Synchrophasor measurement plays a critical role in improving the efficiency, reliability and security of distribution grids. The use of synchrophasors allows for advanced monitoring, control and protection of the dist...
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Synchrophasor measurement plays a critical role in improving the efficiency, reliability and security of distribution grids. The use of synchrophasors allows for advanced monitoring, control and protection of the distribution grid, providing valuable information for decision-making in real-time. In this paper, we present a simple framework based on open-source codes that can convert a typical laboratory oscilloscope to a synchrophasors logger with minimum investment and use devices commonly available in electrical engineering laboratories. We use simple algorithms to extract electrical quantities of interest and log them to a highly efficient time-series database. Post-processing and data analysis can be done for the active and reactive power of each individual harmonic and can be done by accessing the database remotely from any location connected to the internet.
Collaborative Beamforming (CB) which is a cooperative transmission technique where nodes act as a virtual antenna array can achieve an order of magnitude in improved signal strength, directional signal transmission, e...
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ISBN:
(数字)9798350353501
ISBN:
(纸本)9798350353518
Collaborative Beamforming (CB) which is a cooperative transmission technique where nodes act as a virtual antenna array can achieve an order of magnitude in improved signal strength, directional signal transmission, energy efficiency and node redundancy when practically deployed. However, signals arrive out of phase at the target receiver due to unsynchronized clock frequencies of local oscillators (LOs). In this article, an iterative procedure synchronizes the common message signal from multiple transmitters to the receiver. LO frequency synchronization and random phase adjustments are carried out by CB nodes at each time slot to increase the signal to noise ratio (SNR). Mean square error (MSE) of the frequency offset of the feedback signal are estimated with the developed LO estimation algorithm. This algorithm is seen to have tracked the modified Cramer-Rao lower bound (MCRLB) more closely for SNR between 7 dB and 20 dB. An improvement of 99.57% and 93.74% is recorded for LO frequency offset MSE for CB node 1 and CB node 2, respectively, when compared to another estimator. When beamforming is enabled, the percentage increase in amplitude between single and combine node with convergence achieved is 66.67%.
Alzheimer's disease is a brain ailment that impairs thinking, memory, and behaviour. The efficacy of Brain- Computer Interface (BCI) systems must be enhanced to increase their prevalence in the biomedical sector. ...
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ISBN:
(数字)9798350356236
ISBN:
(纸本)9798350356243
Alzheimer's disease is a brain ailment that impairs thinking, memory, and behaviour. The efficacy of Brain- Computer Interface (BCI) systems must be enhanced to increase their prevalence in the biomedical sector. The biomedical sector is crucial for precise and efficient patient diagnostics using digital signalprocessing and wireless sensor network technologies. Alzheimer's disease (AD) is an irreversible brain disorder. Inadequate Alzheimer's disease diagnosis in the early stages, together with exact identification, may aid in halting the progression of the illness. Precise and effective detection and categorization of Alzheimer's disease enhance quality of life and increase life expectancy for patients. The advanced deep learning technology surpasses traditional machine learning methods in classifying complex configurations in high-dimensional composite data, particularly in digital signalprocessing. The deep learning application for the early detection and accurate classification of Alzheimer's disease might be suggested for a neuron-signal methodology in extensive data analysis. This research paper proposes an intelligent decision-making system (IDMS) for the diagnosis and categorization of Alzheimer's disease (AD) and the continuous monitoring of patients utilizing connected wearable devices. Methods for measuring neuronal synchrony are proposed to diagnose and characterize Alzheimer's disease.
While the deployment of 5G mobile networks is still in its early stage, we are currently experiencing a paradigm shift towards Open Radio Access Network (RAN) architectures. In this context, RAN solutions that are hea...
