نشریه مهندسی مخابرات جنوب
پیاپی 50 (زمستان 1402)

Approximate computing is a new design concept that causes a trade-off between circuitry performance and accuracy. The approximate circuits are more useful in error-resilient applications, like image processing. This paper introduces a new imprecise 4:2 compressor with 12 transistors. The presented compressor exhibits low power consumption and provides full-swing outputs, due to the utilization of gate diffusion input and dynamic threshold techniques. The implementation of this compressor using a 16 nm carbon nanotube field-effect transistor (CNTFET) technology yields a minimum area. According to the simulation results, the suggested imprecise 4:2 compressor shows a significant reduction in power consumption and power-delay-product (PDP) compared to the precise 4:2 compressor, with a reduction of 95.18% and 95.27%, respectively. Also, the compressor is evaluated regarding the approximate figure of merits like error rate, mean error distance, and normalized mean error distance. The simulation results affirm the priority of the suggested circuit, especially in digital signal processing.

Quick development of technologies and emergence of Internet of Things technology that can link everything environment us is one of the most amazing developments environ the universe. Need to connect and manage fixed and mobile nodes in this type of networks in order to create a proper interaction among them, addition to solving many serious limitations, has caused researchers to address various challenges of this technology. One of the most important challenges is to provide an efficient routing protocol that is able to manage different aspects of network efficiency alongside manages mobility of nodes and dynamic topology of network. RPL (Routing Protocol for Low Power & Lossy Networks) is one of the famous routing protocols is proposed for internet of things networks. This protocol is designed specifically for Internet of Things networks with fixed nodes, so we developed a new routing algorithm called A-RPL to support mobility and solved some challenges caused by mobility. Our algorithm works better than some superior algorithms were proposed in recent years, because they still suffer from various disadvantages, especially for mobile networks

In this article, a system for recognizing Persian sign language alphabets is presented. This system is able to recognize 32 hand postures for Persian alphabets and translate it into Persian text. For this purpose, images of hand positions have been considered for each letter of the alphabet. The database contains 600 images of different people taken by a digital camera. We have transferred all the image data to the binary domain and resized them with a single scale. Image data preprocessing includes image cropping and noise removal. After pre-processing, 3 algorithms are proposed to extract features. The proposed algorithms include the image segmentation algorithm, the distance between border contour points and the center of gravity algorithm, and Radon transformation. Algorithm of the distances between the border contour points and the center of gravity shows how the points are placed on the peripheral curve of the hand in relation to each other and to the center of gravity, and therefore provides suitable structural information for describing states. The next algorithm is based on image segmentation. In this algorithm, the ratio of the number of white pixels to the total number of pixels is calculated in each of the areas. In Radon transformation, in addition to obtaining the general information of the image in each of the modes, we have increased the accuracy of the detection by using the proposed method and discarding additional information. The proposed methods also provided good results on other image databases.

In this paper, we limit our attention to full adders based on the GDI method, circuits that are commonly used in high-speed circuits and are more prone to noise. So far, a comprehensive review on noise immunity and ambient temperature change of full adders based on the GDI method has not been presented, and most of the studies have compared their proposed design with other full adders, which are mainly not based on the GDI method. These full adder cells were evaluated by various simulations such as supply voltage change, capacitive load change, ambient temperature change and process-voltage-temperature (PVT) changes in 45 nm CMOS technology. A noise immunity curve (NIC) was derived for full adder cells to identify better-performing full adder cells. The unity noise gain (UNG) was also investigated to evaluate the noise. Finally, a comprehensive comparison was made in terms of propagation delay, power consumption, power-delay product (PDP), voltage swing, sensitivity to process changes and noise for full adders based on the GDI method. The obtained results can be useful in the decisions of integrated circuit designers to choose the appropriate structure of the full adder based on the GDI method

Diagnosing bearing defects is one of the basic tasks in machine health monitoring, because bearings are critical components of rotating machines. This paper proposes a new method for detecting defects in bearings based on a combination of feature extraction algorithms in which a two-dimensional signal is used. Different from other classical one-dimensional signal processing methods, the proposed method of this paper converts one-dimensional vibration signals into two-dimensional signal (image), then image processing methods are used to analyze the image signal in order to classify the defects that have occurred. arrive at the bearing. Converted images from vibration signals often have specific texture characteristics, and the texture of each defective category is different. In addition, each descriptor extracts spatial features. Some features are weak and others are strong. In this article, the method of removing additional key points of SIFT (RKEM SIFT) is used. In addition, for each descriptor, the best features are selected using the non-linear principal component analysis method. Finally, the selected features are combined and four classification methods are applied to achieve the best classification performance and after comparison, the best classification method is selected. The performance of the proposed algorithm is evaluated on the standard bearing data set of Case Western Reserve University. The simulation results show that the proposed method performs better than other methods of fault finding of rolling bearings.

IoT-based devices are constantly sending data to the cloud. However, the centralization of cloud data centers and the long distance to the location of data sources has reduced the efficiency of this paradigm in real-time applications. Fog computing can provide the resources needed by Internet of Things devices in a distributed manner at the edge of the network without involving the cloud. Therefore, processing, analysis and storage are closer to the source of data and end users cause the delay is reduced. Every Internet of Things program includes a set of Internet of Things services with different quality of service requirements, whose required resources can be provided by deploying on cloud nodes. This study deals with the challenge of locating Internet of Things services as an autonomous planning model in fog computing. We develop the colonial competition algorithm as a meta-heuristic approach to solve this problem. Since fog nodes with enough resources can host several IoT services, we consider resource distribution in the localization process. The proposed algorithm prioritizes Internet of Things services to reduce delay and solves the multi-objective positioning problem. The results of the experiments show that our algorithm can effectively improve the performance of the system and have 15% to 31% better effectiveness than the best results of the advanced algorithms in the literature.