Current Issue : October-December Volume : 2024 Issue Number : 4 Articles : 5 Articles
Digital Signal Processing (DSP) finds a wide range of applications in various fields, including telecommunications, audio and video processing, biomedical engineering, radar systems, and image processing. Previous DSP designs faced limitations in available processing power and computational resources. Insufficient processing power could result in slower execution times, an inability to handle complex algorithms, or limited capacity to process high-speed or large-scale signals. As the demand for minimal power consumption in DSP circuits continues to grow, reversible logic and quantum-dot cellular automata (QCA) have emerged as promising technologies due to their inherent ability to reduce energy loss. Within this landscape, the arithmetic and logic unit (ALU) plays a vital role in complex circuitry, serving as a key component in digital signal processing applications. However, challenges persist, including high quantum cost and the need to limit the number of cells in the ALU design. To address these challenges, our research aims to develop an efficient ALU by integrating reversible logic and QCA technology. Our focus will be on generating essential components, such as Feynman gates, Fredkin gates, and full adder circuits, which serve as foundational building blocks for reversible logic and QCA designs. These components will be combined to construct a comprehensive ALU capable of performing 20 different operations. Our implementation efforts will be centered around QCADesigner, with a specific emphasis on digital signal processing systems that prioritize energy efficiency and optimal utilization of occupied areas....
Despite numerous researches worldwide, feedback issue in hearing aids remains a challenge requiring further improvement. Existing methods employed to reduce feedback can at times be limited in effectiveness, giving rise to undesired aftermaths. Consequently, there is an apparent demand for more efficient and effective solutions to addressing feedback problems in hearing aids. This research was therefore centred on developing a functional signal processing algorithm using the Spectral Subtraction Technique, SST. In this research, noise samples were collected from four different sources, including a Hospital in South-Western Nigeria, so that simulations and analyses were conducted for performance evaluation of selected scenarios of noise types and audio recordings, using SST. The simulations were implemented using a Python-based approach, aided by the power of digital signal processing algorithms. Results from the simulation revealed the effectiveness of SST in background noise reduction, with improved signal–to–noise ratio (SNR) in the different scenarios, including speech recordings with background chatter, calm pop songs with street traffic noise and public speeches with air conditioning noise. In conclusion, the SST offers a practical approach to noise reduction in audio signals. While the code offers users an effective tool for reducing noise in audio recordings and enhancing audio quality, its simplicity and clarity make it accessible to users with varying expertise in audio signal processing....
The LSPR biosensor chip is a groundbreaking tool popular in laboratory settings for identifying disease markers. However, its use in clinical environments is not as widespread. One notable gap is the lack of a universal signal processing tool for LSPR biosensing. To escalate its precision, there is an emerging need for software that not only optimizes signal processing but also incorporates self-verification functionalities within LSPR biochemical sensors. Enter the visual LSPR sensor software—an innovative platform that processes real-time transmission or reflection spectra. This advanced software adeptly captures the nuanced structural changes at the nanostructure interface prompted by environmental fluctuations. It diligently records and computes a suite of parameters, including the resonance wavelength shift, full width at half maximum, sensitivity, and quality factor. These features empower users to tailor processing algorithms for each data capture session. Transcending traditional instruments, this method accommodates a multitude of parameters and ensures robust result validation while tactfully navigating nanostructure morphology complexities. Forsaking third-party tool dependencies, the software tackles challenges of precision and cost-effectiveness head-on, heralding a significant leap forward in nanophotonics, especially for highthroughput LSPR biosensing applications. This user-centric innovation marks substantial progress in biochemical detection. It is designed to serve both researchers and practitioners in the field of nanophotonic sensing technology, simplifying complexity while enhancing reliability and efficiency....
Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using the signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring....
A vehicle-mounted solar occultation flux–Fourier transform infrared spectrometer uses the sun as an infrared light source to quantify molecular absorption in the atmosphere. It can be used for the rapid three-dimensional monitoring of pollutant emissions and the column concentration monitoring of greenhouse gases. The system has the advantages of high mobility and a capacity for noncontact measurement and measurement over long distances. However, in vehicle-mounted applications, vehicle bumps and obstacles introduce aberrations in the measured spectra, affecting the accuracy of gas concentration inversion results and flux calculations. In this paper, we propose a spectral data preprocessing method that combines a self-organizing mapping neural network and correlation analysis to reject anomalous spectral data measured by the solar occultation flux–Fourier transform infrared spectrometer during mobile observations. Compared to the traditional method, this method does not need to adjust the comparison threshold and obtain the training spectra in advance and has the advantage of automatically updating the weights without the need to set fixed correlation comparison coefficients. The accurate identification of all anomalous simulated spectra in the simulation experiments proved the effectiveness of the method. In the vehicle-mounted application experiment, 342 anomalous spectra were successfully screened from 1739 spectral data points. The experimental results show that the method can improve the accuracy of gas concentration measurement results and can be applied to a vehicle-mounted solar occultation flux–Fourier transform infrared spectrometer system to meet the preprocessing needs of a high number of spectral data in mobile monitoring....
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