Transactions on Machine Intelligence2821-16934120210301Calculation of Current-Voltage Characteristics for Small DNA Chains1615981010.47176/TMI.2021.1ENA.KokabiDepartment of Electrical Engineering, Hamedan University of Technology, Hamedan , IranS.KavoosiDepartment of Electrical Engineering, Hamedan University of Technology, Hamedan , IranJournal Article20201113This study explores the electronic transport characteristics of short deoxyribonucleic acid (DNA) chains, which have emerged as promising nanostructures for future bioelectronic applications. DNA, a molecule essential to the storage and transmission of genetic information in all known living organisms and many viruses, has attracted increasing interest in the field of molecular electronics due to its unique structural and conductive properties. In this research, the current–voltage (I–V) behavior of homogeneous nucleotide chains are analyzed using the Non-Equilibrium Green's Function (NEGF) formalism, a powerful quantum mechanical approach for studying charge transport in nanoscale systems under non-equilibrium conditions. The chains are modeled as linear sequences composed of identical nucleotides to isolate the intrinsic transport features. The applied bias voltage is systematically varied in the range of 0 to 4 volts to assess the electrical response of the system across both sub-gap and above-gap energy regimes. The simulation results reveal key insights into the conduction mechanisms within such molecular structures, which are highly sensitive to the applied voltage and molecular configuration. These findings contribute to a better understanding of DNA-based conductive behavior and may inform the design of future molecular-scale components in nanoelectronic circuits.https://www.tmachineintelligence.ir/article_159810_3a91a9d5f330dd057e173463876dc995.pdfTransactions on Machine Intelligence2821-16934120210301A New Potentiometer-Based Goniometer Design in Joint Angle Measurement71215981110.47176/TMI.2021.7ENS.ShahtalebiDepartment of Electrical Engineering, K.N.Toosi University of Technology, Tehran, IranJ.AboueiDepartment of Electrical and Computer Engineering, Yazd University, Yazd, IranJournal Article20211129Accurate measurement of joint angles is critical in the diagnosis, monitoring, and treatment of musculoskeletal disorders. In this study, we focus on developing a reliable system for measuring the elbow joint angle in a clinical setting, utilizing an exoskeletal linkage-based approach. To achieve this, we design and implement a novel goniometer that combines mechanical simplicity with high measurement accuracy. The device is built around a modern AVR microcontroller platform, ensuring ease of manufacturing, low power consumption, and real-time performance. One of the key innovations of this work lies in the ergonomic and wearable physical design, which enhances user comfort and minimizes interference with natural joint movement. The proposed system is lightweight, compact, and can be worn for extended periods without discomfort, making it particularly suitable for patient use in both clinical and ambulatory environments. Furthermore, the goniometer supports real-time data acquisition and communication via a USB interface, allowing for seamless integration with external monitoring or data logging systems. This feature facilitates mobility during experimental procedures while maintaining continuous tracking of joint kinematics. Overall, the proposed solution presents a practical and efficient tool for clinicians and researchers seeking accurate and wearable joint monitoring systems.https://www.tmachineintelligence.ir/article_159811_e15ff11795731feaa6702997f1522c95.pdfTransactions on Machine Intelligence2821-16934120210301Interior Permanent Magnet Induction Motor Design Considerations132015981210.47176/TMI.2021.13ENM.ShiraviDepartment of Electrical Engineering, Kashan University, Kashan, IranH.MalekiDepartment of Engineering, Islamic Azad University, Sama, Khomeinishahr, IranS.AmirDepartment of Mechanical Engineering, Kashan University, Kashan, IranM. H.RanjbariDepartment of Electrical Engineering, Lorestan University, Lorestan, IranJournal Article20201218The design and simulation of electric machines—such as Induction Machines (IMs) and Interior Permanent Magnet (IPM) motors—can be significantly optimized in terms of both time and cost by applying certain strategic techniques within Ansys Maxwell software. This paper presents a structured, step-by-step methodology for the laboratory-scale design and simulation of an IM, emphasizing best practices that streamline the modeling process. A transient solver is selected as the simulation type due to its suitability for capturing dynamic electromagnetic behavior, although it is computationally intensive. The study further explores the integration of U-shaped permanent magnets within the rotor yoke of an IPM motor, analyzing the impact of this unique configuration on machine performance. The design and simulation results confirm the feasibility of testing and evaluating various motor parameters under realistic conditions. Notably, although the U-shaped magnet configuration introduces geometric and computational complexity, its successful implementation suggests that alternative and potentially simpler structures can be simulated with relative ease. The approach outlined in this study provides a practical reference for engineers and researchers engaged in electric motor design, enabling more efficient modeling and analysis of complex machine architectures using commercial finite element analysis tools.https://www.tmachineintelligence.ir/article_159812_ad41a1bba4f64e6bb8641acea3813f3f.pdfTransactions