نشریه فناوری اطلاعات و ارتباطات انتظامی
پیاپی 7 (پاییز 1400)

In this paper, an image Steganography algorithm based on text message concealment compression is presented. In this proposed method, at the same time, the level of security and capacity in Steganography color images has been increased. The structure of the proposed method consists of two parts: transmitter (lathe) and receiver (lathe). In Steganography, the number of bits of the message is first reduced and compressed as much as possible by converting the text message into binary information and using appropriate coding to increase the capacity of the information to be embedded in the host image. In order to improve the capacity to insert a hidden message, the host signal uses a binary tree search algorithm to compress and expand the text message before inserting and extracting it into the image, and at the same time generates cryptographic keys. In order to evaluate the performance of the proposed design, criteria such as Peak signal-to-noise ratio (PSNR), hiding ratio (HR)bit error rate (BER), similarity level of hidden and extracted messages (SIM) as well as algorithm execution speed, Used. By comparing the performance of the proposed algorithm with other proposed algorithms, it shows the optimal performance of the proposed design.

Undoubtedly, the issue of increasing security in public places has always been the focus of researchers and relevant officials. Significant measures have been taken to increase security in places such as the metro, the airport and places of pilgrimage, including inspection systems, the installation of surveillance cameras and the deployment of an officer. But all of the above is done by human resources, which factors such as fatigue, distraction and many other factors affect the quality of monitoring. One of the security measures is carrying a weapon and pulling a gun. The solution presented in this article is to use deep learning algorithms in the form of weapons detection in surveillance cameras. In addition to monitoring public places, the proposed system can provide as much security as possible. Banks, goldsmiths, chain stores, and the like help As armed robbers prevent employees from informing the police, the automatic weapons detection system quickly contacts security officers if it detects a weapon. And provides information. In this study, two large and small versions of the YOLO_v5 deep neural network were used to detect the lumbar weapon. And the performance of these networks was compared with each other. Finally, the proposed system of automatic detection of pistols after training with the Pishtaw data set performs the detection operation of the pistol with 99.01% accuracy.

The massive volume of images produced in recent years has made image retrieval one of the topics of research in the field of machine vision and image processing. The main challenge of content-based image retrieval systems is to extract the appropriate feature vector for image description to enable image retrieval effectively. In this research, a content-based image retrieval framework is introduced. The introduced feature vector is a combination of low-level features and mid-level features of the image. Extraction of low-level features of the image, including color, shape and texture, was performed using multi-level autocorrelation, discrete wavelet transform and fractal dimension analysis. Mid-level features are also extracted using the deep Boltzmann machine and by learning the low-level features of the image. The resulting feature vector is adjusted with 1K Corel database images and the performance of the proposed framework is also measured on 5K and 10K Corel databases. The best evaluation results are reported on 99.5%, 99.2% and 99.6% of the mentioned databases, respectively.

With the growth of information technology knowledge and the expansion of its applications, the development of new security models and the analysis and design of appropriate methods for detecting intrusion into networks and systems has become particularly important. In this research, a model for intrusion detection called ID2F based on feature selection using dragonfly algorithm and random forest classification has been proposed and proposed. The proposed method is a multi-class method, in other words, in addition to detecting intrusion, it also determines the type of attack. In this study, two completely different datasets, CICIDS-2017 and KDD-CUP99, were used for analysis to evaluate the performance of the method with a separate dataset. The problem is implemented with different algorithms and the best algorithm is selected as the proposed method. The accuracy value in the proposed method in the CICIDS2017 dataset is 99.83 and for the KDD-CUP99 dataset is 99.85. In addition, the results of the research have been compared with several other methods proposed by previous researchers, and this comparison shows that the proposed method is more accurate than most machine learning methods and its implementation time is better.

Today, due to the tendency to integrate different parts of a real-time system with different criticality levels, the concept of mixed-criticality systems has been considered. Applications of these systems include UAVs and smart police devices, in which different tasks of different importance are integrated together on a common platform. In order to provide the processing power required by mixed-criticality systems, multi-core architectures are a good option. One of the main challenges in multi-core architectures is task scheduling. Many researches in the field of task scheduling in mixed-criticality systems with multi-core architecture are assuming tasks are independent, but in reality, we are faced with many tasks that have a data dependency between them. Another aspect of task scheduling in lesser-known multi-core mixed-criticality systems is the reduction and control of the number of preemptions, which reduces time overhead during task execution. In this research, we schedule dependent tasks in a multi-core architecture in such a way that, firstly, the proposed schedule is correct and satisfies the limitations of the system, and secondly, it reduces the number of preemptions when assigning tasks to cores. Finally, the proposed scheduling algorithm is tested on a sample UAV and random tasks and then the scheduling rate and number of preemptions are compared. The results show that the proposed algorithm reduces the number of preemptions by about 96% without significantly changing the scalability rate.

