It can be stated that sigma-delta [3] analog-to-digital adapter is a most common approach of over-sampling analog-to-digital adapter. The map processor of a sigma-delta analog-to-digital adapter is displayed in Fig. 1 [4].The sigma-delta analog-to-digital adapter can be divided into two lumps, the quantizing and the decimating parts. Essentially, decimation is the act of decreasing the data rate down from the over-sampling rate without losing information. The quantizing part contains the analog integrator, the 1-bit analog-to-digital adapter, and the 1-bit digital-to-analog adapter [5]. The task of the quantizing part is to adapt the data in the analog input into digital shape. The input-output relationship of the sigma-delta quantizer is
A B S T R A C TThe received analog signal must be digitized before the digital signal processing can demodulate it. Sampling, quantization, and coding are the separate stages for the analog-to-digital adaptation procedure. The procedure of adapting an unceasing time-domain signal into a separate time-domain signal is called sampling. While, the procedure of adapting a separatetime, continuous-valued signal into a discrete-time, discrete-valued signal is known as quantization. Thus, quantization error is the mismatch between the unquantized sample and the quantized sample. The method of demonstrating the quantized samples in binary form is known as coding. This investigation utilized Matlab® program to recommend a proper scheme for a wireless-call button network of input signal, normalized frequency, and over-sampling ratio against signalto-quantization noise ratio. Two vital characteristics of this wireless network design are cost-effective and low-power utilization. This investigation, through reducing the in-band quantization error, also studied how oversampling can enhance the accomplishment of an analog-to-digital adapter.
This study highlights the role of imaging modalities through prostate image segmentation, using various algorithms depending on segmentation accuracy, and (VIoT) impact on improving imaging, via analyzing relevant articles to prostate segmentation during 2018–2021, from Springer, Science Direct, MDPI, IEEE, Nature Portfolio, Hindawi, with Taylor and Francis pre and during COVID-19. This study deals with 20 articles. Findings illustrated MRI was involved in (90%) of the articles in pre-COVID-19, while during COVID-19 declined to (60%). Furthermore, CNN algorithm was the most dependent method for prostate segmentation which was (50%) of the articles rather than other models. Whereas (80%) of the articles were depended on (DSC). In conclusion, the (VIoT) shows a significant role in all imaging modalities specifically MRI due to the real-time imaging. COVID-19 had impact on prostate segmentation research with the publishers was considerable in pre and during the pandemic. In addition, the best-utilized imaging modality was MRI due to its high image quality and ease applicable for (VIoT). Nevertheless, there is no study considered transabdominal ultrasound database as imaging modality for prostate segmentation. On the other hand, the segmentation performance referred to (DSC) that has a significant influence on prostate image segmentation Quality and performance.
A B S T R A C TNanopores, which are nanometer-sized holes, have been utilized in apparatus that point toward sensing a range of molecules such as DNA and RNA and single proteins The important factor for sensing molecules is diameters of nanopores which can be found through a substantial process called segmenting for nanopores of scanning electron microscope (SEM) images. In this investigation, four segmentation methods, namely, threshold, bilateral filter, k-means, and expectation maximizationGaussian mixture model (EM-GMM) which has been utilized to segment three SEM images of nanopores efficiently. The quality of segmentation evaluated objectively through computing Rand index among them. Consequently, the nanopore size of Al 2 O 3 films computed by means of SEM images. This study found that EM-GMM segmenting method gives promising results among other examined methods. It is for their high R-index, minimum adjustment parameters (just one variable which set usually 2), and low consuming time. Hence, it can be used efficiently for computing nanopore count and size.
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