نشریه فناوری اطلاعات و ارتباطات انتظامی
پیاپی 11 (پاییز 1401)

NC-OFDM systems play a significant role in establishing cognitive radio communication in the operational scenarios within telecommunications science. These systems are classified under cognitive radio systems and have been developed with the aim of utilizing the unused parts of the communication channel in mission-oriented use cases. The issue of synchronization is one of the most difficult and important issues in receiver design due to the high sensitivity of NC-OFDM systems to time and frequency errors. In this paper, data-aided and non-data-aided synchronization methods are examined and the results are simulated. Accordingly, it is suggested that in operational environments with high signal-to-noise, non-data-aided synchronization methods can be used to prevent resource wastage. However, in noisy environments and in the presence of interference, the use of synchronization sequences is a better option.

In the near future, transportation will include connected vehicles in smart cities, and the security issue for these cities citizen who use these will definitely become doubly important. In addition to the common use of connected vehicles in smart transportation, it also has special applications also in other institutions, including the police. Today, police organizations connection to various equipment and advanced technologies played an important role in increasing the quality level in the police actions and the country security organizations, to make the smart police. Connected vehicles have special uses for proper monitoring and tracking of suspicious cases in police missions. There are different cyber attacks around vehicular networks, including spoofing attacks lead to the transmission and sharing of fake information and, if security principles are not followed, can lead to a complete disruption of the network and makes the service unavailable. In this paper, the recent researches related to vehicular network attacks were reviewed and while the attacks were categorized based on the different network layers, the vulnerability severity level of the attacks was obtained based on an international standard. Also, those types of vehicular network attacks that include the High Severity and disruption of the network services were determined and the solutions proposed in recent researches were categorized based on the used algorithms. In this research, a method for a general spoofing attack detection system is proposed, which tries to increase the accuracy of attack detection with a combination of features using machine learning and deep learning approaches.

The navigation system is one of the primary and most essential parts of a positioning system in autonomous vehicles. Inertial navigation systems (INS) based on inertial sensors are among the most common navigation systems, which have received much attention due to their low cost. However, what limits the use of this navigation system is the presence of unavoidable errors caused by inertial sensors, which grow over time. Therefore, to reduce and eliminate this error, this article proposes a suitable integrated INS/image navigation aid system for utilization in a vehicle.</strong>In this article, without the intervention of the global positioning system (GPS), the proposed system gets the map of the places our car has passed through.</strong> This map can be used to monitor the places the driver travels in order to make the traffic police systems intelligent. The vehicle is equipped with a camera and inertial sensors to take pictures of the environment. This study uses the data from the inertial sensors to increase the output data's accuracy. The photos from the camera are processed by a convolutional neural network (CNN) and give us a series of geometric features. These features are in the form of a tensor of numbers that, together with the IMU data, form the inputs of the </strong>long short-term memory</strong></em> (LSTM) netwo</strong>rk. Finally, this network gives us the vehicle's location at any moment while outperforming ORB-SLAM, CL-VO, DeepVO, and VISO2_M and is 36% more accurate than DeepVO.</strong>

Today, technology plays an important role in the process of transformation in organizations, technology grows in the context of science and seeks to solve problems in a practical way, therefore, in order to reach desired goals and solve problems through the use of technologies, identifying the components which is the driving force to reach the desired situation in law enforcement crime prevention, constitutes the purpose of this research. The current research is applied in terms of purpose, in terms of descriptive-analytical nature and in terms of qualitative method. The statistical population includes police elites, 14 of whom were selected as a sample using the purposeful sampling method and theoretical saturation principle. The data collection tool was a semi-structured interview and was used to ensure the validity of the professors' opinions and for the reliability of the open test, comparison and repeatability method.The collected data were analyzed by thematic analysis method and using "Maxqda" software. Based on the findings, while identifying the dimensions of the research including; 1-Strengthening and development of preventive technology capabilities and 2- Development and improvement of processes and work methods in law enforcement crime prevention, 10 and 7 components were identified for the first and second dimensions, respectively.

A harmonic control circuit (HCC) is one of the most important block in the Class-F power amplifiers, which should pass the signal at operating frequency and even harmonics and suppress the signals at odd harmonics. Long open stubs are usually used to suppress odd harmonics in the conventional Class-F power amplifiers, which resulted in the large size of the power amplifiers. In the proposed design a compact harmonic control circuit using coupled lines and bended lines are designed at 1GHz. The proposed HCC passes signals at 1 GHz with less than 0.1 dB insertion loss (IL) and also, passes signals at 2 GHz (second harmonic) with less than 0.1 dB IL, and also suppresses signals at 3 GHz (third harmonic) with more than 35 dB attenuation level, and also passes signals at 4 GHz (fourth harmonic) with less than 0.1 dB IL. With these good preferences the proposed HCC can be easily used in Class-F power amplifiers.

