Although power conversion efficiency (PCE) of state‐of‐the‐art perovskite solar cells has already exceeded 20%, photo‐ and/or moisture instability of organolead halide perovskite have prevented further commercialization. In particular, the underlying weak interaction of organic cations with surrounding iodides due to eight equivalent orientations of the organic cation along the body diagonals in unit cell and chemically non‐inertness of organic cation result in photo‐ and moisture instability of organometal halide perovskite. Here, a perovskite light absorber incorporating organic–inorganic hybrid cation in the A‐site of 3D APbI3 structure with enhanced photo‐ and moisture stability is reported. A partial substitution of Cs+ for HC(NH2)2+ in HC(NH2)2PbI3 perovskite is found to substantially improve photo‐ and moisture stability along with photovoltaic performance. When 10% of HC(NH2)2+ is replaced by Cs+, photo‐ and moisture stability of perovskite film are significantly improved, which is attributed to the enhanced interaction between HC(NH2)2+ and iodide due to contraction of cubo‐octahedral volume. Moreover, trap density is reduced by one order of magnitude upon incorporation of Cs+, which is responsible for the increased open‐circuit voltage and fill factor, eventually leading to enhancement of average PCE from 14.9% to 16.5%.

The database query optimizer requires the estimation of the query selectivity to find the most efficient access plan. For queries referencing multiple attributes from the same relation, we need a multi-dimensional selectivity estimation technique when the attributes are dependent each other because the selectivity is determined by the joint data distribution of the attributes. Additionally, for multimedia databases, there are intrinsic requirements for the multi-dimensional selectivity estimation because feature vectors are stored in multi-dimensional indexing trees. In the l-dimensional case, a histogram is practically the most preferable. In the multi-dimensional case, however, a histogram is not adequate because of high storage overhead and high error rates.In this paper, we propose a novel approach for the multidimensional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. In addition, this approach has the advantage of supporting dynamic data updates by eliminating the overhead for periodical reconstructions of the compressed information. Extensive experimental results show advantages of the proposed approach.

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This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for performing driver-behavior identification using in-vehicle controller area network (CAN-BUS) sensor data. The proposed method outperforms the state-of-the-art driver-behavior profiling models. Particularly, it exhibits significantly reduced computations (i.e., reduced numbers both of floating-point operations and parameters), more efficient memory usage (compact model size), and less inference time. The proposed architecture features depth-wise convolution, along with augmented recurrent neural networks (long short-term memory or gated recurrent unit), for time-series classification. The minimum time-step length (window size) required in the proposed method is significantly lower than that required by recent algorithms. We compared our results with compressed versions of existing models by applying efficient channel pruning on several layers of current models. Furthermore, our network can adapt to new classes using sparse-learning techniques, that is, by freezing relatively strong nodes at the fully connected layer for the existing classes and improving the weaker nodes by retraining them using data regarding the new classes. We successfully deploy the proposed method in a container environment using NVIDIA Docker in an embedded system (Xavier, TX2, and Nano) and comprehensively evaluate it with regard to numerous performance metrics.

The database query optimizer requires the estimation of the query selectivity to find the most efficient access plan. For queries referencing multiple attributes from the same relation, we need a multi-dimensional selectivity estimation technique when the attributes are dependent each other because the selectivity is determined by the joint data distribution of the attributes. Additionally, for multimedia databases, there are intrinsic requirements for the multi-dimensional selectivity estimation because feature vectors are stored in multi-dimensional indexing trees. In the l-dimensional case, a histogram is practically the most preferable. In the multi-dimensional case, however, a histogram is not adequate because of high storage overhead and high error rates.In this paper, we propose a novel approach for the multidimensional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. In addition, this approach has the advantage of supporting dynamic data updates by eliminating the overhead for periodical reconstructions of the compressed information. Extensive experimental results show advantages of the proposed approach.

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