This paper proposes a downlink synchronization and cell identity (CID) estimation technique for cellular systems based on orthogonal time-frequency space (OTFS). In the proposed technique, each base station (BS) transmits one detection preamble (DP) and two OTFS symbols (pilot and secondary-CID (SCID) signal (SS)). The DP based on a linear frequency-modulated (LFM) waveform, called proposed-LFM (pLFM), carries the primary-CID (PCID) for symbol timing (ST) synchronization, whereas the SS based on the Zadoff-Chu (ZC) sequence mapped in the delay-Doppler domain carries the SCID. The pLFM is obtained by discretizing the LFM waveform, increasing the frequency sweeping parameter beyond the limit of the operational bandwidth, and incorporating the PCID into the LFM waveform. The autocorrelation and cross-correlation functions of the pLFM are analyzed to examine its correlation properties under the influence of a high Doppler shift. To reduce the undesirable correlation properties (side peak and time ambiguity), a receiver processing (RP) scheme is developed for the pLFM. It is found that the pLFM with RP is suitable for the DP design because it can provide an accurate ST in high-mobility scenarios. In addition, the influence of OTFS modulation on the ZC sequence is derived and a low-complexity detection algorithm based on message passing is applied to detect SCID at the user equipment. The simulation results demonstrate that the proposed downlink synchronization and CID estimation techniques are suitable for OTFS-based cellular systems in high-Doppler environments.

Noise or artifacts in an image, such as shadow artifacts, deteriorate the performance of state-of-the-art models for the segmentation of an image. In this study, a novel saliency-based region detection and image segmentation (SRIS) model is proposed to overcome the problem of image segmentation in the existence of noise and intensity inhomogeneity. Herein, a novel adaptive level-set evolution protocol based on the internal and external functions is designed to eliminate the initialization sensitivity, thereby making the proposed SRIS model robust to contour initialization. In the level-set energy function, an adaptive weight function is formulated to adaptively alter the intensities of the internal and external energy functions based on image information. In addition, the sign of energy function is modulated depending on the internal and external regions to eliminate the effects of noise in an image. Finally, the performance of the proposed SRIS model is illustrated on complex real and synthetic images and compared with that of the previously reported state-of-the-art models. Moreover, statistical analysis has been performed on coronavirus disease (COVID-19) computed tomography images and THUS10000 real image datasets to confirm the superior performance of the SRIS model from the viewpoint of both segmentation accuracy and time efficiency. Results suggest that SRIS is a promising approach for early screening of COVID-19.

The beam search protocol in cellular systems requires a significant amount of search time and network resources in order to select a serving base station with the optimal beam pair. In 3D beamforming, the processing time increases significantly because the azimuth and elevation angles need to be considered during beam search. In this paper, we propose a fast 3D beamforming technique for millimeter wave (mmWave) cellular systems with a uniform planar array (UPA). In the proposed technique, the beam search time is reduced significantly by decomposing a UPA into a set of horizontal/vertical uniform linear arrays (ULAs), from which the optimal beam direction in azimuth/elevation angle is obtained. Two types of signals, Zadoff–Chu sequence and linear frequency modulated waveform are used for designing the beam search preamble (BSP), which allows a mobile user to distinguish beams in multi-cell multi-beam environments. The strengths and weaknesses of the two proposed BSPs are analyzed after performing simulation using a simple mmWave cellular system model with UPA. Furthermore, it is demonstrated that the proposed technique significantly reduces the beam search time, i.e., number of beam scans, as compared with the conventional technique.

In this paper, a random access preamble (RAP) design technique for underwater acoustic cellular systems is proposed. After showing that the conventional RAP used in long term evolution (LTE) systems is not appropriate for underwater acoustic cellular systems, two different types of RAPs (RAP 1 and RAP 2) are proposed to detect the identity of underwater equipment/nodes (UEs) and estimate the time delay between a UE and an underwater base station (UBS) at the physical layer. RAP 1 is generated using a Zadoff-Chu (ZC) sequence where the identity of the UE is mapped to its root index, whereas RAP 2 is generated using a linear frequency modulation (LFM) waveform where the identity of the UE is mapped to its frequency sweeping parameter and frequency shifting parameter. Ambiguity functions (AFs) and cross-ambiguity functions (CAFs) of RAP 1 and RAP 2 are derived to investigate their correlation properties under the effect of time delay and Doppler shift. The performance of RAP detection is investigated by analyzing the detection probabilities and false alarm probabilities of RAP 1 and RAP 2 in a Doppler environment. By evaluating the performances of RAP 1 and RAP 2 in various situations, it is concluded that RAP 2 is more suitable for underwater acoustic cellular systems. The AF and CAF analytically obtained in this paper are shown to be similar to those obtained using experimental data.

In the realm of computer vision, image segmentation has become a crucial task with widespread applications, particularly in medical imaging. Although there have been significant advancements in image segmentation methods, challenges persist in accurately delineating intricate structures within noisy and varied medical images. In this study, we have developed a novel segmentation model that combines the distance regularized level set evolution (DRLSE) model with a local gradient flow-based image (LGFI) and saliency maps. This innovative fusion addresses the limitations of existing methods and offers robust and precise solutions for medical image segmentation. We provide comprehensive mathematical formulations and demonstrate the effectiveness of the proposed model across diverse medical images. Through quantitative and qualitative analyses of the brain tumor segmentation (BraTS) 2019 dataset, we have demonstrated the superior accuracy, robustness, and computational efficiency of the proposed model in comparison with the state-of-the-art methods. This research marks a significant step toward enhancing medical image analysis, with potential applications in diagnostics and healthcare practices.