Majlesi Journal of Telecommunication Devices
Volume:11 Issue: 1, Mar 2022

All human biological traits are unique as biometrics, such as fingerprint, palm, iris, palm veins, finger veins and other biometrics. Using these biometrics has always been challenging. One of the challenges in biometrics is physical injuries. Finger vein biometrics is one of the characteristics that is most resistant to physical injuries. Numerous algorithms for authentication have been proposed with the help of this biometrics, which have weaknesses such as high computational complexity and low identification accuracy. In this paper, a new method in identification based on maximum curvature algorithm and morphological operators is proposed. The maximum curvature algorithm extracts image properties using a set of operations based on image returns. This process has been enhanced in the proposed method with morphological operators. What distinguishes the proposed method from other methods is that this algorithm is very accurate in distinguishing images which are similar but belonging to different classes. The proposed method, in addition to having a reasonable computational complexity, has been able to record very good identification accuracy in the challenge of low image quality. The identification accuracy of the proposed method is 97.5%, which compared to other methods has been able to improve more than 3%. Also, the identification speed of the proposed method is 0.84 seconds, which is very fast in its kind.

One of the points that can be deduced from the study of common methods and techniques in electronic warfare is that none of these methods is considered absolute winners and the holder can’t consider himself the undisputed winner of this field because there is more or less the opposite of every technique. Using a method or tool in a situation can be very effective, but it becomes a weakness when a competitor changes the method or tool. Therefore, in this battle, the correct and timely use of any technique and with full knowledge of the opponent and in accordance with the actions performed by him can be considered a factor of success and use all useful tools in this field. One of the useful tools in the field of strategic decision analysis is the game theory. With the help of this mathematical tool, the decision-making process between two or more decision-makers can be examined and the results can be analyzed. It is also possible to observe the effect of various parameters involved in the result of the problem and plan the result in the desired direction. In this paper, the selection of the best response in different stages of the conflict to solve different problems has been achieved. The main purpose is to provide a new approach to adopt a strategy, select radar technique, destructive response in case of incomplete information from each. The second goal is to use mathematical and algorithmic methods for better performance in the functionality of the parties.

The Internet of Things (IoT) is an almost new technology in which different things are connected to the Internet and are available anytime and anywhere. The Internet of Things has many uses, including smart cities, smart industries, smart health, etc., and therefore, has attracted a lot of attention. One of the most interesting uses of IoT can be mentioned in the field of healthcare, which in addition to attracting the attention of patients and medical staff, has also interested industries and researchers. The use of the Internet of things in the healthcare’s field provides the possibility of remote monitoring of health parameters, early diagnosis, easy access to patient data, and improving the quality of medical services. This paper examines IoT-based healthcare (HIoT) systems. The HIoT network is described in terms of topology and architecture. Also, new technologies used or have the potential to be used in these systems have been examined. Then, IoT services and applications in healthcare are described. After reviewing IoT-based treatment systems from different aspects, the challenges in the development process of these systems are expressed as open issue and researched topics for further studies.

In this paper, the optimization of the electromagnetic railgun and its projectile path is proposed. The circuit model is used to optimize and simulate the elec- tromagnetic railgun, in which the equivalent circuit of the railgun is extracted. Then the differential equations expressing the physics governing the system are obtained. Using the projectile path equations and simulating them in MATLAB, the output of the simulation of the electromagnetic railgun and its projectile path in MATLAB software has been analyzed. The main advantage of the models used is that they can be used in matters of sensitivity and optimization due to their high speed. Based on the obtained outputs of electromagnetic railgun and projectile path, the cost function is presented, and then the effective parameters of models are optimized using the genetic algorithm. The results show that the losses and costs are drastically reduced for the same purposes, and the waste of costs and energy is prevented.

Diabetes is one of the most common metabolic diseases, and diagnosis of it is a classification problem. The most challenge is this area is missing value problem. Artificial Intelligence techniques have been successfully implemented over medical disease diagnoses. Classification systems aim clinicians to predict the risk factors that cause diabetes. To address this challenge, we introduce a novel model to investigate the role of pre-processing and data reduction for classification problems in the diagnosis of diabetes. The model has four stages consists of Pre-processing, Feature sub-selection, Classification, and Performance. In the classification technique, ensemble techniques such as bagging, boosting, stacking, and voting were used. We considered both states with/without for pre-processing stage to reveal the high performance of our model. Two experiments were conducted to reveal the performance of the model for the diagnosis of diabetics Mellitus. The results confirmed the superiority of the proposed method over the state-of-the-art systems, and the best accuracy and F1 achieved 97.12% and 97.40%, respectively.

This paper presents an optimum network structure based on a BBO tuned adaptive neuro-fuzzy inference system (ANFIS) to control an active suspension system (ASS). The unsupervised learning via Biogeography-Based Optimization (BBO) algorithm is used to train the ANFIS network. The optimal proportional-integral-derivative controller tuned based on the LQR method is used to generate the training data set. ANFIS base on Fuzzy c-means (FCM) clustering algorithm is applied to approximate the relationships between the vehicle body (sprung mass) vertical input velocity and the actuator output force. BBO algorithm is used to optimize fuzzy c means clustering parameters. The numerical simulation results showed that the proposed optimized BBO-FCMANFIS based vehicle suspension system has better performance as compared with the optimal LQR-PID controller under uncertainties in both of reducing actuator energy consumption and the suppression of the vibration of the sprung mass acceleration, with a 43% and 9.5% reduction, respectively.

Scalability, security, resiliency, and efficiency are Named data networking’s (NDN) main features which have attracted many researchers' attention to be considered for IoT. In this field of study, many research areas still need improvements like accessibility. In this paper, an NDN-based protocol is proposed for reducing data access time in the discovery phase in mobile crowdsensing by using a category table (CT). This protocol makes it possible to access semantic information through the category table. The efficiency of the proposed protocol is measured by simulation and demonstrated that in packet end-to-end delay is lower than the existing methods’ (NDN and Geo-NDN). Also, the proposed protocol was found to has the highest rate successful message delivery rate.