This paper studies the problem of the convolutional code parameters estimation in noisy scenario. Among the methods that have been proposed for this problem, the rank-based method has attracted most of the research. In this method, the receiver cuts the received sequence up into vectors of lengthl to form the rows of matrix C (l), for max l =1, ¼, lmax. The code parameters are estimated based on the rank of these matrices. To this end, the relation between the code parameters and the rank of C (l) should be known. To do this, the previous works proposed an experimental relation; however, it is not established in the general case. This paper analytically computes the rank relation and proposes a method to extract the rate k/n convolutional code parameters. The method uses the Gaussian elimination with row pivoting (GERP) algorithm to estimate the rank and null space of C (l). The proposed algorithm is based on a threshold value. Hence, an appropriate threshold will be proposed based on the Minimax decision rule.

Background: The most common types of non-melanoma skin cancer are basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). AKIEC-Actinic keratoses (Solar keratoses) and intraepithelial carcinoma (Bowen’ s disease)-are common noninvasive precursors of SCC, which may progress to invasive SCC, if left untreated. Due to the importance of early detection in cancer treatment, this study aimed to propose a computer-based model for identification non-melanoma malignancies. Methods: In this analytic study, 327 AKIEC, 513 BCC, and 840 benign keratosis images from human against machine with 10000 training dermoscopy images (HAM10000) were extracted. From each of these three types, 90% of the images were designated as the training set and the remaining images were considered as the test set. A deep learning convolutional neural network (CNN) was developed for skin cancer detection by using AlexNet (Krizhevsky, et al., 2012) as a pretrained network. First, the model was trained on the training images to discriminate between benign and malignant lesions. In comparison with conventional methods, the main advantage of the proposed approach is that it does not need cumbersome and time-consuming procedures of lesion segmentation and feature extraction. This is because CNNs have the capability of learning useful features from the raw images. Once the system was trained, it was validated with test data to assess the performance. Study was carried out at Shahid Beheshti University of Medical Sciences, Tehran, Iran, in January and February, 2020. Results: The proposed deep learning network achieved an AUC (area under the ROC curve) of 0. 97. Using a confidence score threshold of 0. 5, a classification accuracy of 90% was attained in the classification of images into malignant and benign lesions. Moreover, a sensitivity of 94% and specificity of 86% were obtained. It should be noted that the user can change the threshold to adjust the model performance based on preference. For example, reducing the threshold increase sensitivity while decreasing specificity. Conclusion: The results highlight the efficacy of deep learning models in detecting non-melanoma skin cancer. This approach can be employed in computer-aided detection systems to assist dermatologists in identification of malignant lesions.

This paper concerns to blind identification of a convolutional code with desired rate in a noiseless transmission scenario. To the best of our knowledge, blind estimation of convolutional code based on only the received bitstream doesn’ t lead to a unique solution. Hence, without loss of generality, we will assume that the transmitter employs a non-catastrophic encoder. Moreover, we consider a complete synchronous scenario in which one can extract separate codewords from received sequence. This assumption is valid in many practical communication systems because, the frame preambles allow us to identify the beginning of each codewords. In this paper, we examine the blind identification problem for rate1/n and rate k/n convolutional codes, respectively. For rate 1/n, we propose an iterative method that uses three steps in each iteration to test the validity of a possible value of n. We show that this method can identify the parameters of a rate 1/n convolutional code from only two different noiseless received codewords. Afterwards, we generalize this method for a rate k/n convolutional code in which each iteration is composed of seven successive steps. We show that this method requires at least k+1 different codewords to identify all parameters of a rate k/n code.

We present a framework for algorithm-based fault tolerance methods in the design of fault tolerant computing systems. The ABFT error detection technique relies on the comparison of parity values computed in two ways. The parallel processing of input parity values produce output parity values comparable with parity values regenerated from the original processed outputs. Number data processing errors are detected by comparing parity values associated with a convolution code. This article proposes a new computing paradigm to provide fault tolerance for numerical algorithms. The data processing system is protected through parity values defined by a high-rate real convolution code. Parity comparisons provide error detection, while output data correction is affected by a decoding method that includes both round-off error and computer-induced errors. To use ABFT methods efficiently, a systematic form is desirable. A class of burst-correcting convolution codes will be investigated. The purpose is to describe new protection techniques that are easily combined with data processing methods, leading to more effective fault tolerance.

Terrorism is a phenomenon which considerable attention has been focused on in recent years. In this respect, the criminal code is one of mechanisms has been regarded to combat terrorism. Although the penal code is a tool to response the crimes of punishments of offenders, political entity of terrorism can separate the criminal code from its basic principles in order to combat terrorism procedures and establishing Tool-using for politicians. Notwithstanding this danger (risk), the criminal code has many privileges to the non-legal alternatives which based on the criminal code; it can be preferred to other methods like war. Still, the suggested method is creating a comprehensive method to combat terrorism entities which it is to employ every of these methods besides the others.