Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classification. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the final phase, the best features are subjected to various classifiers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifier, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept.
The sustainability of the power systems assures consumers to have efficient and cost-effective energy consumption. Consumers' energy management is one of the solutions that in fact boosts the power system stability via efficiently scheduling the appliances. In addition to energy management, consumers fulfill their low-cost energy consumption using decentralized energy generation (such as solar, wind, plugin hybrid electric vehicles, and small diesel generator). This decentralized energy generation and its trading among the prosumers and consumers help in the distribution grid stability and continuous supply. In this paper, the joint energy management and energy trading model is presented, which provides low-cost electricity consumption to the distribution system. The proposed framework is a twofold system. In the first fold, the distribution system is divided into a number of microgrids, where each microgrid electricity demand is managed using a unified energy management approach. While the local energy produced is traded among the microgrids in the second fold, through energy trading concepts that fulfill the consumers' demand without stressing the utility company. The results indicate that the proposed model reduced the electricity cost of the microgrids with maximum share of self-generation. Moreover, the results also indicate that each microgrid either fulfills its electricity demand from self-generation or purchases it from the nearby microgrid. INDEX TERMS Smartgrid, Unified demand side management, Peak to average power ratio, Consumer comfort level.. Nomenclature β 1 , β 2 Set of appliances having various priorities, e.g., β 1 ∈ {Washing machine, dish washer} and β 2 ∈{Dryer, sterilizer}, etc. γ t Peak clipping maximum limit. λ t,n v,a Consumer preference factor A n Set of appliances of consumer's n.
This paper seeks to establish under what conditions (mobility, network size, wireless channel) multi-source video streaming is feasible across a wireless Vehicular Ad Hoc Network (VANET). Overlay networks with multiple sources have proven to be robust, distributed solutions to multimedia transport, including streaming. To achieve video streaming over a VANET overlay, this paper introduces a spatial partition of a video stream based on Flexible Macroblock Ordering. Tests show this can achieve a gain of over 5 dB in video quality (PSNR) depending on video content and packet loss rates. However, routing of streamed services over multiple hops and multiple paths may lead to significant packet losses, resulting in unacceptable quality of service. The paper examines the impact of differing traffic densities and road layouts upon an overlay network's performance. The work modeled the emerging IEEE 802.11p for wireless VANETs. The research demonstrates that the vehicles' mobility pattern and their drivers' behaviour need to be carefully modeled to determine signal reception. The paper also considers the impact of the wireless channel, which also should be more realistically modeled.
Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the un-monitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. Am-Drs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in * sever atmospheric conditions. In this paper, we propose an efficient un-supervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM)and K Nearest Neighbor (KNN)to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM.
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