7 research outputs found
Guidance Navigation and Control for a Mobile Robot using Vision System and Adaptive Particle Filter
No full textThe main goal of this thesis is to present a system for determining a mobile robot’s pose estimation and mapping in unknown environments. This system has the ability to guide, navigate and control the robot based on a vision system. The system utilizes sensor fusion to enhance the accuracy and efficiency of state estimation. The mobile robot employs a stereo camera and to increase the precision and accuracy of the state estimate, a new Adaptive Fuzzy Particle Filter is implemented based on the fuzzy logic which commonly occur in the traditional Particle Filter and prevented them from diverging. The proposal investigated the mobile robot’s characteristics parameters in an unknown environment with varying amounts of particles. The results showed that the sensor noise can be reduced effectively. The mobile robots using PID controlling was introduced. Experimental validation substantiated the resilience and effectiveness of the proposed controller
Multi-Channel Sequential Sensing In Cognitive Radio Networks
No full textAbstract
Multi-Channel Sequential Sensing In Cognitive Radio Networks
Finding white spaces and using them are major goals of cognitive radio networks. In this research work, we investigate multi-channel spectrum sensing for secondary users (SUs), and make improvements by forming sequential sensing as long as the secondary user does not get a channel to transmit on, and also as long as the user still has time left for transmission since waiting for the next cycle might not be the best scenario for the use of
spectrum radio. We first formulate an optimization problem that maximizes the throughput of the system. Then, we introduce a power consumption model for our system since SUs are battery powered devices and the effectiveness of the system is jointly coupled with the energy consumption. Finally, we introduce an energy utility function, and we optimize it by considering both the throughput of the system and the amount of power consumed to
achieve the optimal throughput. Numerical and simulation results are introduced at the end of this research, and they show better performance by the use of our suggested model compared to the work i the literature. The results also showed how to find the optimal number of channels to be sensed considering an efficient use of the SU’s battery
Design WAP-Based to Web-Based Patient Emergency Service System for Pusat Kesihatan Universiti (PKU)
No full textWith Design WAP-based to Web-based Patient Emergency Service System for Pusat Kesihatan Universiti (PKU) by using the wireless technology can be obtained easily way and flexibility to access the information at any time in any location. This research introduces a Web-WAP application that provides the Doctors of Pusat Kesihatan Universiti (PKU) by provide them with the patient information accessing anytime and anywhere to check the patient status, that save the time to check the patient status from different places. By using this technology, Doctors can easily get necessary information about their patient
Optymalne szacowanie stanu za pomocą adaptacyjnego filtra cząstek rozmytych
No full textParticle Filters (PF) accomplish nonlinear system estimation and have received high interest from numerous engineering domains over the past decade. The main problem of PF is to degenerate over time due to the loss of particle diversity. One of the essential causes of losing particle diversity is sample impoverishment (most of particle’s weights are insignificant) which affects the result from the particle depletion in the resampling stage and unsuitable prior information of process and measurement noise. To address this problem, a new Adaptive Fuzzy Particle Filter (AFPF) is used to improve the precision and efficiency of the state estimation results. The error in AFPF state is avoided from diverging by using Fuzzy logic. This method is called tuning weighting factor (α) as output membership function of fuzzy logic and input memberships function is the mean and the covariance of residual error. When the motion model is noisier than measurement, the performance of the proposed method (AFPF) is compared with the standard method (PF) at various particles number. The performance of the proposed method can be compared by keeping the noise level acceptable and convergence of the particle will be measured by the standard deviation. The simulation experiment findings are discussed and evaluated.Adaptacyjny filtr cząstek rozmytych (AFPF) służy do poprawy precyzji i wydajności wyników szacowania stanu. Metoda ta nazywana jest dostrajaniem współczynnika ważenia (α), ponieważ wyjściowa funkcja przynależności logiki rozmytej, a wejściowa funkcja przynależności jest średnią i kowariancją błędu resztowego. Wydajność proponowanej metody jest porównywana przez utrzymanie dopuszczalnego poziomu hałasu, a zbieżność cząstki będzie mierzona przez odchylenie standardowe. Wyniki eksperymentu symulacyjnego są omawiane i oceniane
