Current Issue : July-September Volume : 2024 Issue Number : 3 Articles : 5 Articles
Digital signal processing (DSP) has been widely adopted in sensor systems, communication systems, digital image processing, artificial intelligence, and Internet of Things applications. However, these applications require circuits for complex arithmetic computation. The logarithmic number system is a method to reduce the implementation area and transmission delay for arithmetic computation in DSP. In this study, we propose antilogarithmic converters with efficient error–area–delay products (eADPs) based on the fractional-bit compensation scheme. We propose three mathematical approximations—case 1, case 2, and case 3—to approximate the accurate antilogarithmic curve with different DSP requirements. The maximum percentage errors of conversion for case 1, case 2, and case 3 are 1.9089%, 1.7330%, and 1.2063%, respectively. Case 1, case 2, and case 3 can achieve eADP savings of 15.66%, 80.80%, and 84.61% compared with other methods reported in the literature. The proposed eADP-efficient antilogarithmic converters can achieve lower eADP and digitalized circuit implementation. The hardware implementation utilizes Verilog Hardware Description Language and the digital circuits are created via very-large-scale integration by the Taiwan Semiconductor Manufacturing Company with 0.18 μm CMOS technology. This proposed antilogarithmic converter can be efficiently applied in DSP....
Nuclear power plants (NPPs) are globally important sources of clean energy. However, high operation and maintenance (O&M) costs are one of the factors hindering the development of NPPs. In this study, we focused on the development of a digital algorithm for ex-core instrumentation systems (EISs) to reduce O&M costs, given that the digitalization of EISs is not progressing compared with other instrumentation and control systems in NPPs. Specifically, we developed a digital algorithm for pulse signal processing in EISs, which traditionally require a significant amount of hardware and maintenance efforts. Our purposes were to simplify the configuration and reduce the O&M costs associated with pulse signal processing. To validate the algorithm, we used a detector emulator and a research reactor. Additionally, we explored a new approach to reduce the workload associated with a discrimination characteristics test (DCT) in EISs. The results of this study demonstrated that the proposed algorithm is effective in achieving good linearity within ±5% of the span and response performance with a delay time of 5 ms, which are required for EISs. Furthermore, the proposed method for the DCT shows promising results in reducing the time required for the test compared with conventional methods....
In the field of direction of arrival (DOA) estimation for coherent sources, subspace-based model-driven methods exhibit increased computational complexity due to the requirement for eigenvalue decomposition. In this paper, we propose a new neural network, i.e., the signal space deep convolution (SSDC) network, which employs the signal space covariance matrix as the input and performs independent two-dimensional convolution operations on the symmetric real and imaginary parts of the input signal space covariance matrix. The proposed SSDC network is designed to address the challenging task of DOA estimation for coherent sources. Furthermore, we leverage the spatial sparsity of the output from the proposed SSDC network to conduct a spectral peak search for obtaining the associated DOAs. Simulations demonstrate that, compared to existing state-of-the-art deep learning-based DOA estimation methods for coherent sources, the proposed SSDC network achieves excellent results in both matching and mismatching scenarios between the training and test sets....
Frequency estimation is often the basis of various measurement techniques, among which optical distance measurement stands out. One of the most used techniques is interpolated fast Fourier transform due to its simplicity, combined with good performance. In this work, we study the limits of this technique in the case of real signals, with reference to a particular interferometric technique known as self-mixing interferometry. The aim of this research is the better understanding of frequency estimation performances in real applications, together with guidance on how to improve them in specific optical measurement techniques. An optical rangefinder, based on self-mixing interferometry, has been realized and characterized. The simulation results allow us to explain the limits of the interpolated fast Fourier transform applied to the realized instrument. Finally, a method for overcoming them is proposed by decorrelating the errors between the measurements, which can provide a guideline for the design of frequency-modulated interferometric distance meters....
Face milling is among the processes that can produce a high-precision surface finish. Tool condition monitoring and signal processing algorithms are under extensive research to improve production quality and productivity in machining processes. In the present research, the time– frequency analysis technique was applied to the signal obtained from a sensor integrated into the primary AC power circuitry during the milling of steel bars to evaluate its applicability in detecting the current variations associated with the cutting force. The signal acquired from the sensor was processed in the time–frequency domain using wavelet analysis, and the results were compared with the traditional time and frequency analyses. The results showed that the signal variations produced by the cutting force were well localized in the frequency spectra with both approaches. However, the wavelet processing method yielded a poorly defined cutting force signal shape due to the limited resolution inherent in the sub-bands containing the frequencies of interest....
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