نشریه فناوری اطلاعات و ارتباطات انتظامی
پیاپی 15 (پاییز 1402)

Given the significant increase in the smuggling of goods and currency at the country's borders and the important role of the police in combating this phenomenon, the use of information technology tools can be effective in smartening up the police and enhancing its efficiency in combating smuggling. The purpose of this article is to examine the impact of information technology tools on police intelligence in the fight against smuggling from border customs.

The research method is descriptive-survey. The data collection tool is a library and researcher-made questionnaire. The statistical population of the study consisted of 69 elites and experts in the Border Customs Police Headquarters, who were selected as a whole. Statistical software SPSS was used for data analysis.

The research findings showed that the use of equipment such as "e-commerce", "QR system", "management information system", "radio frequency identification technology", "control devices", and "global ASYCUDA in border customs" have an impact on combating smuggling from border customs. Also, measures such as "use of radio frequency identification technology", "use of control devices", "use of QR system", "use of management information system", "use of global ASYCUDA in border customs", and "transition from traditional trade to e-commerce" are effective in preventing smuggling, respectively.

The research results show that one of the important measures in combating the smuggling of goods through official channels is the use of modern technologies and the transition from traditional to technology-based modern approaches,

With the expansion of the Internet and the production of cheap digital cameras and mobile phone cameras, a large amount of different images have been made available to users, which has led to the creation of extensive databases. Image fusion is used to combine two or more images and achieve a desired image, which contains more information and better accuracy than the input images. In this paper, a hybrid approach based on variable mode analysis and gray wolf optimization algorithm is proposed to improve the quality of images. Examining the results shows that the proposed method always provides higher quality and richer images in different image patterns than basic machine learning models, supervised deep learning models, and unsupervised deep learning models; In such a way that the average entropy obtained for the fused images is equal to 7.57 and the average image quality is 0.9958, while the guided filter approach has the worst performance in both indicators. Also, after the proposed method, the deep learning unsupervised approach has performed best, which is also considered as the base plan; The proposed method has improved the entropy index by 0.6% and the image quality index by 0.04% compared to the basic plan.

Today, positioning has attracted special attention as one of the fundamental aspects of various systems. Over time, the navigation system's error, especially in the Inertial Navigation System (INS), has significantly increased. This error increase can lead to serious issues in navigation processes, especially on long routes. To ensure the high accuracy and stability of navigation on long routes, the use of advanced and effective auxiliary systems is necessary. The goal of this article is to present an innovative method for positioning by combining camera and inertial sensor data to enhance accuracy in this vital process. This method is particularly important in self-driving vehicles, domestic robots, and search and rescue robots operating in enclosed environments or areas without active GPS coverage. Consequently, there is a crucial need for a real-time positioning system for these systems. In this article, we have implemented a real-time Visual-Inertial Odometry (VIO) navigation system on a PC platform, which performs significantly better than the INS system alone. In this system, cameras are used to correct the accumulated INS error, and the fusion of cameras and inertial data is done using a graph. To validate and evaluate the system, practical data collected by the system are used, demonstrating that the proposed VIO system exhibits more than a 90% improvement compared to INS.

Understanding cyberspace in all dimensions of political, economic, regulatory, security, etc. From a policy perspective, the appropriate approach, management, health and regulation of this phenomenon is of special importance It was ordered until the countries talked about transitions on this ground , made the path for resistance even easier than that. The cyberspace regulatory system of the Islamic Republic of Iran is one of four systems (regulatory, security, legal and judicial) that the Supreme Commander of the Armed Forces (Supreme Commander) is in charge of He studied in 2015.This question shows that there is a lot of interest among researchers working in this field, but there is no direct search in the subject literature for the issue and conceptualization of cyberspace governance systems This issue has led to activities to improve the space regulatory system Virtually performed technical results. Therefore, the present research is an exploratory-descriptive study that is based on content analysis and aims to defend the viewpoint of the Supreme Commander of All Powers in terms of the concept of order From the issue of the disciplinary system and the answer to this question is the basis What is the cyberspace regulatory system of the Islamic Republic of Iran?.

Slotted waveguide antennas (SWAs) are utilized in various applications such as space systems, radars, and unmanned aerial vehicles (UAVs). Slotted waveguide arrays with slots in narrow walls have attracted significant attention due to their capability to create horizontal polarization. However, since the size of the narrow waveguide walls is often smaller than the resonant length of the slot, the slots created inevitably bend on the broad waveguide walls. This bending on the broad walls not only reduces the mechanical strength of the waveguide but also complicates the precise modeling of slot excitation and increases the complexity in fabrication of planar arrays, necessitating the use of metallic separators. In this paper, we present a novel design that prevents the bending of slot edges on the wider waveguide walls without compromising the antenna performance. In this design, by placing a dielectric layer on the slots of the narrow walls, the resonant length of the slot is reduced by at least 55%, ensuring that the slot is fully located on the narrow wall without the need for bending the slot on the broad walls. To demonstrate the effectiveness of this design, an array consisting of 12 slots with a Taylor synthesis-based amplitude distribution was designed and fabricated, and measurements showed a relative impedance bandwidth of 3.3% and side lobes below -30 dB.

