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This paper presents a Viterbi-specific 2T gain cellbased embedded DRAM (eDRAM) design for IEEE 802.11n WLAN application. In the proposed Viterbi decoder, refresh operations are completely removed in the eDRAM, by ensuring that the read-after-write period of survivor memory is shorter than the retention time of the gain cell. In order to facilitate the write operation with single-supply voltage, a beneficial read word-line (RWL) coupling technique is proposed. In this work, we also present a reference voltage generation scheme to support single-ended read operation. Thanks to the decoupled read and write structure of the gain cell, the proposed eDRAM can support dual-port operations without large area overhead, thus doubling the bandwidth of memories in the Viterbi decoder. To further reduce the area of the customized Viterbi memory, common redundant hardware between the memory peripheral and computational logics is identified and eliminated without latency overhead. The 4 bit soft-decision 64-state Viterbi decoder with 24 kb eDRAM (1-bank) is implemented in 65 nm CMOS process technology. The chip measurement results show 44% area and 39% power savings over the conventional SRAM-based Viterbi decoder implementation.

The current method of estimating CO 2 emissions during the construction phase does not consider the variability that can occur in actual work. Therefore, this study aims at probabilistic CO 2 estimation dealing with the statistical characteristics in activity data of building construction work, focused on concrete pouring work and based on field data. The probabilistically estimated CO 2 emissions have some differences from CO 2 emissions measured by current deterministic methods. The results revealed that the minimum difference was 11.4%, and the maximum difference was 132.7%. This study also used Monte Carlo simulations to derive information on a probability model of CO 2 emissions. Results of the analysis revealed that there is a risk of underestimating emissions because the amount of emissions was estimated at a level that exceeds the 95% confidence interval of the simulation results. In addition, the probability that CO 2 emissions using the measured activities data were less than the estimated CO 2 emissions using the bill of quantity was 73.2% in the probability distribution model.