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The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

Massive multiple-input multiple-output (MIMO) is currently entering the practical implementation phase, and key implementation issues for this technology have yet to be fully addressed. Crucial among these is the practical problem of frequency synchronisation, which refers to the adjustment of the clock frequency of local nodes to the clock frequency of a reference node by estimating and compensating carrier frequency offset. Existing theoretical studies on massive MIMO generally assume perfect frequency synchronisation; however, the potentially very high complexity of this process poses a major challenge for massive MIMO systems. Therefore, new frequency synchronisation techniques are urgently needed to make the practical implementation of massive MIMO feasible. In this study, the authors provide a comprehensive classification of the existing research efforts along this line, considering different antenna architectures and modulation schemes. They also highlight the key challenges in frequency synchronisation for massive MIMO, and they outline future research directions on this topic.

Accurate channel estimation is essential for achieving the performance gains offered by reconfigurable intelligent surface (RIS)-aided wireless communications. A variety of channel estimation methods have been proposed for such systems; however, none of the existing methods takes into account the effect of synchronization errors such as carrier frequency offset (CFO). In general, CFO can significantly degrade channel estimation performance of orthogonal frequency division multiplexing (OFDM) systems. Motivated by this, we investigate the effect of CFO on channel estimation for RIS-aided OFDM systems. Furthermore, we propose a joint CFO and channel impulse response (CIR) estimation method for these systems. Simulation results demonstrate the effectiveness of our proposed method, and also demonstrate that the use of time-domain rather than frequency-domain estimation in this context results in an improvement in the mean-squared error (MSE) performance of channel estimation as well as a significantly lower overall computational complexity., Comment: 5 pages, 5 figures, this manuscript has been submitted to IEEE WCL and accepted (Digital Object Identifier:10.1109/LWC.2021.3124049)

Carrier frequency offset (CFO) can significantly influence the performance of multiple-input multiple-output (MIMO) systems if not estimated and corrected. Most of the existing synchronization techniques are designed for MIMO systems with collocated antennas, where the same frequency error is experienced by the received signal at each antenna. In contrast, for MIMO systems with distributed antennas, the received signals at different receive antennas suffer from different frequency errors. This makes synchronization of such systems computationally complex, especially as the number of receive antennas grows large, i.e. massive MIMO. In this paper, we study the problem of CFO estimation for distributed massive MIMO and cell-free massive MIMO. In particular, we evaluate the performance of distributed large-scale MIMO systems with both collaborative and non-collaborative CFO estimation techniques based on the maximum likelihood (ML) criterion. These optimal CFO estimation results can then serve as benchmarks on the achievable estimation performance of practical collaborative and non-collaborative CFO estimation techniques for distributed MIMO systems. The proposed ML-based CFO estimation methods are tailored to work with periodic pilot sequences. We also show how exploiting the relationships which exist between the CFOs to be estimated can substantially reduce the computational load of CFO estimation in the collaborative approach.

Underwater time synchronisation is required to use the latest communication technologies. Because propagation time should be estimated to improve time synchronisation accuracy, node mobility is an important factor. In the subaqueous environment, furthermore, it is impossible to exclude the influence of the node movement occurred by environmental factors and underwater nodes could have self-mobility. Conventional time synchronisations have considered node mobility but have an unrealistic assumption of node movement and high computational complexity. In this paper, however, our novel method, Low-complexity time synchronisation for 3-dimensional underwater communications environment using estimated instantaneous node velocity based (V-Sync), shows high accuracy with low complexity and short processing latency. In this paper, it is supposed that mobile nodes could know their instantaneous speeds when they receive the synchronising messages. Time synchronisation errors, skew and offset, are estimated by the instantaneous speeds. The performance and analysis of V-Sync in the underwater mobile environment is demonstrated by simulation results.

Underwater wireless communication is greatly attractive to explore and develop because there is a vast unexploited area under the sea. Therefore, the effect of skew is not negligible when we use sound waves in water for wireless communication. For underwater sensor or cellular networks, some time synchronization protocols which consider the skew such as Time Synchronization for High Latency (TSHL) have been proposed to overcome the environmental limitation. TSHL has two phases. The first one is a skew estimation step with time stamps and linear regression. We propose a new skew estimation with a time interval of beacon messages and Root Mean Square which is simpler than linear regression of TSHL. Our Time Synchronization for Underwater Cellular Networks (TSUC) confirms really comparable accuracy with TSHL but much simpler in skew estimation. Additionally, we think of resynchronization too. Our second idea is using sliding window to reuse the former estimated skew data. Therefore, TSUC can reduce latency in re-synchronization. This is important since it makes TSUC appropriate for cellular networks.