By using machine learning methods to discover hidden patterns in social networks through the analysis of large datasets of social interactions, researchers gain new insights into social dynamics and behavior. These techniques can reveal previously unknown communities, influential people, and information dissemination patterns. This emerging field of network science has the potential to shed light on a variety of social phenomena, including disease outbreaks, rumors, political movements, and scientific collaborations. This paper presents a new approach for deception detection in social networks using reconstructed data clustering. The proposed method includes the reconstruction of missing values in social network data using a technique called EinImpute, which reconstructs the data set based on a graph-based method, and then attempts to identify deceptive behavior patterns with using K-means++ clustering algorithm. The proposed method is implemented using the R language and the experimental results on the real-world dataset of Movielens and based on the famous ARI, NMI and Purity criteria show the improvement of the clustering results of the proposed method compared to other methods. Also, the stability test shows that the proposed method has a high stability rate, which increases the hope of using it in different conditions. In general, the results show the effectiveness of the proposed method in detecting deception, which can help researchers in this field and provide practical implications for social media platforms and law enforcement agencies in identifying possible frauds and disseminators of false information.

The purpose of this study was to investigate the relationship between enterprice architecture and organizational agility with the moderating role of technology acceptance. This research is applied in terms of purpose and descriptive-survey in terms of nature and method. The statistical population of the study includes the Islamic Revolution Housing Foundation of Khorasan Razavi. According to Cochran's formula, 131 people were selected as the sample. The sampling method in this research is simple random. In this research, standard questionnaires of organizational architecture, organizational agility and technology acceptance have been used. The reliability of the questionnaires was confirmed by calculating Cronbach's alpha. The validity of the questionnaires has been confirmed by consulting experts. Data analysis was performed using SPSS and Smart-PLS software. Findings show that there is a direct and significant relationship between enterprice architecture and organizational agility . Technology acceptance moderates the relationship between enterprice architecture and organizational agility .

Nowadays, organizational, commercial, and industrial systems have become more complex, and their connection to other networks such as the Internet is inevitable. Preserving the privacy of users and their communications is very important. Intrusion into these systems and data theft pose a constant threat to them. Studies have been conducted to detect intrusions in systems using statistical methods, machine learning, and neural networks. Given the leading performance of deep learning networks in various fields, their performance in the field of intrusion detection has also been highlighted. Based on this, the aim of this research is to present a method for detecting intrusions in systems connected to the Internet. This method uses federated learning and convolutional neural networks to cluster the data of each network node while preserving their privacy at the network level. This method can identify infected nodes and attacker users. Simulation of this method shows that its performance has improved compared to similar methods, achieving 93% accuracy in identifying infected nodes and showing less sensitivity to noise. Additionally, it has greater scalability and less communication overhead. This method can be used in various security systems, such as network intrusion detection systems, device intrusion detection systems, and operating system intrusion detection systems.

Wireless systems have important applications in various scientific fields, including medical applications and military fields. In this paper, a ring voltage control oscillator is implemented by combining direct bulk biasing techniques and oscillating frequency current control and tunability in a wide frequency range for behind-the-wall live target detection applications. In this paper, three new structures are proposed to improve phase noise and frequency limitation. The simulations related to the first proposed circuit in 0.18µm CMOS technology using bulk voltage bias technique show that the amount of phase noise at 1 MHz frequency is equal to -138dBc/Hz and its power consumption is -45dB with a frequency range of 2 to 12 gigahertz, which represents the efficiency of the provided circuit. In the second proposed structure, by using the inductor in these control transistors, the frequency range has been improved. Circuit simulations of the second structure show that the phase noise for this circuit is equal to -123.26dBc/Hz and the frequency range is between 6 and 16 GHz with a power consumption of -90dB. By combining the first and second circuits, another circuit was proposed, which has a wider frequency range of 7 to 22 GHz. The simulation results show that the three proposed circuits have a much better performance than the previous ones. In this article, all the results were analyzed and simulated with Advance Design System (ADS) software.

In this article, the modeling of Automatic Link Establishment device or ALE using neural networks or artificial intelligence is introduced. In general, the performance of an ALE device can be modeled. The purpose of modeling is to design this device using new methods and implement it using existing tools and thus localize it. In this regard, in this article, using neural networks and MLP algorithm, it has been tried to model and implement the overall performance of the ALE device. At first, after examining telecommunication channels and nonlinear effects and noise on data transmission, a model for data transmission in telecommunication channels has been introduced and coded in the MATLAB software environment. Then the types of neural networks and their applications have been introduced and the best algorithm has been selected for modeling the ALE device. In the following, several models have been made and compared using MATLAB software tools and MLP algorithm coding. Finally, the proposed model, which is based on the MLP algorithm, can predict the right channel for each new output with the least error. After optimization, the proposed models can be implemented on FPGA and provide a way to build this device in the country.