Outcomes Of Microincision Pars Plana Vitrectomy In Rhegmatogenous Retinal Detachment.

BACKGROUND: With the technological advances, microincision pars plana vitrectomy is commonly used method for primary treatment of eyes with rhegmatogenous retinal detachment. Objective of this study is to evaluate anatomical and visual outcomes of microincision pars plana vitrectomy in eyes with rhegmatogenous retinal detachment. METHODS: This was a hospital based prospective observational study done in Tilganga Institute of Ophthalmology, Kathmandu, Nepal. All consecutive cases of rhegmatogenous retinal detachment who underwent primary microincision pars plana vitrectomy from October 2020 to March 2021 were included in the study. Patients were evaluated at baseline, postoperative day 1, 1 week, 6 weeks and 3 months. Outcome measures evaluated were anatomical results, visual outcomes and complications of the surgery. RESULTS: Forty-nine eyes with rhegmatogenous retinal detachment treated with primary microincision pars plana vitrectomy with minimum follow up of at least 3 months were evaluated. Anatomical success was achieved in 91.8% of cases (45/49). Baseline mean best corrected visual acuity was logMAR 1.63±0.88 and median best corrected visual acuity was 2.00 (range 0.00 to 2.70) while at 3 months follow up mean best corrected visual acuity was logMAR 1.22±0.66 and median BCVA was 1.00 ( range 0.00 to 2.70). There was significant improvement in median BCVA ( p= 0.005). There were no cases of postoperative hypotony and endophthalmitis. Other complications were also minimal such as silicon oil in anterior chamber in 1 eye, epiretinal membrane in 3 eyes and macular hole in 2 eyes. CONCLUSIONS: Microincision pars plana vitrectomy is an effective surgical method of primary treatment for rhegmatogenous retinal detachment with good anatomical and visual outcomes with minimal complications.

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies.

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS's incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices.

A long-range wide area network (LoRaWAN) is one of the leading communication technologies for Internet of Things (IoT) applications. In order to fulfill the IoT-enabled application requirements, LoRaWAN employs an adaptive data rate (ADR) mechanism at both the end device (ED) and the network server (NS). NS-managed ADR aims to offer a reliable and battery-efficient resource to EDs by managing the spreading factor (SF) and transmit power (TP). However, such management is severely affected by the lack of agility in adapting to the variable channel conditions. Thus, several hours or even days may be required to converge at a level of stable and energy-efficient communication. Therefore, we propose two NS-managed ADRs, a Gaussian filter-based ADR (G-ADR) and an exponential moving average-based ADR (EMA-ADR). Both of the proposed schemes operate as a low-pass filter to resist rapid changes in the signal-to-noise ratio of received packets at the NS. The proposed methods aim to allocate the best SF and TP to both static and mobile EDs by seeking to reduce the convergence period in the confirmed mode of LoRaWAN. Based on the simulation results, we show that the G-ADR and EMA-ADR schemes reduce the convergence period in a static scenario by 16% and 68%, and in a mobility scenario by 17% and 81%, respectively, as compared to typical ADR. Moreover, we show that the proposed schemes are successful in reducing the energy consumption and enhancing the packet success ratio.

Lightweight Workload Fingerprinting Localization Using Affinity Propagation Clustering and Gaussian Process Regression.

Fingerprinting localization approach is widely used in indoor positioning applications owing to its high reliability. However, the learning procedure of radio signals in fingerprinting is time-consuming and labor-intensive. In this paper, an affinity propagation clustering (APC)-based fingerprinting localization system with Gaussian process regression (GPR) is presented for a practical positioning system with the reduced offline workload and low online computation cost. The proposed system collects sparse received signal strength (RSS) data from the deployed Bluetooth low energy beacons and trains them with the Gaussian process model. As the signal estimation component, GPR predicts not only the mean RSS but also the variance, which indicates the uncertainty of the estimation. The predicted RSS and variance can be employed for probabilistic-based fingerprinting localization. As the clustering component, the APC minimizes the searching space of reference points on the testbed. Consequently, it also helps to reduce the localization estimation error and the computational cost of the positioning system. The proposed method is evaluated through real field deployments. Experimental results show that the proposed method can reduce the offline workload and increase localization accuracy with less computational cost. This method outperforms the existing methods owing to RSS prediction using GPR and RSS clustering using APC.