development, xenobiotic metabolism, and lignin biosynthesis were overexpressed in the susceptible genotype, suggesting that alterations in cell wall integrity and growth processes result in disease symptom development. These findings will be helpful for further functional studies and the development of SqVYV-resistant watermelon cultivars.

In the current era, satisfying the appetite of data hungry models is becoming an increasingly challenging task. This challenge is particularly magnified in research areas characterised by sensitivity, where the quest for genuine data proves to be elusive. The study of violence serves as a poignant example, entailing ethical considerations and compounded by the scarcity of authentic, real-world data that is predominantly accessible only to law enforcement agencies. Existing datasets in this field often resort to using content from movies or open-source video platforms like YouTube, further emphasising the scarcity of authentic data. To address this, our dataset aims to pioneer a new approach by creating the first synthetic virtual dataset for violence detection, named the Weapon Violence Dataset (WVD). The dataset is generated by creating virtual violence scenarios inside the photo-realistic video game namely: Grand Theft Auto-V (GTA-V). This dataset includes carefully selected video clips of person-to-person fights captured from a frontal view, featuring various weapons—both hot and cold across different times of the day. Specifically, WVD contains three categories: Hot violence and Cold violence (representing the violence category) as well as No violence (constituting the control class). The dataset is designed and created in a way that will enable the research community to train deep models on such synthetic data with the ability to increase the data corpus if the needs arise. The dataset is publicly available on Kaggle and comprises normal RGB and optic flow videos.

Manually labelling datasets for training violence detection systems is time-consuming, expensive, and labor-intensive. Mind wandering, boredom, and short attention span can also cause labelling errors. Moreover, collecting and distributing sensitive images containing violence has ethical implications. Automation is the future for labelling sensitive image datasets. Deep labeller is a two-stage Deep Learning (DL) method that uses pre-trained DL object detection methods on MS-COCO for automatic labelling. The Deep Labeller method labels violent and nonviolent images in WVD and USI. In stage 1, WVD generates weak labels using synthetic images. In stage 2, the Deep labeller method is retrained on weak labels. USI dataset is used to test our method on real-world violence. Deep labeller generated weak and strong labels with an IoU of 0.80036 in stage 1 and 0.95 in stage 2 on the WVD. Automatically generated labels. To test our method's generalisation power, violent and nonviolent image labels on USI dataset had a mean IoU of 0.7450. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation performance may degrade substantially when linear frequency modulation (LFM) signals are spectrally-overlapped in time-frequency (TF) domain. In order to solve this problem, the single-source TF points selection algorithm based on Wigner-Ville distribution (WVD) and Hough transform is studied in this paper. Firstly, the signal intersections in TF domain can be solved based on the Hough transform. Secondly, by removing multiple-source TF points at intersections according to the empirical threshold value which is calculated by using the statistical experiment method, we can get single-source TF points set. Then, based on the Euclidean distance operator, single-source TF points set belonging to each signal can be obtained according to the property that TF points of the same signal have the same eigenvector. Finally, the averaged spatial TF distribution matrix is constructed and DOA estimation is realized based on the multiple signal classification (MUSIC) algorithm. In this way, the proposed algorithm can resolve the TF non-disjoint LFM signals because it can automatically select single-source TF points set of each signal. Simulation results illustrate that the proposed algorithm possesses higher angular resolution and has pretty good DOA estimation precision compared with existing algorithms.

