This paper proposes two numerical methods to solve the optimal problem of contrast enhancement in the cross-pol and co-pol channels. For the cross-pol channel case, the contrast (power ratio) is expressed in a homogeneous form, which leads the polarimetric contrast optimization to a distinctive eigenvalue problem. For the co-pol channel case, this paper proposes a cross iterative method for optimization, based on the formula used in the matched-pol channel. Both these numerical methods can be proved as convergent algorithms, and they are effective for obtaining the optimum polarization state. Besides, one of the proposed methods is applied to solve the optimal problem of contrast enhancement for the time-independent targets case. To verify the proposed methods, this paper provides two numerical examples. The results of calculation are completely identical with other authors', showing the validity of the proposed methods.

In H.264, the context-based adaptive variable length coding (CAVLC) is used for lossless compression. Direct table-lookup implementation requires higher cost because it employs a larger memory to produce the encoded results. In this letter, we present a more efficient technique for CAVLC implementation. Compared with those previous CAVLC chips, our design requires the lowest hardware cost.

Rapid building damage identification plays a vital role in rescue operations when disasters strike, especially when rescue resources are limited. In the past years, supervised machine learning has made considerable progress in building damage identification. However, the usage of supervised machine learning remains challenging due to the following facts: 1) the massive samples from the current damage imagery are difficult to be labeled and thus cannot satisfy the training requirement of deep learning, and 2) the similarity between partially damaged and undamaged buildings is high, hindering accurate classification. Leveraging the abundant samples of auxiliary domains, domain adaptation aims to transfer a classifier trained by historical damage imagery to the current task. However, traditional domain adaptation approaches do not fully consider the category-specific information during feature adaptation, which might cause negative transfer. To address this issue, we propose a novel domain adaptation framework that individually aligns each category of the target domain to that of the source domain. Our method combines the variational autoencoder (VAE) and the Gaussian mixture model (GMM). First, the GMM is established to characterize the distribution of the source domain. Then, the VAE is constructed to extract the feature of the target domain. Finally, the Kullback-Leibler (KL) divergence is minimized to force the feature of the target domain to observe the GMM of the source domain. Two damage detection tasks using post-earthquake and post-hurricane imageries are utilized to verify the effectiveness of our method. Experiments show that the proposed method obtains improvements of 4.4% and 9.5%, respectively, compared with the conventional method.

As an important method to solve sequential decision-making problems, reinforcement learning learns the policy of tasks through the interaction with environment. But it has difficulties scaling to large-scale problems. One of the reasons is the exploration and exploitation dilemma which may lead to inefficient learning. We present an approach that addresses this shortcoming by introducing qualitative knowledge into reinforcement learning using cloud control systems to represent ‘if-then’ rules. We use it as the heuristics exploration strategy to guide the action selection in deep reinforcement learning. Empirical evaluation results show that our approach can make significant improvement in the learning process.

This letter derives the packet error rate (PER) in terms of the retry limit and the channel error probability in wireless local area networks (WLANs), when an additional number of retries is allocated to a block of packets to be transmitted. We prove that the lower bound of the PER is the dropping probability which is defined as the probability of any given packet being dropped after its retry limit has been reached.

In this paper we apply a parallel adaptive solution algorithm to simulate nanoscale double-gate metal-oxide-semiconductor field effect transistors (MOSFETs) on a personal computer (PC)-based Linux cluster with the message passing interface (MPI) libraries. Based on a posteriori error estimation, the triangular mesh generation, the adaptive finite volume method, the monotone iterative method, and the parallel domain decomposition algorithm, a set of two-dimensional quantum correction hydrodynamic (HD) equations is solved numerically on our constructed cluster system. This parallel adaptive simulation methodology with 1-irregular mesh was successfully developed and applied to deep-submicron semiconductor device simulation in our recent work. A 10 nm n-type double-gate MOSFET is simulated with the developed parallel adaptive simulator. In terms of physical quantities and refined adaptive mesh, simulation results demonstrate very good accuracy and computational efficiency. Benchmark results, such as load-balancing, speedup, and parallel efficiency are achieved and exhibit excellent parallel performance. On a 16 nodes PC-based Linux cluster, the maximum difference among CPUs is less than 6%. A 12.8 times speedup and 80% parallel efficiency are simultaneously attained with respect to different simulation cases.