Technology commercialization is a complex process that starts with scientific and technological ideas and ends with a new technology / product or service that can be offered in the market. The study of participation and synergy between technology stakeholders in the commercialization process has been neglected in the academic literature. With this in mind, the present paper provides a framework for the components of the commercialization of complex participatory technologies. First, through a systematic review, related articles (41 articles) are selected and the concepts of commercialization and technology participation are extracted and categorized. Then the main drivers of stakeholder motivation for concerted action, dynamic capabilities, are identified as the requirements for achieving the best performance and implementation mechanisms for technology development and accelerating the process of technology dissemination. In the following, by analyzing inductive content, a new theoretical conceptual model in this field is proposed. This model plots the relationships between partnership factors, partnership synergies, resources, and business performance. The results of this research can be effected in inter-organizational collaborations to produce or use technologies in police intelligence. This highlights the role of the police in setting up appropriate security practices in developing the localized operating system and serving the people.

The main challenge in establishing communication with mobile wireless communication systems in inaccessible areas is to use a suitable energy source, which is very important for users on long-term missions. In this study, the usage of solar cells based on perovskite layers is investigated to improve the performance and efficiency of solar cells in solar chargers of wireless mobile communication systems.It is obvious that the optical properties indicate a high absorption index of these samples at orders of 104 cm-1 .The band gap energy values of the samples show a decreasing trend with increasing the thickness of the perovskite layer. The I-V characterization of the studied solar cells showed that with increasing the thickness of perovskite layers, the efficiency of the cells increases, and this efficiency is higher than the cases reported in previous studies. The constructed cells are also highly stable, and may be used in impassable places and mountainous areas for proper charging of mobile wireless communication systems.

In this paper, AKAZE feature detection algorithm is implemented on a 7th generation ARM processor while it is tried to optimize the execution speed. As a first step, parts of AKAZE algorithm that are time-consuming and considered as bottleneck, are identified and rewritten using the C++ to increase the speed of the algorithm. Next, use of the NEON instructions in the ARM processor will be studied in the improvement of the execution speed of the algorithm, and parts of the program that can run simultaneously will be implemented using the specific commands of NEON. This study shows that rewriting time consuming parts of the algorithm will be very effective in terms of speed and the execution time of the algorithm with the proposed method is reduced up to 60 times compared to the existing codes. In addition, use of the NEON instructions in specific parts of the algorithm will be effective in improving the speed of program. The speed increase is very important in applications that need to process real time video images, and therefore can be useful in security systems and fast processing of camera images such as face recognition, license plate recognition, etc. required in FAVA operations.

Digital Video Broadcasting-Terrestrial transmitters (DVB-T/T2) sets are designed for reception of TV broadcasts in VHF (174-230 MHz) and UHF (470-790 MHz) bands. They provide an effective solution for the reception of DVB-T/T2 transmitters broadcasting with various combinations of polarizations in the VHF and UHF bands. In the present paper, for the purpose of Digital Video Broadcasting-Terrestrial (DVB-T/T2) transmitters and military wireless systems, an optimized out-phasing push-pull power amplifier based on Pareto genetic algorithm optimization is designed for ultra-high frequency (UHF) applications using Laterally diffused metal oxide semiconductor (LDMOS) transistor. In the new DVB-T/T2 Multi Frequency Network (MFN) transmitters, radio frequency power (RF) amplifier products, the linearity, efficiency, and broadband bandwidth all needed to be improved. Herein, three LDMOS-RF transistors are utilized in two stages. The multi-objective pareto genetic algorithm (MOPGA) is used to optimize the input and output matching networks, and insertion loss of matching network.In DVB-T/T2 transmitters the power output of modulator is low (1Mw). For this purposed the broadband out-phasing power amplifier is designed with optimal gain (EX: 40dB) in UHF bands. In the first step, the Agilent Design System (ADS) simulations are used. A gain >42 dB was achieved with an optimized matching network. The intermodulation distortion (IMD) is calculated between -29 and -52.2 dBc. The power-added efficiency (PAE) of 32-66% is obtained in the frequency range of 400-900 MHz in this study. Subsequently, an amplifier printed circuit board is fabricated.

In order to increase the reading range and remote control, the blocks on the RFID tag should be designed in a way that they dissipate lower power. In this paper, a voltage reference circuit with 1 V supply voltage for smart police applications is presented. In the proposed voltage reference structure, MOSFET transistors biased in the sub-threshold region. To generate complementary to absolute temperature voltage (CTAT), the voltage of a pn junction (PN junction) generated by a PMOS transistor To achieve the reference voltage of about 753 mV, two proportional to absolute temperature voltage (PTAT) stages are added to the CTAT voltage to create a reference voltage with a minimum temperature dependence. The voltage reference is simulated in the 0.18 μm standard CMOS technology and its area occupation is 0.0064 mm2. The results from post-layout simulation show that in the temperature range of 0°C to 110 °C, the temperature coefficient (TC) is 19.7 ppm/°C. The line regulation (LR) under supply voltage variation from 0.8 V to 1.2 V is equal to 0.058 %/ V. Also, the current consumption of voltage reference is obtained 13.2 nA.