In the current era, satisfying the appetite of data hungry models is becoming an increasingly challenging task. This challenge is particularly magnified in research areas characterised by sensitivity, where the quest for genuine data proves to be elusive. The study of violence serves as a poignant example, entailing ethical considerations and compounded by the scarcity of authentic, real-world data that is predominantly accessible only to law enforcement agencies. Existing datasets in this field often resort to using content from movies or open-source video platforms like YouTube, further emphasising the scarcity of authentic data. To address this, our dataset aims to pioneer a new approach by creating the first synthetic virtual dataset for violence detection, named the Weapon Violence Dataset (WVD). The dataset is generated by creating virtual violence scenarios inside the photo-realistic video game namely: Grand Theft Auto-V (GTA-V). This dataset includes carefully selected video clips of person-to-person fights captured from a frontal view, featuring various weapons-both hot and cold across different times of the day. Specifically, WVD contains three categories: Hot violence and Cold violence (representing the violence category) as well as No violence (constituting the control class). The dataset is designed and created in a way that will enable the research community to train deep models on such synthetic data with the ability to increase the data corpus if the needs arise. The dataset is publicly available on Kaggle and comprises normal RGB and optic flow videos., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Direction of arrival (DOA) estimation performance may degrade substantially when linear frequency modulation (LFM) signals are spectrally-overlapped in time-frequency (TF) domain. In order to solve this problem, the single-source TF points selection algorithm based on Wigner-Ville distribution (WVD) and Hough transform is studied in this paper. Firstly, the signal intersections in TF domain can be solved based on the Hough transform. Secondly, by removing multiple-source TF points at intersections according to the empirical threshold value which is calculated by using the statistical experiment method, we can get single-source TF points set. Then, based on the Euclidean distance operator, single-source TF points set belonging to each signal can be obtained according to the property that TF points of the same signal have the same eigenvector. Finally, the averaged spatial TF distribution matrix is constructed and DOA estimation is realized based on the multiple signal classification (MUSIC) algorithm. In this way, the proposed algorithm can resolve the TF non-disjoint LFM signals because it can automatically select single-source TF points set of each signal. Simulation results illustrate that the proposed algorithm possesses higher angular resolution and has pretty good DOA estimation precision compared with existing algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new method for detecting EEG spikes using the time-frequency distribution of the signal. As spikes are short-time broadband events, their energy patterns are represented as ridges in the time-frequency domain. In this domain, the high instantaneous energy of the spikes makes them more distinguishable from the background. To detect spikes, the time-frequency distribution of the signal of interestis first enhanced to attenuate the noise. Two frequency slices of the enhanced time-frequency distribution arethen extracted and subjected to the smoothed nonlinear energy operator (SNEO). Finally, the output of the SNEO is thresholded to localise the position of the spikes in the signal. The SNEO is employed to accentuate the spike signature in the extracted frequency slices. A spike is considered to exist in the time domain signalif the spike signature is detected at the same position in both frequency slices. The performance of the proposed method is evaluated and compared with an existing spike detection method using both synthetic and newborn EEG signals.

The article discusses a new methodology for automated identification of sleep stage in full-term and pre-term neonates based on the time frequency distribution of single electroencephalogram (EEG) recording and artificial neural networks (ANN). Topics covered include the reliable sleep stage scoring method developed by the authors, the materials and methods used in developing the proposed automated sleep stage identification methodology, and the advantages of using the proposed methodology.

Abstract: Lidar is an efficient tool for remotely measuring physical quantities or detecting targets. To improve the range resolution in long pulse lidars, such as lidar systems based on TEA–CO2 lasers, deconvolution methods were used by previous investigators. Deconvolution is a noise sensitive process. In order to avoid noise amplification during the deconvolution process, the Fourier–wavelet regularized deconvolution method is used to deconvolve and denoise the back-scattered lidar signal simultaneously. This method is applied to lidar systems based on the TEA–CO2 laser and the results are compared to nitrogen tail clipping method. Numerical simulation shows, in comparison to the clipping nitrogen tail technique, by using the ForWaRD method; the range resolution and working distance of the lidar is improved and the clipping tail apparatus is also eliminated. [Copyright &y& Elsevier]

The paper proposes a new method for computation of the Wigner-Ville distribution(WVD) taking account of the conjugate symmetry of the WVD kernel function and the periodicity and symmetry of the trigonometric function. The method transfers the computation of WVD into real field from complex field to remove the redundancies in the fast Fourier transform(FFT) computation. The realization of fast cosine operation and fast sine operation considerably reduces the computation cost. Theoretical analysis shows that the algorithm provided in the paper is much more advantageous than the existed ones. [ABSTRACT FROM AUTHOR]

We propose a method that uses linear chirp modulated Gaussian functions as the elementary functions, by adaptively adjusting variances, time-frequency centers and sweep rates, to decompose signals. By taking WVD, an improved adaptive time-frequency distribution is developed, which is non-negative, free of cross-term interference, and of better time-frequency resolution. The paper presents an effective numerical algorithm to estimate the optimal parameters of the basis. Simulations indicate that the proposed approach is effective in analyzing signal’s time-frequency behavior. [ABSTRACT FROM AUTHOR]

A fully automatic method for optimising the smoothed‐pseudo Wigner‐Ville distribution is presented. An alternative view on time–frequency representations (TFRs) through correlation intensity landscapes is offered, and the notion of wanted and unwanted correlations and their balance is introduced which is introduced as the measure of quality. The problem of optimising TFRs is then formulated as the cost function based on this measure. With the differential evolution algorithm as the solver, the method's performance is tested on synthetic and real‐life signals. [ABSTRACT FROM AUTHOR]