An Intelligent Technique for Grape Fanleaf Virus Detection

Grapevine Fanleaf Virus (GFLV) is one of the most important viral diseases of grapes, which can damage up to 85% of the crop, if not treated at the right time. The aim of this study is to identify infected leaves with GFLV using artificial intelligent methods using an accessible database. To do this, some pictures are taken from infected and healthy leaves of grapes and labeled by technical specialists using conventional laboratory methods. In order to provide an intelligent method for distinguishing infected leaves from healthy ones, the area of unhealthy parts of each leaf is highlighted using Fuzzy C-mean Algorithm (FCM), and then the percentages of the first two segments area are fed to a Support Vector Machines (SVM). To increase the diagnostic reliability of the system, K-fold cross validation method with k = 3 and k =5 is applied. After applying the proposed method over all images using K-fold validation technique, average confusion matrix is extracted to show the True Positive, True Negative, False Positive and False Negative percentages of classification. The results show that specificity, as the ability of the algorithm to really detect healthy images, is 100%, and sensitivity, as the ability of the algorithm to correctly detect infected images is around 97.3%. The average accuracy of the system is around 98.6%. The results imply the ability of the proposed method compared to previous methods

A ground based circular synthetic aperture radar

Detecting on-the-ground objects is a subject of interest for some applications. Typical example is foreign object detection on the airport runway. In response to this demand, a ground-based Circular Synthetic Aperture Radar (CSAR) system is proposed and explained in the paper. In the proposed CSAR, the antennas represent a circular movement trajectory. Wideband Linear Frequency (LFM) chirps were used for transmission. A simulation model for CSAR, based on the Doppler Effect between the radar and object is developed in this paper. In addition, a processing method for object detection using correlation between image data produced by simulation and experimental data is developed. The resultant of the simulated model at each point, which represents the object's behavior in an ideal and clutter-free environment, is used as a template for object detection. Simulation and experimental results demonstrate that the proposed method is well suited in detecting small objects at different positions

A Linear Frequency Modulated bistatic radar for on-the-ground object detection

A radar system for detecting and localizing small targets on the ground is proposed in this paper. The system transmits wideband Linear Frequency Modulated pulses from ground-based transmitter. The reflected pulses will be collected simultaneously by two different ground-based receivers installed in different bistatic positions. Accurate range processing in this bistatic configuration will lead us to detect small objects like N-type connectors in several meters distances

A multistatic circular synthethic aperture radar for small object detection

This paper introduces a ground-based Multistatic Circular Synthetic Aperture Radar (MuCSAR) used to detect small objects on the ground. The received signals have been modelled and the system prototype has been developed. The proposed signal processing is also described. An experimental investigation for Foreign Object Detection application has been analysed

Foreign object detection based on circular bistatic synthetic aperture radar

Synthetic Aperture Radar is well known for producing a radar image of the ground, so it can be used for detecting on-the-ground object which is interesting for some applications. A possible application can be Foreign Object Detection (FOD), which is an important issue in aviation safety. A ground-based Circular Bistatic Synthetic Aperture Radar (Circular-BiSAR) is introduced in this paper. The circular movement makes it more practical while the bistatic configuration offers some advantages. Wideband Linear Frequency Modulated (LFM) chirp pulses are employed here, for transmission and reception of reflection pulses to and from the under test object. A simulated model is developed for the system which analyzes the transmitting, receiving, Doppler and LFM signals by considering the distances and movement of antennas. A prototype system is launched, and some experiments are done to detect and localize various objects based on their reflection properties of microwaves. A processing algorithm is proposed in this paper to confirm the detection. The results show that the proposed system can detect and localize on-the ground objects with as small a dimension as 2 cm height and 2 cm diameter located several metres away

A bistatic linear frequency modulated radar for on-the-ground object detection

The use of radar systems for detecting on-the-ground objects is a subject of interest for some applications. Among them, foreign object detection systems are important issues in airport aviation safety. Due to the characteristics of the object, a ground-based bistatic radar configuration is introduced in this article. The transmitter sends broadband linear frequency modulated chirp pulses. The reflected pulses are collected simultaneously by at least two ground-based receivers installed in different positions. Accurate range processing is conducted to detect small objects, such as N-type connectors in distances of several meters. A prototype system consisting of one transmitter and two receivers is developed. The system is then launched over land similar in appearance to a runway, and its ability to make an accurate image of the area where the object is placed in different positions is confirmed. Modifications that need to cover a bigger area are also discussed. The system resolution is analyzed and shows that in the case of several existing transmitter-receiver pairs, the best resolution can be achieved by the closer pairs

A Hierarchical Classification Method for Breast Tumor Detection

Introduction Breast cancer is the second cause of mortality among women. Early detection of it can enhance the chance of survival. Screening systems such as mammography cannot perfectly differentiate between patients and healthy individuals. Computer-aided diagnosis can help physicians make a more accurate diagnosis. Materials and Methods Regarding the importance of separating normal and abnormal cases in screening systems, a hierarchical classification system is defined in this paper. The proposed system is including two Adaptive Boosting (AdaBoost) classifiers, the first classifier separates the candidate images into two groups of normal and abnormal. The second classifier is applied on the abnormal group of the previous stage and divides them into benign and malignant categories. The proposed algorithm is evaluated by applying it on publicly available  Mammographic Image Analysis Society (MIAS) dataset. 288 images of the database are used, including 208  normal and 80 abnormal images. 47 images of the abnormal images showed benign lesion and 33 of them had malignant lesion.  Results Applying the proposed algorithm on MIAS database indicates its advantage compared to previous methods. A major improvement occurred in the first classification stage. Specificity, sensitivity, and accuracy of the first classifier are obtained as 100%, 95.83%, and 97.91%, respectively. These values are calculated as 75% in the second stage   Conclusion A hierarchical classification method for breast cancer detection is developed in this paper. Regarding the importance of separating normal and abnormal cases in screening systems, the first classifier is devoted to separate normal and tumorous cases. Experimental results on available database shown that the performance of this step is adequately high (100% specificity). The second layer is designed to detect tumor type.  The accuracy in the second layer is obtained 75%