Information-Coupled Turbo Codes for LTE Systems

We propose a new class of information-coupled (IC) Turbo codes to improve the transport block (TB) error rate performance for long-term evolution (LTE) systems, while keeping the hybrid automatic repeat request protocol and the Turbo decoder for each code block (CB) unchanged. In the proposed codes, every two consecutive CBs in a TB are coupled together by sharing a few common information bits. We propose a feed-forward and feed-back decoding scheme and a windowed (WD) decoding scheme for decoding the whole TB by exploiting the coupled information between CBs. Both decoding schemes achieve a considerable signal-to-noise-ratio (SNR) gain compared to the LTE Turbo codes. We construct the extrinsic information transfer (EXIT) functions for the LTE Turbo codes and our proposed IC Turbo codes from the EXIT functions of underlying convolutional codes. An SNR gain upper bound of our proposed codes over the LTE Turbo codes is derived and calculated by the constructed EXIT charts. Numerical results show that the proposed codes achieve an SNR gain of 0.25 dB to 0.72 dB for various code parameters at a TB error rate level of $10^{-2}$, which complies with the derived SNR gain upper bound.Comment: 13 pages, 12 figure

Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go

Adverse conditions typically suffer from stochastic hybrid weather degradations (e.g., rainy and hazy night), while existing image restoration algorithms envisage that weather degradations occur independently, thus may fail to handle real-world complicated scenarios. Besides, supervised training is not feasible due to the lack of a comprehensive paired dataset to characterize hybrid conditions. To this end, we have advanced the aforementioned limitations with two tactics: framework and data. First, we present a novel unified framework, dubbed RAHC, to Restore Arbitrary Hybrid adverse weather Conditions in one go. Specifically, our RAHC leverages a multi-head aggregation architecture to learn multiple degradation representation subspaces and then constrains the network to flexibly handle multiple hybrid adverse weather in a unified paradigm through a discrimination mechanism in the output space. Furthermore, we devise a reconstruction vectors aided scheme to provide auxiliary visual content cues for reconstruction, thus can comfortably cope with hybrid scenarios with insufficient remaining image constituents. Second, we construct a new dataset, termed HAC, for learning and benchmarking arbitrary Hybrid Adverse Conditions restoration. HAC contains 31 scenarios composed of an arbitrary combination of five common weather, with a total of ~316K adverse-weather/clean pairs. Extensive experiments yield superior results and establish new state-of-the-art results on both HAC and conventional datasets.Comment: In submissio

Associations of road traffic noise and its frequency spectrum with prevalent depression in Taichung, Taiwan

IntroductionExposure to road traffic noise has been reported to be associated with depression in many epidemiological studies, but the association between noise frequency spectrum and depression remains unclear. This community-based study investigated the associations between road traffic noise exposure and its frequency components with prevalent depression.MethodsA total of 3,191 residents living in Taichung who participated in the Taiwan Biobank between 2010 and 2017, were included as study participants. The land-use regression models were used to evaluate individual annual average values of A-weighted equivalent sound level over 24 h (Leq,24h) and particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) using the geographic information system. Multiple logistic regression was applied to estimate the odds ratios (ORs) for depression after adjusting for potential risk factors and PM2.5.ResultsAn interquartile range increase in Leq,24h at full frequency (4.7 dBA), 1,000 Hz (5.2 dB), and 2,000 Hz (4.8 dB) was significantly associated with an elevated risk for depression with ORs of 1.62 (95% confidence interval [CI]: 1.03, 2.55), 1.58 (95% CI: 1.05, 2.37), and 1.58 (95% CI:1.03, 2.43), respectively, by controlling for PM2.5. The high-exposure group (≥3rd quartile median of noise levels) at full frequency, 1,000 Hz, and 2,000 Hz had an increased risk for depression with ORs of 2.65 (95% CI: 1.16–6.05), 2.47 (95% CI: 1.07–5.70), and 2.60 (95% CI: 1.10–6.12), respectively, compared with the reference group (<1st quartile of noise levels) after adjustment for PM2.5. Significant exposure-response trends were observed between the prevalent depression and noise exposure by quartiles at full frequency, 1,000 Hz, and 2,000 Hz (all p < 0.05).ConclusionExposure to road traffic noise may be associated with an increased prevalence of depression, particularly at 1,000 and 2,000 Hz

Correlation of the composite equilibrium score of computerized dynamic posturography and clinical balance tests

AbstractBackgroundThe computerized dynamic posturography has been widely used to access balance control in patients with balance dysfunction. A composite-equilibrium score (CS) can be calculated from the sensory organization test using the computerized dynamic posturography. However, the correlation between the composite equilibrium score and clinical tests and its ability to predict falls has rarely been explored in the past.MethodsA total of 60 patients with chief complaint of dizziness were enrolled in our study, and clinical assessments were done including the sensory organization test (SOT), Timed Up and Go test (TUG), Tinetti Performance-Oriented Mobility Assessment (POMA), and the dynamic gait index (DGI). The age and the subjective feeling of the severity of dizziness quantified by the visual analog scale (VAS) of each patient were also recorded.ResultsStatistical analysis revealed significant correlation between the composite equilibrium score and the TUG, POMA (gait, balance and total scores), and the DGI. However, there is statistically significant correlation between neither the CS and the age nor the VAS of dizziness. When grouping the DGI, POMA (total score), and the TUG cutoff to predict fall risks, the correlations to the CS can still be established except the TUG.ConclusionFrom the results of our study, the validity of the clinical tests was established in assessment of balance function, and clinicians can utilize these tools for preliminary evaluation of patient balance when computerized dynamic posturography is not available. In addition, CS can be used to predict the risk of falls