CLASSIFICATION OF SKIN CANCER BASED ON DEEP LEARNING USING CONVOLUTIONAL NEURAL NETWORKS – OPPORTUNITIES AND VULNERABILITIES A SYSTEMATIC REVIEW
No full textConvolutional Neural Networks (CNNs) have outperformed dermatologists in the classification of skin lesions related to skin cancer, potentially saving lives through earlier diagnosis. By just installing an app on their mobile devices, people will be able to self-diagnose their cancer. By the end of 2021[28], 6.3 billion people are expected to have used the subscriptions to diagnose themselves with skin cancer. This study shows its findings after reviewing a large number of research articles on CNN-based skin lesion classification. Thanks to recent advances in machine learning algorithms, the rate at which skin lesions are erroneously identified has decreased as compared to dermatologist categorisation. This study looks at the approaches that have been taken, the effectiveness of those approaches, and the development of CNN in the successful classification of skin cancer subtypes. While deep learning with CNN gives advantages over a dermatologist, it also has certain disadvantages when misclassifying photos depending on symptoms and criteria. We also address these weaknesses in this overview research. We searched the Science Direct, PubMed, Elsevier, Web of Science, and Google Scholar databases for published original research publications. From the web publications we looked for, we selected articles with sufficient data and information about the authors\u27 study and created an overview of the authors\u27 approaches and methodologies. There is currently a lack of review literature addressing the merits and drawbacks of applying deep learning to the classification of skin cancer. Advances in deep learning and machine learning technology can eliminate human error and prevent errors and classifications. Along with their limitations, we will also discuss the benefits of using CNNs for deep learning
Maternal and neonatal outcomes after caesarean delivery in the African Surgical Outcomes Study: a 7-day prospective observational cohort study
No full textSummary: Background: Maternal and neonatal mortality is high in Africa, but few large, prospective studies have been done to investigate the risk factors associated with these poor maternal and neonatal outcomes. Methods: A 7-day, international, prospective, observational cohort study was done in patients having caesarean delivery in 183 hospitals across 22 countries in Africa. The inclusion criteria were all consecutive patients (aged ≥18 years) admitted to participating centres having elective and non-elective caesarean delivery during the 7-day study cohort period. To ensure a representative sample, each hospital had to provide data for 90% of the eligible patients during the recruitment week. The primary outcome was in-hospital maternal mortality and complications, which were assessed by local investigators. The study was registered on the South African National Health Research Database, number KZ_2015RP7_22, and on ClinicalTrials.gov, number NCT03044899. Findings: Between February, 2016, and May, 2016, 3792 patients were recruited from hospitals across Africa. 3685 were included in the postoperative complications analysis (107 missing data) and 3684 were included in the maternal mortality analysis (108 missing data). These hospitals had a combined number of specialist surgeons, obstetricians, and anaesthetists totalling 0·7 per 100 000 population (IQR 0·2–2·0). Maternal mortality was 20 (0·5%) of 3684 patients (95% CI 0·3–0·8). Complications occurred in 633 (17·4%) of 3636 mothers (16·2–18·6), which were predominantly severe intraoperative and postoperative bleeding (136 [3·8%] of 3612 mothers). Maternal mortality was independently associated with a preoperative presentation of placenta praevia, placental abruption, ruptured uterus, antepartum haemorrhage (odds ratio 4·47 [95% CI 1·46–13·65]), and perioperative severe obstetric haemorrhage (5·87 [1·99–17·34]) or anaesthesia complications (11·47 (1·20–109·20]). Neonatal mortality was 153 (4·4%) of 3506 infants (95% CI 3·7–5·0). Interpretation: Maternal mortality after caesarean delivery in Africa is 50 times higher than that of high-income countries and is driven by peripartum haemorrhage and anaesthesia complications. Neonatal mortality is double the global average. Early identification and appropriate management of mothers at risk of peripartum haemorrhage might improve maternal and neonatal outcomes in Africa. Funding: Medical Research Council of South Africa
