Safety and pharmacokinetic comparison between fenofibric acid 135 mg capsule and 110 mg enteric-coated tablet in healthy volunteers.

This study aimed to compare the pharmacokinetic (PK) and safety profiles of 2 fenofibric acid formulations under fasting and fed conditions. The reference was a 135 mg capsule, while the test was a 110 mg enteric-coated tablet. This randomized, open-label, two-sequence, two-period crossover phase 1 clinical trial was conducted in healthy Korean men. Sixty participants were enrolled in each of the fasting and feeding groups. Blood samples were collected 72 hours after drug administration. PK parameters were calculated using a non-compartmental method with Phoenix WinNonlin®. A total of 53 and 51 participants from the fasting and feeding groups, respectively, completed the study. The geometric mean ratio and 90% confidence intervals of the maximum concentration (Cmax) and area under the concentration-time curve to the last measurable plasma concentration were 0.9195 (0.8795-0.9614) and 0.8630 (0.8472-0.8791) in the fasting study and 1.0926 (1.0102-1.1818) and 0.9998 (0.9675-1.0332) in the fed study, respectively. The time to reach Cmax of the enteric-coated tablet compared to that of the capsule was extended by 1 and 3 hours under fasting and fed conditions, respectively. In conclusion, enteric-coated tablets have a higher bioavailability than capsules. In addition, the enteric-coated tablet was smaller than the capsule, making it easier for patients to swallow. Trial Registration: Clinical Research Information Service Identifier: KCT0007177, KCT0003304.

A user application-based access point selection algorithm for dense WLANs.

The current commercial access point (AP) selection schemes are mostly based on received signal strength, but perform poorly in many situations. To address this problem, a number of alternative schemes collect and analyze the actual load of every candidate AP. However, these schemes may incur significant latency and signaling overhead in dense wireless local area networks (WLANs). To mitigate such overhead, we propose a user application-based AP selection scheme that considers historical information about AP performance. Without inducing any signaling activity, our scheme monitors the amount of network traffic used by applications and estimates the achievable throughput of APs. Our scheme employs the characteristics of application traffic with the intent of accurately predicting AP performance. Using a measurement study in dense WLAN environments, we show that our scheme achieves higher throughput and lower association latency than those of existing schemes in places highly accessible to the user.

Coexistence of ZigBee-Based WBAN and WiFi for Health Telemonitoring Systems.

The development of telemonitoring via wireless body area networks (WBANs) is an evolving direction in personalized medicine and home-based mobile health. A WBAN consists of small, intelligent medical sensors which collect physiological parameters such as electrocardiogram, electroencephalography, and blood pressure. The recorded physiological signals are sent to a coordinator via wireless technologies, and are then transmitted to a healthcare monitoring center. One of the most widely used wireless technologies in WBANs is ZigBee because it is targeted at applications that require a low data rate and long battery life. However, ZigBee-based WBANs face severe interference problems in the presence of WiFi networks. This problem is caused by the fact that most ZigBee channels overlap with WiFi channels, severely affecting the ability of healthcare monitoring systems to guarantee reliable delivery of physiological signals. To solve this problem, we have developed an algorithm that controls the load in WiFi networks to guarantee the delay requirement for physiological signals, especially for emergency messages, in environments with coexistence of ZigBee-based WBAN and WiFi. Since WiFi applications generate traffic with different delay requirements, we focus only on WiFi traffic that does not have stringent timing requirements. In this paper, therefore, we propose an adaptive load control algorithm for ZigBee-based WBAN/WiFi coexistence environments, with the aim of guaranteeing that the delay experienced by ZigBee sensors does not exceed a maximally tolerable period of time. Simulation results show that our proposed algorithm guarantees the delay performance of ZigBee-based WBANs by mitigating the effects of WiFi interference in various scenarios.

Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.

Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system throughput performance.