Enhancing FastSLAM 2.0 performance using a DE Algorithm with Multi-mutation Strategies

FastSLAM 2.0 is considered one of the popular approaches that utilizes a Rao-Blackwellized particle filter for solving simultaneous localization and mapping (SLAM) problems. It is computationally efficient, robust and can be used to handle large and complex environments. However, the conventional FastSLAM 2.0 algorithm is known to degenerate over time in terms of accuracy because of the particle depletion problem that arises in the resampling phase. In this work, we introduce an enhanced variant of the FastSLAM 2.0 algorithm based on an enhanced differential evolution (DE) algorithm with multi-mutation strategies to improve its performance and reduce the effect of the particle depletion problem. The Enhanced DE algorithm is used to optimize the particle weights and conserve diversity among particles. A comparison has been made with other two common algorithms to evaluate the performance of the proposed algorithm in estimating the robot and landmarks positions for a SLAM problem. Results are accomplished in terms of accuracy represented by the positioning errors of robot and landmark positions as well as their root mean square errors. All results show that the proposed algorithm is more accurate than the other compared algorithms in estimating the robot and landmark positions for all the considered cases. It can reduce the effect of the particle depletion problem and improve the performance of the FastSLAM 2.0 algorithm in solving SLAM problem

Modelling Of Reactive Distillation For The Production Of Methyl Tert-Butyl Ether (MTBE) Parametric Sensitivity Study On Kinetic Model

Modelling of reactive distillation for the production of MTBE has been presented in this thesis. A reactive distillation column modelled by using RADFRAC module in the Aspen Plus V10 software for the production of MTBE. The simulation was done on an equilibrium basis. Prior to running the simulation, all the necessary data were collected. The kinetic data which is the coefficients of the equilibrium equation were collected from the equilibrium equation. The values obtained were 357.094, -1492.77, -77.4002 and 0.507563. These values were entered into the Aspen Plus V10 built-in Keq expression. The simulated model was verified by comparing to the published data. Once it was verified, the simulation was then used to carry out parametric sensitivity study on kinetic model. The effect of changes in the kinetic data and four different operating conditions of choice such as the feed flowrate of methanol, the feed flowrate of mixed butenes, the reflux ratio and the composition of isobutylene on the simulation results in terms of MTBE purity and isobutylene conversion were studied in detail. The individual best values for each operating conditions were determined. Then optimization carried out. The optimized values were 209.3 mol/s for methanol feed flowrate, 583.2 mol/s for mixed butenes feed flowrate, 7 for reflux ratio and 0.357 for isobutylene mole fraction. From these set of values, a MTBE purity and isobutylene conversion of 100.00 % obtained successfully. This study shows that the changes in parameters influences the performance of reactive distillation process for the production of MTBE

Slice Admission control based on Reinforcement Learning for 5G Networks

Network slicing empowers service providers to deploy diverse network slice architectures within a shared physical infrastructure. This technology enables the provision of differentiated services that cater for specific Quality of Service (QoS) requirements of different use cases which need to be adequately supported in 5G networks. By leveraging Network Slicing, operators can effectively meet these diverse requirements and provide customized services to different tenants in a flexible and efficient manner. However, infrastructure providers face a challenging dilemma of the slice admission control regarding whether to accept or reject slice requests. From one perspective, they strive to optimize the utilization of network resources through accepting a significant number of network slices. From another perspective, the availability of network resources is restricted, and it is crucial to fulfil the QoS requirements specified by the network slices. In this research, an Admission Control (AC) Algorithm founded upon Reinforcement Learning mechanisms, specifically Q-Learning (QL), Double Q-Learning (Double-QL), and a proposed mechanism based on Double QL is obtained to overcome this challenge. This algorithm is applied in order to make informed decisions regarding network slice requests. The simulation results demonstrate that the AC algorithm, leveraging the suggested mechanism, surpasses the Double-QL and QL mechanisms in relation to gained profit with average of 8% and 26%, respectively. In case of the acceptance ratio of slice requests, it achieves average of 13% and 28% higher than Double-QL and QL mechanisms, respectively. Finally, it obtains the maximum resource utilization, surpassing Double-QL and QL by 9% and 20%, respectively

The burden of sialorrhoea in chronic neurological conditions: current treatment options and the role of incobotulinumtoxinA (Xeomin®).

