نشریه فناوری اطلاعات و ارتباطات انتظامی
پیاپی 10 (تابستان 1401)

QoS (Quality of Service) controls network resources by setting priorities for a specific type of configuration. There are many clustering algorithms for cloud workload clustering, most of which are static. In fact, according to a dynamic algorithm, in the face of huge databases that are seen in real-time and according to the existing conditions of clustering. Speed ​​and accuracy are some of the most important factors that the proposed method should include. To solve this challenge, one of the most important components of service quality is the use of resources. In this article, it is proposed to use the Chameleon algorithm, which performs clustering based on pairs of neighborhood similarity components and dynamic clustering capability. 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 operate in the field of order and security. The results of accurate and dynamic clustering patterns from the huge databases of this organization can greatly improve the quality of dealing with this information. The science of data mining and the use of its clustering techniques is essential for information analysis. Correct analysis, correct positioning, and correct receiving of information at any moment will lead to the correct result. Well, dynamism in clustering and knowing the correct situation at any moment can also lead to security. By comparing the Chameleon algorithm with the Gaussian algorithm, 36% improvement in time can be observed. Also, the resource consumption of Chameleon algorithm has decreased by 50% compared to Gaussian algorithm. Therefore, it can be concluded that the proposed method has the dynamic cluster in the shortest time due to reducing the use of resources

In this research we present the novel designs for high speed interconnection networks. Despite classical interconnection networks, in these design two and three processing elements can communicate in the same time on the same line. The designs comprise simple elements like MOSCAPs. It enjoys very high speed and consumes low chip area. Even if in the first glance it seems complicated to be realized in real VLSI but using MOSCAPs results in simple realization of the overall circuit as well as achieving very high speed. The simulation results confirm that the voltage mode MVL interconnection network has achieved simultaneous data transfer, and the static power consumption and delay has been improved. According to the subthreshold characteristics of 180 nm technology compare to 65nm, the mean delay of the proposed structures in 180 nm is lower than 65 nm.The results show that the delay and power consumption in interconnection networks with two processing elements using wires compared to DC-2 approach in 65 nm technology are decreased respectively from 500.0126×10-6 to 159.9667 ×10-12 and 3.422×10-9 to 178.1493×10-15 . Due to the capability of simultaneous communication by defined voltage levels, , there is significant improvement on the performance characteristics, such as transmission delay and power consumption.The results of this study can be used to enhance the security of information and communication technology systems, organization and business equipment of future generations of security systems.

With the expansion of industries and of new technologies that require more computing resources, supercomputer has attracted much attention as a platform for high performance computing services. Although various features and indicators for Testing and evaluation of supercomputers have been proposed, however, a comprehensive classification of these features has not been provided so that the developer, designer, or user can easily compare and select supercomputers by scoring. Therefore, selecting a supercomputer solution to meet the requirements is a complicated problem. This paper presents a new feature-based and integrated taxonomy (Hierarchical Supercomputer Performance Indicators Taxonomy), and their scoring and ranking routine which facilitates the process of supercomputer evaluation and selection. Also, this paper provides a case study and using the proposed framework presents a comparison between some commercial and research supercomputers including Fukaku's ideal supercomputer, Sharif supercomputer, Aramco supercomputer and ITU supercomputer. The ranking results show that Aramco supercomputer, ITU supercomputer and Sharif supercomputer have 65.9%, 57.6% and 48.2% of ideal supercomputer points, respectively. Also, the percentage of coverage of a customer's needs with supercomputers and the percentage of index coverage categorized separately for supercomputers are determined. In this article, the proposed method has been compared with the Top500 method, which shows that this method facilitates the ranking, comparison and selection of the appropriate supercomputer in various fields such as military and law enforcement systems by considering various aspects of design and implementation.

The intelligent system for detecting and recognition traffic signs detects traffic signs on its way and warns the driver by receiving images from the camera installed on the vehicle. These systems can also be used in self-driving and smart vehicles and usually have two main parts: sign recognition from other parts of the image and the other detecting and recognizing the type of traffic signs. In this article, traffic signs are identified using the power of convolutional networks. In fact, designing a system for detecting traffic signs is associated with many problems: Images taken may be noisy for a variety of reasons. Intensity and dimming of ambient light change the color of images. Or the signs may not be exactly the same as the standard defined, and most importantly most of the work has been done on foreign traffic signs. In this paper, a system is provided to detecting and recognition any of the traffic signs so that it can work on native and domestic datasets and consider the "power" and "speed" of detection, which are two important factors in smart vehicles. The performance results of this system show that the accuracy of traffic sign detection on training data and test data is 99% and one of the salient features of this system compared to similar tasks is the high speed of traffic sign detection in any type of weather and Light conditions and the presence of noise.

Image fusion is a set of operation which is done by combination of two or more input images and the result is a better image with more useful information, in a specific application direction. Images can be taken from the same source with different parameters or from completely different sources. In this paper a novel multi focus image fusion method based on depth map and focused regions is proposed. The combination of depth map with segmented focused from de-focused areas by means of deep convolutional neural networks will improve the fusion performance. In the presented research, the input images are passed through a deep neural networks and then merged with the images obtained from the depth map in accordance with the strategy of image fusion. Our approach can yield much fewer ringing results. Comparative experimental results based on various real images demonstrate that our approach outperforms the state of the art methods.

