نشریه مهندسی مخابرات جنوب
پیاپی 51 (بهار 1403)

This paper proposes a new approximate full adder (FA) based on the majority logic (ML) concept. The fundamental structure of the ML concept is a 3-input majority voter and is widely utilized in digital arithmetic cells. The ML-based proposed FA works at low power, small delay, and low power-delay-product (PDP). The carbon nanotube field-effect transistor (CNTFET) technology lowers the FA power, while the gate diffusion input (GDI) technique is used as the main technique. The swing issue of the GDI technique is resolved by the dynamic threshold (DT) technique. Compared with its exact circuit, the proposed FA saves 2 majority gates, 3 inverters, and a 4.02 ns delay. In the proposed FA, the PDP is improved by 53.73%. The product of the PDP and the normalized mean error distance (NMED) is called PDPE, and in the presented FA, it is reduced by 9.50%. Moreover, the proposed FA is embedded into a multiplier-less discrete cosine transform (DCT) design, which is an appropriate circuit for very large-scale integration (VLSI) systems. The 8-input DCT architecture consumed 2.2321 mW of power for each DCT operation. Also, the circuit has better performance in terms of PDP-area-product (PDAP). The results of DCT implementations confirm the efficiency of the FA.

The lower power consumption, larger communication bandwidth, and reduced latency are advantages of optical networks over electrical communications. However, there are challenges in these networks, such as routing and connectivity issues, which result in increased network size and wastage. As networks become more complex and larger, building on-chip networks brings problems like communication costs between components and the likelihood of unpredictable failures in communication circuits. Therefore, providing an error-tolerant routing algorithm plays a crucial role in the development of on-chip network architecture. In this article, an adaptive fault-tolerant routing algorithm will be presented, whose main objective is to create the ability to handle a reasonable number of faults without disrupting the healthy nodes in the network. The simulation results of message delay in the proposed method show a gradient norm equal to 1.1691E-5 and μ = 1E-8 for epoch=280, demonstrating its capability to reduce delay in the network. A very slight change in message delay in evaluating the proposed method also indicates the acceptability of the proposed method. Moreover, the presence of a gradient of 1.527E-3 and μ = 1E-7 for epoch=350 in the energy consumption value indicates a reduction in energy consumption compared to conventional methods in existing references, although the proposed system may incur additional overhead compared to some previous methods.

By performing cooperative spectrum sensing in a cognitive radio network, although the network throughput increases with the increase in the number of secondary users, but at the same time, it also causes an increase in energy consumption. This makes it necessary to provide a system that is able to create a tradeoff between throughput and energy consumption. In contrast to the conventional method of spectrum sensing based on one detection threshold, spectrum sensing with double thresholds avoids reporting unreliable data to the fusion center, thus potentially leading to greater energy saving. In this paper, a double threshold spectrum sensing cognitive radio network with a non-ideal reporting channel is optimized. The values of the threshold and the sensing time are jointly optimized to maximize the throughput of the network, provided that the network energy consumption and the amount of interference with the primary users are limited. The optimization problem is formulated and a numerical method is presented to solve it. The simulation results show a flexible system that can simultaneously provide higher throughput and lower energy consumption than the conventional sensing method. These results, while confirming the higher tolerance against the error of the reporting channel, show a significant energy saving of up to 70% by guaranteeing the throughput efficiency greater than 1.

In the present study, a low-power power function generator circuit is proposed for fuzzy applications. The proposed power function generator circuit consists of squaring, square root, and analog multiplier circuits. All the circuits are designed in the subthreshold region to achieve minimum power consumption. The proposed power generator module is based on a fuzzifier circuit, and the analog multiplier circuits are used to adjust the slopes of the fuzzy functions. Besides performing the mentioned adjustments, the proposed circuit can adjust the rising and falling slopes quite separately. Analog multipliers are used in the power generator part to generate desired powers continuously with minimum number of control inputs. The proposed structure is presented in 0.35 μm technology, and the simulation results show that at a supply voltage of 1.3 V, the values of power consumption and error are respectively equal to 0.0036 μw and 0.8%, indicating the improvement of the proposed structure in terms of error and power consumption compared to the best relevant structures in the literature.

This paper presents a new watermarking method to improve the robustness and transparency of extracted and host images. The embedding process is based on decomposing of pyramidal directional filter bank and triangular matrix, while the watermark extraction process is based on Mamdani fuzzy logic. In this design, in order to obtain efficient robustness and transparency, the Harris hawks optimization algorithm is used to find the best value of embedding factor. For this purpose, in the embedding algorithm, pyramid directional filter bank decomposition is utilized and accordingly the approximation sub-bands are divided into 8*8 non-overlapping blocks. Moreover, by decomposing the triangular matrix, which embeds the watermark bits in the matrix element, the use of Mamdani implication and the product inference engine have led to an efficient watermark extraction. The simulation results show that the quality of the watermarked image is equal to 60.6dB. Furethermore, applying the proposed algorithm is strong against attacks.

They are directly integrated into smart distribution networks and can supply stored energy during peak demand periods, while absorbing and storing energy during periods of low demand. This capability helps maintain a balance between supply and demand in power grids, preventing voltage fluctuations and the inability to meet peak loads during high-demand hours. Thanks to technological advancements, it is now possible to upgrade large-scale energy storage facilities. The modern architecture and technology of these facilities facilitate the efficient utilization of renewable energy sources, significantly reducing energy costs and increasing energy efficiency. Additionally, through the use of artificial intelligence algorithms and optimization techniques, the performance and operations of large-scale energy storage facilities can be enhanced. This article focuses on the management of large-scale energy storage facilities, introducing innovative measures that include constraints on the number of charge and discharge processes. Furthermore, the use of the advanced Fakete search algorithm is employed as a powerful and efficient method for solving the proposed model. This algorithm has the capability to find global optimal solutions and can significantly improve the efficiency and profitability of large-scale energy storage facilities. Simulation results demonstrate that adopting this approach in managing large-scale energy storage facilities leads to significant economic impacts. These impacts include reduced energy costs, increased efficiency, greater independence from fossil fuel resources, the preservation of grid stability, and improved performance of the power transmission system.