Sialorrhoea is a frequent symptom of neurological diseases (e.g. Parkinson's disease, motor neuron disease, cerebral palsy, and stroke) and is defined as excessive saliva accumulation leading to unintentional loss of saliva from the mouth. Sialorrhoea increases the overall burden on the patient and their caregivers, the impact of which can be both physical and psychosocial. Treatments for sialorrhoea range from lifestyle and behavioural guidance, to medications, surgery or radiation. Nonpharmacological interventions include advice on posture, swallowing control, cough management, dietary changes, eating and drinking techniques, and behavioural modification; however, these conservative measures may be ineffective for people with progressive neurological conditions. The pharmacological treatment of sialorrhoea is challenging because medications licensed for this purpose are limited, but treatments can include anticholinergic drugs and botulinum toxins. Surgical treatment of sialorrhoea is typically reserved as a last resort for patients. IncobotulinumtoxinA (Xeomin®) is the first botulinum toxin type A to receive US and UK marketing authorization for the symptomatic treatment of chronic sialorrhoea due to neurological disorders in adults. In this review, we discuss and compare the frequency and method of administration, location of treatment delivery, approximate annual costs and main side effects of botulinum toxin and different anticholinergic drugs. Management of patients with chronic neurological conditions requires input from multiple specialist teams and thus a multidisciplinary team (MDT) approach is considered fundamental to ensure that care is consistent and tailored to patients' needs. To ensure that adult patients with neurological conditions receive the best care and sialorrhoea is well managed, we suggest a potential clinical care pathway for sialorrhoea with a MDT approach, which healthcare professionals could aspire to

Slice Admission control based on Reinforcement Learning for 5G Networks

Network slicing empowers service providers to deploy diverse network slice architectures within a shared physical infrastructure. This technology enables the provision of differentiated services that cater for specific Quality of Service (QoS) requirements of different use cases which need to be adequately supported in 5G networks. By leveraging Network Slicing, operators can effectively meet these diverse requirements and provide customized services to different tenants in a flexible and efficient manner. However, infrastructure providers face a challenging dilemma of the slice admission control regarding whether to accept or reject slice requests. From one perspective, they strive to optimize the utilization of network resources through accepting a significant number of network slices. From another perspective, the availability of network resources is restricted, and it is crucial to fulfil the QoS requirements specified by the network slices. In this research, an Admission Control (AC) Algorithm founded upon Reinforcement Learning mechanisms, specifically Q-Learning (QL), Double Q-Learning (Double-QL), and a proposed mechanism based on Double QL is obtained to overcome this challenge. This algorithm is applied in order to make informed decisions regarding network slice requests. The simulation results demonstrate that the AC algorithm, leveraging the suggested mechanism, surpasses the Double-QL and QL mechanisms in relation to gained profit with average of 8% and 26%, respectively. In case of the acceptance ratio of slice requests, it achieves average of 13% and 28% higher than Double-QL and QL mechanisms, respectively. Finally, it obtains the maximum resource utilization, surpassing Double-QL and QL by 9% and 20%, respectively

Towards Extended Bit Tracking for Scalable and Robust RFID Tag Identification Systems

The surge in demand for Internet of Things (IoT) systems and applications has motivated a paradigm shift in the development of viable radio frequency identification technology (RFID)-based solutions for ubiquitous real-Time monitoring and tracking. Bit tracking-based anti-collision algorithms have attracted considerable attention, recently, due to its positive impact on decreasing the identification time. We aim to extend bit tracking to work effectively over erroneous channels and scalable multi RFID readers systems. Towards this objective, we extend the bit tracking technique along two dimensions. First, we introduce and evaluate a type of bit errors that appears only in bit tracking-based anti-collision algorithms called false collided bit error in single reader RFID systems. A false collided bit error occurs when a reader perceives a bit sent by tag as an erroneous bit due to channel imperfection and not because of a physical collision. This phenomenon results in a significant increase in the identification delay. We introduce a novel, zero overhead algorithm called false collided bit error selective recovery tackling the error. There is a repetition gain in bit tracking-based anti-collision algorithms due to their nature, which can be utilized to detect and correct false collided bit errors without adding extra coding bits. Second, we extend bit tracking to 'error-free' scalable mutli-reader systems, while leaving the study of multi-readers tag identification over imperfect channels for future work. We propose the multi-reader RFID tag identification using bit tracking (MRTI-BT) algorithm which allows concurrent tag identification, by neighboring RFID readers, as opposed to time-consuming scheduling. MRTI-BT identifies tags exclusive to different RFIDs, concurrently. The concept of bit tracking and the proposed parallel identification property are leveraged to reduce the identification time compared to the state-of-The-Art. 2013 IEEE.This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) through NPRP under Grant 7-684-1-127. The work of A. Fahim and T. ElBatt was supported by the Vodafone Egypt Foundation.Scopu