Frequency Estimation Using Complex-Valued Shifted Window Transformer

Estimating closely spaced frequency components of a signal is a fundamental problem in statistical signal processing. In this letter, we introduce 1-D real-valued and complex-valued shifted window (Swin) transformers, referred to as SwinFreq and CVSwinFreq, respectively, for line-spectra frequency estimation on 1-D complex-valued signals. Whereas 2-D Swin transformer-based models have gained traction for optical image super-resolution, we introduce for the first time a complex-valued Swin module designed to leverage the complex-valued nature of signals for a wide array of applications. The proposed approach overcomes the limitations of the classical algorithms such as the periodogram, MUSIC, and OMP in addition to state-of-the-art deep learning approach cResFreq. SwinFreq and CVSwinFreq boast superior performance at low signal-to-noise ratio SNR and improved resolution capability while requiring fewer model parameters than cResFreq, thus deeming it more suitable for edge and mobile applications. We find that the real-valued Swin-Freq outperforms its complex-valued counterpart CVSwinFreq for several tasks while touting a smaller model size. Finally, we apply the proposed techniques for radar range profile super-resolution using real data. The results from both synthetic and real experimentation validate the numerical and empirical superiority of SwinFreq and CVSwinFreq to the state-of-the-art deep learning-based techniques and traditional frequency estimation algorithms. The code and models are publicly available at https://github.com/josiahwsmith10/spectral-super-resolution-swin.Comment: Submitted to IEEE Geoscience and Remote Sensing Letter

Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE

A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-Resolution

In this paper, we develop a novel super-resolution algorithm for near-field synthetic-aperture radar (SAR) under irregular scanning geometries. As fifth-generation (5G) millimeter-wave (mmWave) devices are becoming increasingly affordable and available, high-resolution SAR imaging is feasible for end-user applications and non-laboratory environments. Emerging applications such freehand imaging, wherein a handheld radar is scanned throughout space by a user, unmanned aerial vehicle (UAV) imaging, and automotive SAR face several unique challenges for high-resolution imaging. First, recovering a SAR image requires knowledge of the array positions throughout the scan. While recent work has introduced camera-based positioning systems capable of adequately estimating the position, recovering the algorithm efficiently is a requirement to enable edge and Internet of Things (IoT) technologies. Efficient algorithms for non-cooperative near-field SAR sampling have been explored in recent work, but suffer image defocusing under position estimation error and can only produce medium-fidelity images. In this paper, we introduce a mobile-friend vision transformer (ViT) architecture to address position estimation error and perform SAR image super-resolution (SR) under irregular sampling geometries. The proposed algorithm, Mobile-SRViT, is the first to employ a ViT approach for SAR image enhancement and is validated in simulation and via empirical studies.Comment: Accepted to Proc. IEEE WMC

Detection of prevalence, antibiotic resistance and virulence factors of enterococcus spp. ısolated from ready to eat foods

Bu çalışmada tüketime hazır bazı gıdalarda Enterokok türlerinin prevalansı, antibiyotik dirençliliği ve virülans faktörleri belirlendi. Analize alınan 187 gıda örneğinin 112 (%59,9)’sinde 114 Enterococcus spp. izole edildi. Et ürünlerinden 39 (%34,8), peynirlerden 42 (%37,5), salatalardan 25’i (%22,3) ve helva örneklerinden 8 (%7,1)’inde Enterococcus spp. izolatı elde edildi. Antibiyotik dirençlilik testi sonuçlarına göre, elde edilen Enterokok izolatlarının çalışmada kullanılan antibiyotiklerden en az dördüne dirençlilik gösterdiği tespit edildi. İzolatların hiçbirinde gelatinaz aktivitesi gözlenmezken, 36’sında (%31,6) hemolizin aktivitesi pozitif tespit edildi. Sonuç olarak starter kültür olarak kullanılabileceği ve insanlar için zararsız olduğu düşünülen bazı Enterokok türlerinin, virülens faktörler ve sahip olabilecekleri antimikrobiyal direnç bakımından halk sağlığı ve gıda güvenliği açısından bir risk oluşturabilmektedir. Bu nedenle gıda endüstrisinde starter olarak kullanılabilecek Enterokok türleri, patojenite özelliği bulunmayan ve antibiyotik direnç genlerine sahip olmayanlardan seçilmelidir.In this study, we identified the prevalence of Enterococcus spp., antibiotic resistance and several virulence factors of some ready-to-eat foods. Totally 114 Enterococcus spp. were isolated in 112 (59.90 %) of the 187 food samples analysed. Enterococcus spp. isolates were obtained from 39 samples of meat products (34.80 %), 42 samples of cheese brands (37.50 %), 25 samples of salads (22.30 %) and eight samples of halva (7.10 %). According to the results of the antibiotic resistance test, the Enterococci isolates obtained were determined to show resistance to at least 4 of the antibiotics used in the study. While no gelatinase activity was observed in any of the isolates, haemolysin activity was observed to be positive in 36 of them (31.60 %). As a result, having been regarded for years as harmless and reported likely to be used as a starter culture, some Enterococcus spp. pose a risk to public health and to food safety since they have virulence factors and strong antimicrobial resistance. For this reason, the Enterococcus spp. to be used as a starter in the food industry should be chosen from among those that don’t have pathogenicity and antibiotic resistance genes