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Article Fitting a Recurrent Dynamical Neural Network To Neural Spiking Data: Tackling the Sigmoidal Gain Function Issues(Tubitak Scientific & Technological Research Council Turkey, 2019) Doruk, Reşat ÖzgürThis is a continuation of a recent study (Doruk RO, Zhang K. Fitting of dynamic recurrent neural networkmodels to sensory stimulus-response data. J Biol Phys 2018; 44: 449-469), where a continuous time dynamical recurrentneural network is fitted to neural spiking data. In this research, we address the issues arising from the inclusion ofsigmoidal gain function parameters to the estimation algorithm. The neural spiking data will be obtained from the samemodel as that of Doruk and Zhang, but we propose a different model for identification. This will also be a continuoustime recurrent neural network, but with generic sigmoidal gains. The simulation framework and estimation algorithmsare kept similar to that of Doruk and Zhang so that we can have a solid base to compare the results. We evaluatethe estimation performance in two different ways. First, we compare the firing rate responses of the original and theestimated model. We find that responses of both models to the same stimuli are similar. Secondly, we evaluate variationsof the standard deviations of the estimates against a number of samples and stimulus parameters. They show a similarpattern to that of Doruk and Zhang. We thus conclude that our model serves as a reasonable alternative provided thatfiring rate is the response of interest (to any stimulus).Article Citation - Scopus: 2An Approach for Performance Prediction of Saturated Brushed Permanent Magnet\rdirect Current (dc) Motor From Physical Dimensions(Tubitak Scientific & Technological Research Council Turkey, 2022) Asl, Rasul Tarvirdilu; Zeinali, Reza; Ertan, Hulusi Bulent; Tarvirdilu-Asl, Rasul; Tarvirdilu–Asl, RasulAn analytical approach for performance prediction of saturated brushed permanent magnet direct current\r(DC) motors is proposed in this paper. In case of a heavy saturation in the stator back core of electrical machines, some\rflux completes its path through the surrounding air, and the conventional equivalent circuit cannot be used anymore.\rThis issue has not been addressed in the literature. The importance of considering the effect of the flux penetrating\rthe surrounding air is shown in this paper using finite element simulations and experimental results, and an analytical\rapproach is proposed to consider this effect on magnet operating point determination and performance prediction of\rsaturated brushed permanent magnet DC motors. An analytical method is also presented to determine the boundary\rradius of the surrounding air for obtaining accurate results in finite element (FE) solutions and analytical calculations.\rAn analytical approach based on Carter’s coefficient is also proposed to calculate the effective length of the magnet when\rthe length of the magnet and rotor length are not the same. The accuracy of the proposed analytical model is illustrated\rusing finite element simulations and experimental results. With this accuracy, this analytical model is very suitable to\rbe used for reliable and quick mathematical design optimization.Article ISAR Imaging of Drone Swarms at 77 GHz(Tubitak Scientific & Technological Research Council Turkey, 2025) Coruk, Remziye Busra; Kara, Ali; Aydin, ElifThe proliferation of easily available, internet-purchased drones, coupled with the emergence of coordinated drone swarms, poses a significant security threat for airspace. Detecting these swarms is crucial to prevent potential accidents, criminal misuse, and airspace disruptions. This paper proposes a novel inverse synthetic aperture radar (ISAR) imaging technique for high-resolution reconstruction of drone swarms at 77 GHz millimeter wave (mmWave) frequency, offering a valuable tool for military and defense antidrone systems. The key parameters affecting down-range and cross-range resolution (0.05 m), ultimately enabling the generation of detailed ISAR images are discussed. Here, we create diverse scenarios encompassing various swarm formations, sizes, and payload configurations by employing ANSYS simulations. To enhance image quality, different window functions are evaluated, and the Hamming window is selected due to its highest peak signal-to-noise ratio (PSNR) (16.3645) and structural similarity (SSIM) (0.9067) values, ensuring superior noise reduction and structural preservation. The results demonstrate that the effectiveness of high-resolution ISAR imaging in accurately detecting and characterizing drone swarms pave the way for enhanced airspace security measures.
