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Fulltext Two-Level Scheduling for Video Transmission over Downlink OFDMA Networks

Tham ML, Chow CO, Xu YH, Ramli N

PLoS One, 2016;11(2):e0148625.
PMID: 26906398 DOI: 10.1371/journal.pone.0148625

This paper presents a two-level scheduling scheme for video transmission over downlink orthogonal frequency-division multiple access (OFDMA) networks. It aims to maximize the aggregate quality of the video users subject to the playback delay and resource constraints, by exploiting the multiuser diversity and the video characteristics. The upper level schedules the transmission of video packets among multiple users based on an overall target bit-error-rate (BER), the importance level of packet and resource consumption efficiency factor. Instead, the lower level renders unequal error protection (UEP) in terms of target BER among the scheduled packets by solving a weighted sum distortion minimization problem, where each user weight reflects the total importance level of the packets that has been scheduled for that user. Frequency-selective power is then water-filled over all the assigned subcarriers in order to leverage the potential channel coding gain. Realistic simulation results demonstrate that the proposed scheme significantly outperforms the state-of-the-art scheduling scheme by up to 6.8 dB in terms of peak-signal-to-noise-ratio (PSNR). Further test evaluates the suitability of equal power allocation which is the common assumption in the literature.
Fulltext FedDdrl: Federated Double Deep Reinforcement Learning for Heterogeneous IoT with Adaptive Early Client Termination and Local Epoch Adjustment

Wong YJ, Tham ML, Kwan BH, Owada Y

Sensors (Basel), 2023 Feb 23;23(5).
PMID: 36904696 DOI: 10.3390/s23052494

Federated learning (FL) is a technique that allows multiple clients to collaboratively train a global model without sharing their sensitive and bandwidth-hungry data. This paper presents a joint early client termination and local epoch adjustment for FL. We consider the challenges of heterogeneous Internet of Things (IoT) environments including non-independent and identically distributed (non-IID) data as well as diverse computing and communication capabilities. The goal is to strike the best tradeoff among three conflicting objectives, namely global model accuracy, training latency and communication cost. We first leverage the balanced-MixUp technique to mitigate the influence of non-IID data on the FL convergence rate. A weighted sum optimization problem is then formulated and solved via our proposed FL double deep reinforcement learning (FedDdrl) framework, which outputs a dual action. The former indicates whether a participating FL client is dropped, whereas the latter specifies how long each remaining client needs to complete its local training task. Simulation results show that FedDdrl outperforms the existing FL scheme in terms of overall tradeoff. Specifically, FedDdrl achieves higher model accuracy by about 4% while incurring 30% less latency and communication costs.
Fulltext Mobility-Aware Resource Allocation in IoRT Network for Post-Disaster Communications with Parameterized Reinforcement Learning

Kabir H, Tham ML, Chang YC, Chow CO, Owada Y

Sensors (Basel), 2023 Jul 17;23(14).
PMID: 37514742 DOI: 10.3390/s23146448

Natural disasters, including earthquakes, floods, landslides, tsunamis, wildfires, and hurricanes, have become more common in recent years due to rapid climate change. For Post-Disaster Management (PDM), authorities deploy various types of user equipment (UE) for the search and rescue operation, for example, search and rescue robots, drones, medical robots, smartphones, etc., via the Internet of Robotic Things (IoRT) supported by cellular 4G/LTE/5G and beyond or other wireless technologies. For uninterrupted communication services, movable and deployable resource units (MDRUs) have been utilized where the base stations are damaged due to the disaster. In addition, power optimization of the networks by satisfying the quality of service (QoS) of each UE is a crucial challenge because of the electricity crisis after the disaster. In order to optimize the energy efficiency, UE throughput, and serving cell (SC) throughput by considering the stationary as well as movable UE without knowing the environmental priori knowledge in MDRUs aided two-tier heterogeneous networks (HetsNets) of IoRT, the optimization problem has been formulated based on emitting power allocation and user association combinedly in this article. This optimization problem is nonconvex and NP-hard where parameterized (discrete: user association and continuous: power allocation) action space is deployed. The new model-free hybrid action space-based algorithm called multi-pass deep Q network (MP-DQN) is developed to optimize this complex problem. Simulations results demonstrate that the proposed MP-DQN outperforms the parameterized deep Q network (P-DQN) approach, which is well known for solving parameterized action space, DQN, as well as traditional algorithms in terms of reward, average energy efficiency, UE throughput, and SC throughput for motionless as well as moveable UE.
Fulltext Tear Samples for Protein Extraction: Comparative Analysis of Schirmer's Test Strip and Microcapillary Tube Methods

Tham ML, Mahmud A, Abdullah M, Md Saleh R, Mohammad Razali A, Cheah YK, et al.

Cureus, 2023 Dec;15(12):e50972.
PMID: 38259376 DOI: 10.7759/cureus.50972

INTRODUCTION: Tear sampling is an attractive option for collecting biological samples in ophthalmology clinics, as it offers a non-invasive alternative to other invasive techniques. However, there are many tear sampling methods still in consideration. This study explores the suitability of Schirmer's test strip and microcapillary tube as reliable and satisfactory methods for tear sampling.
METHODS: Tear samples were collected from eight healthy volunteers using the standard Schirmer's test strip method with or without anesthesia and microcapillary tubes. The total tear protein concentrations were analyzed via spectrophotometry and bicinchoninic acid (BCA) protein assay. The protein profile was determined by sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). The optimal wetting length of Schirmer's strip and suitable buffer solutions were compared. Discomfort levels reported by participants and the ease of execution for ophthalmologists were also evaluated.
RESULTS: Tear samples exhibited typical protein profiles as shown by SDS-PAGE. The mean total protein obtained from an optimum wetting length of 20 mm using Schirmer's strip without anesthesia in phosphate-buffered saline (PBS) yielded substantial quantities of protein as measured by nanophotometer (220.20 ± 67.43 µg) and the BCA protein assay (210.34 ± 59.46 µg). This method collected a significantly higher quantity of protein compared to the microcapillary tube method (p=0.004) which was much more difficult to standardize. The clinician found it harder to utilize microcapillary tubes, while participants experienced higher insecurity and less discomfort with the microcapillary tube method. PBS used during the tear protein extraction process eluted higher tear protein concentration than ammonium bicarbonate, although the difference was not statistically significant. Using anaesthesia did not ease the sampling procedure substantially and protein quantity was maintained.
CONCLUSION: Good quality and quantity of protein from tear samples were extracted with the optimized procedure. Schirmer's strip test in the absence of local anesthesia provided a standard, convenient, and non-invasive method for tear collection.
Correction: Tear Samples for Protein Extraction: Comparative Analysis of Schirmer's Test Strip and Microcapillary Tube Methods

Tham ML, Mahmud A, Abdullah M, Md Saleh R, Mohammad Razali A, Cheah YK, et al.

Cureus, 2024 May;16(5):c173.
PMID: 38736763 DOI: 10.7759/cureus.c173

[This corrects the article DOI: 10.7759/cureus.50972.].