Modern wireless communications gadgets demand multi-standard communications facilities with least overlap between different input radio channels. A sharp digital filter of extremely narrow transition-width with lower ...Modern wireless communications gadgets demand multi-standard communications facilities with least overlap between different input radio channels. A sharp digital filter of extremely narrow transition-width with lower stop band ripples offers alias-free switching among the preferred frequency bands. A computationally competent low pass filter (LPF) structure based on the multistage frequency response masking (FRM) approach is proposed for the design of sharp finite impulse response (FIR) filters which are suitable for wireless communications applications. In comparison of basic FRM with other existing multistage FRM structures, the proposed structure has a narrow transition bandwidth and higher stop band attenuation with significant reduction in terms of the number of computational steps. A design example is incorporated to demonstrate the efficiency of the proposed approach. Simulation results establish the improvement of the proposed scheme over other recently published design strategies.展开更多
In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficien...In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.展开更多
文摘Modern wireless communications gadgets demand multi-standard communications facilities with least overlap between different input radio channels. A sharp digital filter of extremely narrow transition-width with lower stop band ripples offers alias-free switching among the preferred frequency bands. A computationally competent low pass filter (LPF) structure based on the multistage frequency response masking (FRM) approach is proposed for the design of sharp finite impulse response (FIR) filters which are suitable for wireless communications applications. In comparison of basic FRM with other existing multistage FRM structures, the proposed structure has a narrow transition bandwidth and higher stop band attenuation with significant reduction in terms of the number of computational steps. A design example is incorporated to demonstrate the efficiency of the proposed approach. Simulation results establish the improvement of the proposed scheme over other recently published design strategies.
文摘In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.