on Machine Intelligence2821-16934120210301Extended Access Control on electronic passport with the aim of overcoming limited computing resources212815981310.47176/TMI.2021.21ENSh.GhanbariDepartment of Computer-Software Engineering, Faculty of Engineering, Islamic Azad University, South Tehran Branch, Tehran, IRANM.RSalehnamadiAssistant Professor, Department of Software Engineering, Software Engineering, Faculty of Engineering, South Tehran Branch, Islamic Azad University, Tehran, IranJournal Article20201220In this project, Extended Access Control on the electronic passport was designed to overcome limited computing resources. Today, experts are looking for safer ways to identify and authenticate authenticity. One of the most successful of these ways is the use of biometrics. In this project, in order to reduce the volume of computing, the Fast Exponential method has also been added to Diffie- Hellman, as well as to enhance the security of the proposed research protocol and reduce the success rate of attacks such as a man-in-the-middle attack to steal information, from fingerprint to extract some of the required parameters of the Diffie-Hellman method (parameters q and g) is used. To this end, three different scenarios were raised. The results of the simulation showed that the proposed method reduces the computational load of the classical Diffie-Hellman method and, therefore, reduces the run-time. Also, the results showed that the first scenario is better than the other two scenarios in terms of both runtime and computational load.https://www.tmachineintelligence.ir/article_159813_1a8230999a8576c413af608f71a9ce4c.pdfTransactions on Machine Intelligence2821-16934120210301A Simple Approach for Real Time Speed Estimation of On Road Vehicles Using Video Sequences293515981410.47176/TMI.2021.29ENA.MosayebiElectrical Engineering Department, Shahrood University, Shahrood , IranH.KhosraviElectrical Engineering Department, Shahrood University, Shahrood , IranJournal Article20210103<br>This study presents a practical and efficient method for real-time speed estimation of vehicles on two-lane roads, particularly suited for surveillance applications. The proposed approach utilizes video input captured from a fixed, stationary camera without requiring prior camera calibration—making it both cost-effective and easy to deploy. The algorithm operates in two primary phases. In the initial phase, an interactive setup allows the user to manually define lane boundaries and corresponding real-world distances once at the beginning of the analysis. Based on this input, two rectangular regions of interest (ROIs) are assigned to each lane. In the second phase, moving vehicles are detected by generating an approximate binary foreground mask through frame differencing of consecutive video frames. By calculating the centroids of moving objects and measuring the norm values of the binary mask within each ROI, the time taken by each vehicle to traverse the space between the predefined lines can be determined. From this time and known distance, the average speed of each vehicle is estimated. Despite its algorithmic simplicity, the method achieves real-time performance without requiring specialized hardware. Experimental results demonstrate a mean speed estimation error of ±3 km/h and a vehicle detection accuracy of approximately 83%.https://www.tmachineintelligence.ir/article_159814_4b9f2b24d1be524b60b04c1f91ca721a.pdfTransactions on Machine Intelligence2821-16934120210301Noise Reduction in Medical X-Ray Images Using Wavelet and Neural Networks365215982110.47176/TMI.2021.36ENM.ZamaniDepartment of Electrical Engineering, Semnan UniversityS.AzadiDepartment of Electrical Engineering, Semnan UniversityO.Rahmani SeryasatAssistant professor, Department of Electrical Engineering, Adiban Institute of Higher Education, Garmsar, IranJournal Article20210105Noise reduction in X-ray imaging has been a critical area of research due to its direct impact on image clarity and diagnostic accuracy. This noise primarily results from the reduction of X-ray power, which is necessary to minimize radiation exposure and associated health risks. Traditional noise reduction methods, such as wavelet domain thresholding techniques like BayesShrink, have been widely explored. However, their effectiveness is often limited due to the Poisson-distributed nature of X-ray noise, making standard thresholding approaches suboptimal. In this study, we propose an advanced denoising framework that integrates wavelet domain processing with a genetic algorithm to optimize the BayesShrink threshold. To further enhance image quality, we employ a multi-layer perceptron (MLP) neural network, which improves clarity by refining local pixel intensities. Despite its effectiveness, neural network-based denoising alone struggles to eliminate high-intensity noise. To address this limitation, we introduce a directional adaptive median filter to suppress severe noise while preserving crucial image structures. Since median filtering may compromise edge details, we incorporate an edge reconstruction step to restore essential structural information. Simulation results demonstrate that our proposed approach outperforms conventional methods in terms of Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index (MSR), and Contrast-to-Noise Ratio (CNR). The findings indicate that our hybrid method provides significantly improved image clarity compared to existing denoising techniques, making it a promising solution for enhancing X-ray image quality while maintaining diagnostic integrity.https://www.tmachineintelligence.ir/article_159821_fe9788b4edb3db87433c6cfa8062af0f.pdf