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ISBN:
(纸本)9781665445054
While the deployment of 5G mobile networks is still in its early stage, we are currently experiencing a paradigm shift towards Open Radio Access Network (RAN) architectures. In this context, RAN solutions that are heavily designed on software can reduce the implementation cost and time-to-market, as well as increase flexibility by making such solutions 'future-ready' through enabling new features, such as new advancedsignalprocessingalgorithms, to be included even via a simple software update. Realization of software-based RANs enabled by this paradigm shift, although attractive is however non-trivial, especially the realization of physical layer which mandates fast and efficient processing to meet strict real-time requirements. In this direction, we propose in this work a specialized acceleration software solution (SACCESS) to accelerate computationally expensive physical layer processing operations. We show that SACCESS can provide a slot processing speed-up of over 2.2, compared to OpenAirInterface (OAI) which emerged as one of the most advanced software-based 5G-NR RAN solutions available to a wider community. Our results demonstrate, for first time, that a peak throughput for a 40MHz TDD and FDD single antenna 5G-NR system based on OAI's development can be achieved without any hardware acceleration.
Significant safety dangers are posed by gas leaks in commercial, industrial, and residential environments. In order to improve safety and dependability in the detection and response to gas leaks, this study proposes a...
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ISBN:
(数字)9798331506452
ISBN:
(纸本)9798331506469
Significant safety dangers are posed by gas leaks in commercial, industrial, and residential environments. In order to improve safety and dependability in the detection and response to gas leaks, this study proposes an Arduino-based gas leak detector and monitoring system. By utilizing carefully placed sensors for early detection and precise identification of gases like methane, propane, and carbon monoxide, the system minimizes false alarms by fusing cutting-edge sensor technology with clever algorithms. At the core are Arduino microcontrollers processing data from gas sensors. Communication modules enable robust remote monitoring and alert notifications through smartphones. The system triggers visual and auditory alarms when gas concentrations exceed thresholds, facilitating prompt response. The system's design incorporates user-friendly interfaces, low maintenance requirements, and cost-effective implementation. With a reliable power source and battery backup, it offers an ad- vanced, accessible solution for gas leak detection and monitoring, enhancing overall safety and mitigating risks across residential, commercial, and industrial settings. Key features include Arduino microcontrollers interfaced with gas sensors like MQ-2, MQ-3, or MQ-7, communication modules (Wi-Fi, Bluetooth, GSM), alarm systems (LEDs, buzzers), and reliable power supply. The system's architecture ensures seamless integration and functionality.
Crop pests pose a great threat to global food security; thus, the best pest prevention measures must be implemented. By using different machine learning (ML) techniques to perform crop pest classification, this resear...
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ISBN:
(数字)9798350350654
ISBN:
(纸本)9798350350661
Crop pests pose a great threat to global food security; thus, the best pest prevention measures must be implemented. By using different machine learning (ML) techniques to perform crop pest classification, this research provides ways to improve the accuracy and speed of identifying pests in agricultural sectors. Conventional methods for identifying pests frequently depend on manual observation, which is tedious, error-prone, and labor-intensive. On the other hand, machine learning (ML) presents an effective way to automate this procedure by using sophisticated techniques to analyze massive data sets and produce precise predictions. The study applies a variety of machine learning approaches, such as Random Forests, K-Nearest Neighbor, and Naive Bayes, to classify agricultural pests according to features that have been extracted from images. For model training and validation, an extensive collection of high-resolution images of different agricultural pests taken in a range of environmental settings is used. Metrics like accuracy are used to determine how well the machine learning models perform. The potential of machine learning approaches to revolutionize pest management in agriculture is evident from the results, which indicate how accurately they can identify and classify agricultural pests. The suggested method improves the overall effectiveness of pest management procedures and drastically reduces the time and effort required to identify pests. Ultimately, this research promotes more resilient and productive farming systems by supporting efforts to develop sustainable and technologically advanced solutions for addressing agricultural difficulties. The results demonstrate the potential of machine learning (ML) as an invaluable tool for farmers, agronomists, and policymakers, encouraging a proactive and data-driven approach to pest management in contemporary agriculture.
Kathakali, an ancient art form that dates back to the 17th century, is renowned for its intricate hand gestures, dance movements, musical accompaniment, elaborate costumes, and makeup. Mastering such an art form is a ...
