Transmission Control Protocol with Network Coding (TCP/NC) and its variants were developed to improve the goodput performance of TCP protocol in lossy networks. However, no TCP/NC enhancement work can get a high goodput performance in bi-directional packet loss environments, especially with a high degree and/or burst loss of ACK packets. Therefore, we propose a new scheme called TCP/NC with Bi-directional Loss Tolerance (TCP/NCwBLT) to maintain a goodput in a wide range of bi-directional loss environments by combining three techniques of “ACK accumulation”, “ACK retransmission”, and “Duplicate ACK generator” at the source and/or the sink sides. The simulation results on ns-3 show that TCP/NCwBLT always outperforms the various TCPs and a recent variant of TCP/NC when packet losses happen in both directions, and its advantage significantly increases at a high ACK loss rate.

Spectrum sensing (SS) plays a significant role in cognitive radio network. In our work, we focused on FPGA (Field Programmable Gate Array) implementation of SS applying for DVB-T (Digital Video Broadcasting - Terrestrial) signal detection because TV band has been considered for the operation of cognitive users (unlicensed users) by Federal Communications Commission (FCC). The OFDM signal (Orthogonal Frequency Division Multiplexing) - 1024 subcarriers in 2k mode of DVB-T system is considered as primary transmitter as specified in IEEE 802.22 WRAN standard for reusing the TV frequency band. The system is synthesized and implemented on Stratix II EP2S180 board. The fixed point 32 bits (fraction: 22.10) is chosen for calculation in FPGA model. Numerical results showed that detection effectiveness of ED based on FPGA platform matched well with theoretical analyses. They also indicated that system performances degraded dramatically in Rayleigh fading environment. Thus, longer sensing time should be considered and and higher probability of false alarm should be accepted to improve sensing efficiency in fading condition.

Wearable devices have gained significant popularity for continuous Electrocardiogram (ECG) monitoring due to their compactness and convenience. Day-by-day and 24/7 monitoring without gaps is demanded to promptly detect unusual symptoms that are short-term warning signs of dangerous diseases. Cost-saving criteria are the highest priority to extend the use time of devices and maintain the system operation. However, not all measured signals are useful because the signal quality will be affected by many things such as motion artifacts and muscle noise. Demands on classifying and sending only the usable signal is a requirement, it can save the battery on wearable devices and the expense on the server side (i.e., cloud-based processing), such as storage and processing resources. Therefore, Signal Quality Indices (SQIs) have been developed and researched to determine signal quality to meet the above requirements. This study introduces a novel SQI approach to classify signals. The proposed method has three contributions: 1) exponentially Weighted Mean-Variance (EWMV), a lightweight equation, to identify peaks, followed by applying an adaptive threshold to define peaks that have the same shape as R-peaks; 2) outlier elimination process is proposed to enhance the accuracy; and 3) maximal Overlap Discrete Wavelet Transform (MODWT) is employed to categorize ECG signals into a new class, potentially containing signals relevant to pathological analysis. Experimental results demonstrate that our algorithm achieves the highest sensitivity for both noisy and noiseless data sets. Specifically, it achieved a sensitivity of 99.31% for clean signals and 97.69% for noisy signals. In the case of the PhysioNet challenge data set, while our sensitivity of 96.37% may not be the highest, our accuracy stands out at 95.10%, surpassing other methods recently reviewed. Additionally, our approach demonstrates the lowest trade-off between sensitivity and accuracy among the surveyed SQI techniques.