detection

Due to recent advancements in artificial intelligence, the utilization of image and video data along with image processing technologies has been significantly used in automating surgical procedures and their training. Today, one or more cameras or imaging devices are commonly used in most operating rooms to capture crucial medical information for further analysis. These visual data can be leveraged to design and develop automated image-guided systems aimed at assisting specialized surgeons during procedures and facilitating their training. Furthermore, these systems can serve as the cognitive knowledge for assisting surgical robots. An image-guided surgical system comprises of various components, among which the identification, segmentation, and tracking of surgical instruments and critical anatomical regions, phase recognition, motion analysis, and gesture recognition, and more importantly as surgical skill assessment, are the most crucial components. Automating these components by the use of image processing and machine vision will certainly contribute to the system's deep and independent understanding of the surgical scene. This article introduces the important datasets relevant to image processing and machine vision in surgical applications. Subsequently, several research endeavors in the realm of image processing, machine vision, on the above-mentioned applications are presented, and some prospect research areas are introduced.

In this manuscript, we proposed a new scheme in communication signal detection which is respect to the curve shape of received signal and based on the extraction of curve fitting (CF) features. This feature extraction technique is proposed for signal data classification in receiver. The proposed scheme is based on curve fitting and approximation of rational fraction coefficients. For each symbol of received signal, a specific rational function approximation is developed to fit with received signal curve and the coefficients of the numerator and denominator polynomials of this function are considered as new extracted features. Then  it will be shown that the coefficients of this polynomials have the potential for using as new features in a statistical classifier and have better performance in competition with other solutions such as linear and even nonlinear feature extraction methods in  classification. The criteria used in performance evaluation are  probability of error and signal to noise ratio in FSK and ASK modulations. For each symbol of received signal, a specific rational function approximation is developed to fit with received signal curve and the coefficients of the numerator and denominator polynomials of this function are considered as new extracted features. In the proposed method, there are two phases train and test, which are described in the following two steps. First, in the train phase, the algorithm starts by using binary FSK and ASK modulations, so first, a system generate a number of random symbols then signal is modulated by binary ASK and FSK. The Modulated FSK and ASK signals are corrupted in the channel with noise. The noise-corrupted signal enters the receiver at the corresponding transmitted interval. Then, the samples are extracted from the modulated signals based on predetermined sample rates. Then, we fit a rational fraction curve with degrees of L and M to each set of N samples. Afterward, we apply all the numerator (L+1) and denominator (M) coefficients to 0 and 1 classes  in the binary FSK and ASK modulations. We store all the specific coefficients of the deterministic symbols at different M and L values to create the corresponding histogram in each class. In each histogram (i.e., the coefficients of a class), we extract and store specific coefficients that completely discriminate between the two classes. Therefore, we determine all the histograms where there is a good approximation of discrimination and create the related table. Note that the data used in histograms are the training data. Then, in order to analyze and evaluate the performance of the proposed curve fitting method, we send the testing data through the channel corresponding to the related modulator. The results of the proposed classification method show that it provides smaller error rate regarding to the theoretical error rate probability in AWGN channel.

Today , economic centers such as refineries, political centers such as parliament, government, judiciary and military centers such as barracks can be attacked by spy or suicide Mini-drones. Various systems and methods have been proposed to detect and identify this threat. Since this issue is a competition between two groups of defenders and attackers, several factors are effective in the decision-making of these two groups, their operational approach and the optimal selection of equipment. In this research, by using the game theory based on the graph model, we have been able to, in the first step, by considering the two parameters of cost and budget in equipping the electronic warfare , as well as the operational range of the systems in two independent scenarios, considering the equipment of the parties and the preferences of the actors. Predict the battle scene and the equilibrium situations based on game theory.In the second step, by combining these two components and simultaneously applying the influence of the decision parameters in a cost function, we predict the final state of detection and identification of the Mini drone and, taking into account the real conditions, we model the correct decision-making process for the commanders of the battlefield of electronic warfare. The results show that the simultaneous effect of two parameters, cost and operating range, have completely opposite results with one-dimensional decisions. Also, the cost function can be effective in changing the results

Different materials have different absorption spectra of the terahertz (THz) radiation. So, by detecting the absorption spectra of THz radiation of different materials we can assess material substances. We propose and analyze an efficient way to detect the THz radiation in a magnetized graphene system via electromagnetically induced transparency (EIT). In this paper an all-optical method for THz signal detection in graphene nanostructure in core of a detector is investigated. Such a scheme for THz signal detection mainly relies on the measurement of probe transmission spectra, in which the behaviors of a weak-probe transmission spectrum can be controlled by switching on/off the THz signal radiation. Taking into account the tunable optical transition frequency between the Landau levels in graphene, our analytical results demonstrate that a broad frequency bandwidth of the THz signal radiation can be inspected and modulated by means of an external magnetic field. This feature being useful for detection of explosives with width absorption spectra of the optical terahertz signal. Common explosives such as RDX, TEND and Ammonium Nitrate in the spectral range of THz signal are widely used in a variety of explosives. Effects of parameters such as intensity of the terahertz radiation and relative phase between the fields on the absorption anddispersion of probe field hase been studied. Also, Detector sensitivity and rapid response to fluctuations of terahertz radiation by measuring the switching time of group velocity of probe field from subluminal to superluminal and vice versa hase been studied. The estimated range of the switching time is between 5 ps to 8 ps for subluminal to superluminal light propagation.Such all optical detectors can be well used in a wide range of THz spectroscopy for (explosive or forbidden) material detection.