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ISBN:
(数字)9798350376135
ISBN:
(纸本)9798350376142
Kathakali, an ancient art form that dates back to the 17th century, is renowned for its intricate hand gestures, dance movements, musical accompaniment, elaborate costumes, and makeup. Mastering such an art form is a challenge even for the most dedicated enthusiasts, given its unconventional methods of expression. However, with current advanced technology, we can use this to aid the learning of unfamiliar subjects by classifying factors to help the new learners. In this paper, we focus on classifying one aspect of the play, which is the expressions on the actor’s face given a picture using computer vision methods. To achieve the final model, we use a combination of Dual Shot Face Detector (DSFD) and Convolution Neural Network (CNN) and finally tuning up the epochs of the training process. With our dataset, the proposed model reached an accuracy of 93% among nine classes. This project hopes to introduce Kathakali to a wider audience and facilitate the learning process of this art form.
This paper focuses on SADino, an aperture array deployed at the Sardinia Radio Telescope site (San Basilio, Sardegna, Italy). SADino is a sub-array of the Sardinia Aperture Array Demonstrator, an Italian aperture arra...
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ISBN:
(数字)9798350360974
ISBN:
(纸本)9798350360981
This paper focuses on SADino, an aperture array deployed at the Sardinia Radio Telescope site (San Basilio, Sardegna, Italy). SADino is a sub-array of the Sardinia Aperture Array Demonstrator, an Italian aperture array that was designed as a demonstrator of Low Frequency Aperture Array systems. The flexibility intrinsic to SADino will offer the opportunity to investigate and experimentally verify different antenna configurations and algorithms for data processing, e.g. new methods of beamforming and procedures of data calibration. In this study, the SADino performance will be investigated in terms of its radiation pattern and polarisation purity. Thanks to the possibility of re-positioning the dual-polarised antennas on the site, two different alignments of the antennas with respect to the cardinal directions have been studied. Results at 270, 345 and 420 MHz will be shown.
A staple of human nourishment, milk can be tainted, compromising consumer health and the dairy industry's reputation. Chemicals, particularly water, are often added to milk to adulterate *** methods for assessing ...
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ISBN:
(数字)9798350353068
ISBN:
(纸本)9798350353075
A staple of human nourishment, milk can be tainted, compromising consumer health and the dairy industry's reputation. Chemicals, particularly water, are often added to milk to adulterate *** methods for assessing milk quality typically involve sensory evaluation, chemical analysis, and microbiological examination may not be effective especially in low concentrations. Milk has a complex chemical composition. Detecting adulterants within this complex matrix requires advanced analytical techniques. This paper explores an innovative approach to tackle the problem of milk adulteration by integrating machine learning with sensor technology. The system is designed for monitoring and predicting milk quality using a combination of data collection through sensors, machine learning techniques, and web deployment. The system integrates hardware components such as Arduino Uno and various sensors (pH, turbidity, temperature) to gather data on milk quality factors. Data was collected after testing eight different milk samples which includes skimmed milk, homogenized milk, standardized milk with varying fat concentrations. The data is processed and analyzed using machine learning algorithms including Support Vector Machine, Adaboost classifier, and Random Forest to predict milk quality.
The most advanced methods available today are convolutional neural networks (CNNs), which are frequently used for image categorization tasks. This article uses sophisticated neural network models to explore the catego...
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ISBN:
(数字)9798331510213
ISBN:
(纸本)9798331510220
The most advanced methods available today are convolutional neural networks (CNNs), which are frequently used for image categorization tasks. This article uses sophisticated neural network models to explore the categorization of peripheral blood smear pictures for B-ALL diagnosis and its subtypes. We introduce a method for classifying images using a modified VGG19 model. Images are pre-processed at first before being input into the multi-class classification algorithms. We discovered throughout this study that the suggested methods improve model performance. Our study focused on two types of images: benign and malignant, as well as three subtypes of malignant lymphoblasts: Early Pre-B, Pre-B, and Pro-B ALL. The model has a validation loss of 0.1499 and an accuracy of 94.63%, whereas its training loss is 0.1127 and 96.97%, respectively. These findings demonstrate how well the VGG19-based model performs in terms of categorization.
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