Today, antenna with high bandwidth is very important in commercial and military sectors. Helical antennas are of independent frequency type that can solve this demand. In this article, we have reached this demand by using a two-span Archimedean spiral antenna with the width of each arm equal to 0.24 mm. This structure is simulated and built using HFSS software. One of the advantages of this antenna is the 12 GHz bandwidth, which includes the frequency band from4.5 to 16.5 GHz. By using the metal cover (cavity) the left-handed circular field produced by the helical antenna has been removed. Placing the absorber behind the antenna removes the electric and magnetic fields that disrupt the performance of the microstrip balloon. From the microstrip balloon structure, we were able to increase the impedance of the 188-ohm spiral antenna to 50 ohms. In the end, the manufactured antenna is tested and measured, and the measurement results are in good agreement with the simulation.

The purpose of this study was to predict the tendency to use information technology based on media literacy and social intelligence in the era of police intelligence in 1400. The present study was a descriptive correlational and regression study. The statistical population of the study consisted of all female police officers in 1400. The sample size was 200 female police officers who were selected by non-random and available sampling method. Data were collected by the Kansidine's media literacy Questionnaire (2008), Silvera et al's social intelligence Questionnaire (2001) and Trainor et al's orientation to technology Questionnaire. (2011). Pearson correlation coefficient and multiple regression analysis using Spss v.22 software were used to analyze the data. Findings showed that social intelligence explained 21% and media literacy 15% of the variance of information technology orientation.Pearson correlation coefficient and multiple regression analysis using Spss v.22 software were used to analyze the data. Findings showed that social intelligence explained 21% and media literacy 15% of the variance of information technology orientation.

Automatic modulation classification is an intermediate step between signal detection and demodulation, and is also considered as one of the main parts of modern telecommunication receivers. It can play a vital role in many civil and military applications. In most previous studies, it has been assumed that the carrier frequency is known. It may be far from the realistic condition. In this study, it is assumed that there is no information in the receiver about the central frequencies of the received signals and the purpose is to estimate the carrier frequency and classify the type of digital modulation. For this purpose, a two-step algorithm is proposed. In the first step, the carrier frequency is initially estimated based on zero-crossing method. Then, in the second step, a gradient descent algorithm is used to maximize the mean of I/Q demodulator output with the initial point achieved by zero-crossing method. For the classification process, efficient statistical features are first extracted based on moments and cumulants for all modulated signals. After that, the classical linear discriminant analysis (LDA) and principle component analysis (PCA) methods are used to reduce the features dimension and then the modulated signals are classified. The simulation results show that the proposed algorithm estimates the carrier frequency of the signals with much less error than the ZC benchmark algorithm. In addition, it performs well in classifying modulated signals.

The mobility of the nodes and their limited energy supply in cognitive Internet of Things networks leads to complexity of network conditions. So that having efficient energy mechanism is considered important in these networks and can reduce energy consumption in the network. network elements (including nodes, policies and behaviors) are not able to create intelligent adaptation to the environment in which they operate due to being limited in their status, scope and response mechanisms and compatibility in these networks is usually passive. for this reason energy efficiency algorithms are expected to be able to adapt themselves to environmental changes through a preventive solution. On the other hand The Administration Command of the Islamic Republic of Iran(Farja) is in contact with a large amount of information in various social, security, economic and cultural fields in order to work in the field of order and security. therefore to achieve aforementioned purposes. this paper presents a new RMLAB solution for energy efficiency based on Learning Automata from the perspective of cognitive networks. which is self organizing, self aware and dynamically regulated by use of learning automata to send network nodes to manage energy consumption and one of the main strengths of this method compared to existing methods is that network able to organize, manage and rebuild itself. the experimental results show that examining energy efficiency from perspective of cognitive network leads to improvement of quality of service parameters, including throughput and end to end delay in network compared with other energy efficiency methods.

Malicious links are sent by intruders in various ways such as email, SMS, or social networks for the purpose of fraud or abuse. Various methods have been presented so far to identify malicious links, all of which have errors. On the other hand, by identifying how these methods work, hackers have changed their techniques and are able to present their malicious link as a useful link to the victim. The purpose of this article is to present a new method to identify and deal with malicious links. For this purpose, after reviewing the previously presented researches in the field of identifying malicious links and security risk criteria, the concept of security risk for links has been redefined and a new criterion has been presented to calculate it. This measure uses the nearest neighbor method to estimate the security risk. Based on these criteria, a security score can be calculated for each provided link so that the user can decide whether or not to use the link, which will reduce internet fraud. Experiments performed on two real datasets related to previously known malicious and useful links show the effectiveness of the proposed criterion in terms of detection rate. That is, this criterion is able to identify a high percentage of malicious links in the datasets.