The deep learning literature is presented in this publication. in the classification of respiratory diseases, focusing on pneumonia, COVID-19, and tuberculosis. The review explores the potential of various Complex pat...
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The zero padding (ZP) variants of orthogonal frequency-division multiplexing (OFDM) exhibit a lower bit error rate (BER) and higher energy efficiency compared to their cyclic prefix (CP) counterparts. However, the emp...
The zero padding (ZP) variants of orthogonal frequency-division multiplexing (OFDM) exhibit a lower bit error rate (BER) and higher energy efficiency compared to their cyclic prefix (CP) counterparts. However, the employment of ZP-OFDM demands strict time synchronization, which is challenging in the absence of pilots or CP. Moreover, time synchronization in OFDM systems is even more challenging when impulsive noise is present. It is well known that urban noise, which consists largely of impulsive noise generated by spark plugs used in internal combustion engines, switching and industrial activities, and discharge of high voltage distribution lines, has a strong influence on digital mobile communications. In this paper, we propose a new low-complexity approximate maximum likelihood (A-ML) timing offset (TO) estimator for ZP multiple-input multiple-output (MIMO)-OFDM in impulsive-noise environments. Performance comparison of the A-ML estimator with existing TO estimators demonstrates a superior performance in terms of lock-in probability with similar computational complexity. Also, compared to the optimal ML TO estimator, it offers a significantly lower computational complexity with negligible performance loss. The A-ML estimator can be employed for both frame and symbol synchronization.
Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigation of the use of machine learning (ML) methods for precise classificat...
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The Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently develope...
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Electric vehicles are rapidly gaining popularity as a sustainable alternative to conventional gasoline. In urban areas, chargers with different ratings can accommodate the diverse needs of electric vehicles. However, ...
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Backdoor attacks present a substantial security concern for deep learning models, especially those utilized in applications critical to safety and security. These attacks manipulate model behavior by embedding a hidde...
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We consider the following question of bounded simultaneous messages (BSM) protocols: Can computationally unbounded Alice and Bob evaluate a function f(x, y) of their inputs by sending polynomial-size messages to a com...
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Real-time identification of unmanned aerial vehicle (drone) is a relatively growing nascent research area which leverages on deep learning and computer vision methods. However, the question arises as to possible dange...
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ISBN:
(数字)9798350387490
ISBN:
(纸本)9798350387506
Real-time identification of unmanned aerial vehicle (drone) is a relatively growing nascent research area which leverages on deep learning and computer vision methods. However, the question arises as to possible dangers, and misuse of drones in different circumstances. These concerns are in respect to privacy, safety and security possible violations. Cameras and software are for example bundled in detection systems where visual information is used to facilitate the detection process. Thus, This Study was devoted to examining the object detection feature of the YOLO Only Look Once (YOLOv8) algorithm and its applicability for analyzing visual material captured by drones. One of the challenges in reviewing literature was to look for an online dataset of small drones and make it publicly available. Therefore, a real-world dataset was established accurately in this study and it includes small drones. The outcome presented in the document is expected to help understand the capability of the chosen models when one attempts to recognize drones in complex conditions. Further this shall serve as base on enhancing the development of even better and long-lasting anti-drone detection systems. The mentioned issues were addressed and solved with the help of the YOLOv8 architecture implementation, and the outstanding results were obtained: the mean average precision (mAP) of $\mathbf{9 3. 9 \%}$ , the precision of $\mathbf{9 2. 9 \%}$ , and the recall of $\mathbf{9 0. 3 \%}$ .
Ti-6Al-4 V is an (α + β) titanium alloy that has been most widely used in automotive, aerospace, and biomedical applications due to the extensive material properties of high strength, toughness, high strength-t...
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A study has been conducted to investigate the sensing performance of zinc oxide (ZnO) nanorods coated glass substrate using the side coupling approach for lactose sensing application. The concentration of lactose solu...
A study has been conducted to investigate the sensing performance of zinc oxide (ZnO) nanorods coated glass substrate using the side coupling approach for lactose sensing application. The concentration of lactose solution was varied from 0% to 100% in order to examine the performance of the proposed sensor, which is influenced by the scattering and absorption of light. Coated glass substrates with optical side coupling could solve the lower output coupling voltage existed in the recent approach. Experimental results show that the proposed approach improved by a factor of 2.5 as compared to the uncoated glass substrate when exposed to variation concentration of lactose solution. The average sensitivity of the sensor was observed to be 0.0201 V/%Concentration throughout the tested %Concentration levels. The utilization of affordable and uncomplicated sensors allows for the precise identification of alterations in the refractive index solution, hence presenting potential applications in the domains of environmental and optical sensing.
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