Various natural and human disturbances can widely cause stoppage or disruption in the activity and cooperation of companies and aid organizations, supply chain resilient and agile during a crisis is one of the most important issues that occupy the minds of senior managers related to crisis management. Resilience and agility using technologies such as blockchain creates an advantage in relief organizations. Many of the aid seekers are deprived of proper services due to poor coordination and cooperation, therefore, the need for a stable and agile supply chain based on blockchain in times of crisis is necessary to overcome the challenges.

In this research, using the fuzzy DEMATEL method, basic and important variables Related to block chain technology and effective variables in crisis management supply chain and relationships between them were identified. After expert approval, cause and effect diagrams and state-flow diagrams were drawn using the system dynamics method.

The obtained results include 22 variables related to the resilience of the supply chain in times of crisis and the block chain. The cause and effect diagram related to the variables was drawn and some strengthening and moderating loops of the crisis management process were extracted.

The inherent elements of the blockchain are effective on the key elements of crisis management, and it improves the ability to support and retrieve information, the ability to predict needs and prevent unfair distribution of goods and services, the ability to respond better and the ability to learn service organizations.

Today, the Internet of Things (IOT) as a growing global network is susceptible to various attacks, and one of the dangerous attacks of the network layer is cyber attacks. Maintaining security against various cyber attacks within the network is considered as one of the important challenges of IoT. Intrusion detection system (IDS) is one of the main and effective defense methods to deal with attacks in IOT and plays an important role to identify and prevent cyber attacks in IoT networks. Different attacks have their own behavior, and attack detection using the combined method achieves a suitable performance in detecting new types of attacks. In this paper, a new hybrid method for attack and anomaly detection based on machine learning algorithms (Random Forest (RF), Perceptron Neural Network (MLP), Gradient Boosted Decision Trees (GBT) and K-Nearest Neighbor (K-NN)) is presented. It is proposed in the Internet of Things. Unlike the existing works that focus on single classifiers, this paper uses ensemble boosting and bagging algorithms to enhance the performance of intrusion detection system (IDS). The learning process and experiments have been performed on UNSW_NB15 and NLS_KDD datasets. The results show that random forest algorithms with accuracy (0.973 and 0.95) and Bagging with accuracy (0.998 and 0.997) effectively detect cyber attacks, and the ensemble algorithm of Bagging in terms of accuracy, recognition accuracy, recall and score. The F1 is better than comparable models.

Short-range telecommunication systems based on ultra wide band technology have unique features. In this article, according to the smart police approach, some applications for implementation in the police are introduced. Wireless sensor networks at borders, high-speed data transmission, intelligent transportation, live target detection behind the wall, and positioning are items that can be used in modern police equipment shortly. The design of the front of the ultra wide band receiver has requirements. In this article, the points related to noise figure, power consumption, linearity, gain, input matching network, and Electrostatic discharge circuit have been studied for designing low noise amplifiers. Also, a low-noise amplifier is selected, and a diode circuit is applied to it for Electrostatic discharge. The simulation results in Cadence software show that for the addition of a parasitic capacitor equivalent to an ESD circuit, the variation of the input matching and noise figure is less than 10%.

The use of smart radio equipment with high performance is one of the requirements of the smart police, and in this regard, it is necessary to consider operational requirements in the design of different parts of the radio transmitter and receiver. In the transmitter section, the power amplifier has some features such as power consumption, output power, and power added efficiency which are significant items in the design. This paper deals with the design of high- performance Class-E power amplifiers for the Radio communication systems. For this purpose, a cascade structure consisting of an amplifier is used as a switch to amplify the signal and an amplifier to increase reliability. In this circuit, one transistor is biased in the triode region as a switch and the other transistor is biased in the triode or saturation region. In this paper, a cascade class E power amplifier with a driver circuit and a compensator circuit is proposed to increase the output power and power added efficiency (PAE). The presented PA in this paper is appropriate for Radio communication. In this paper, the output power and PAE is improved by changing the structure of the PA. The PA has 2.6 GHz operating frequency, output power of 31.4 dBm, maximum PAE of 55.5%. The designed circuits are simulated with Cadence software.

Sensor networks consisting of hundreds of thousands of nodes are usually referred to as low-power and lossy networks (LLNs). In these networks, nodes are interconnected by low data-rate lossy links, which have low stability due to low packet delivery rates. Since routing protocols play an important role in the performance of sensor networks, the required protocols must be designed to address energy-related issues. This helps not only to reduce the energy consumption of the overall network but also to evenly distribute the energy load among the network nodes to increase the lifetime of the network. In such networks, the routing protocol for low-power and lossy network (RPL) can minimize the existing issues and improve performance. This paper aims at minimizing energy consumption by specifying the best path in the network for data transmission. To achieve this aim, the particle swarm optimization (PSO) algorithm is used. This algorithm shows a good convergence for the movement of particles towards the best path and presents the best solution with the maximum number of particles in different particle velocity ranges. The results of this study show that as the number of particles increases, the cost function decreases by 58% on average, and thus, lower energy is required to reach the root. Moreover, with increasing velocity range, the cost function decreases by 43% on average. The proposed algorithm shows a 43% decrease in energy consumption on average.