In this paper, we propose an end-to-end two-branch feature attention network. The network is mainly used for single image dehazing. The network consists of two branches, we call it CAA-Net: 1) A U-NET network composed of different-level feature fusion based on attention (FEPA) structure and residual dense block (RDB). In order to make full use of all the hierarchical features of the image, we use RDB. RDB contains dense connected layers and local feature fusion with local residual learning. We also propose a structure which called FEPA.FEPA structure could retain the information of shallow layer and transfer it to the deep layer. FEPA is composed of serveral feature attention modules (FPA). FPA combines local residual learning with channel attention mechanism and pixel attention mechanism, and could extract features from different channels and image pixels. 2) A network composed of several different levels of FEPA structures. The network could make feature weights learn from FPA adaptively, and give more weight to important features. The final output result of CAA-Net is the combination of all branch prediction results. Experimental results show that the CAA-Net proposed by us surpasses the most advanced algorithms before for single image dehazing.

Recent developments in engineering applications of stochastic resonance have expanded to various fields, especially biomedicine. Deterministic chaos generates a phenomenon known as chaotic resonance, which is similar to stochastic resonance. However, engineering applications of chaotic resonance are limited owing to the problems in controlling chaos, despite its uniquely high sensitivity to weak signal responses. To tackle these problems, a previous study proposed “reduced region of orbit” (RRO) feedback methods, which cause chaotic resonance using external feedback signals. However, this evaluation was conducted under noise-free conditions. In actual environments, background noise and measurement errors are inevitable in the estimation of RRO feedback strength; therefore, their impact must be elucidated for the application of RRO feedback methods. In this study, we evaluated the chaotic resonance induced by the RRO feedback method in chaotic neural systems in the presence of stochastic noise. Specifically, we focused on the chaotic resonance induced by RRO feedback signals in a neural system composed of excitatory and inhibitory neurons, a typical neural system wherein chaotic resonance is observed in the presence of additive noise and feedback signals including the measurement error (called contaminant noise). It was found that for a relatively small noise strength, both types of noise commonly degenerated the degree of synchronization in chaotic resonance induced by RRO feedback signals, although these characteristics were significantly different. In contrast, chaos-chaos intermittency synchronization was observed for a relatively high noise strength owing to the noise-induced attractor merging bifurcation for both types of noise. In practical neural systems, the influence of noise is unavoidable; therefore, this study highlighted the importance of the countermeasures for noise in the application of chaotic resonance and utilization of noise-induced attractor merging bifurcation.

This paper presents a convex-analytic framework to learn sparse graphs from data. While our problem formulation is inspired by an extension of the graphical lasso using the so-called combinatorial graph Laplacian framework, a key difference is the use of a nonconvex alternative to the ℓ₁ norm to attain graphs with better interpretability. Specifically, we use the weakly-convex minimax concave penalty (the difference between the ℓ₁ norm and the Huber function) which is known to yield sparse solutions with lower estimation bias than ℓ₁ for regression problems. In our framework, the graph Laplacian is replaced in the optimization by a linear transform of the vector corresponding to its upper triangular part. Via a reformulation relying on Moreau's decomposition, we show that overall convexity is guaranteed by introducing a quadratic function to our cost function. The problem can be solved efficiently by the primal-dual splitting method, of which the admissible conditions for provable convergence are presented. Numerical examples show that the proposed method significantly outperforms the existing graph learning methods with reasonable computation time.

Reducing the number of link crossings in a network drawn on the plane such as a wiring board is a well-known problem, and especially the calculation of the minimum number of such crossings: this is the crossing number problem. It has been shown that finding a general solution to the crossing number problem is NP-hard. So, this problem is addressed for particular classes of graphs and this is also our approach in this paper. More precisely, we focus hereinafter on the torus topology. First, we discuss an upper bound on cr(T(2, k)) the number of crossings in a 2-dimensional k-ary torus T(2, k) where k ≥ 2: the result cr(T(2, k)) ≤ k(k - 2) and the given constructive proof lay foundations for the rest of the paper. Second, we extend this discussion to derive an upper bound on the crossing number of a 3-dimensional k-ary torus: cr(T(3, k)) ≤ 2k⁴ - k³ - 4k² - 2⌈k/2⌉⌊k/2⌋(k - (k mod 2)) is obtained. Third, an upper bound on the crossing number of an n-dimensional k-ary torus is derived from the previously established results, with the order of this upper bound additionally established for more clarity: cr(T(n, k)) is O(n²k^2n-2) when n ≥ k and O(nk^2n-1) otherwise.

At ToSC 2021, Sakamoto et al. proposed Rocca, an AES-based encryption scheme, for Beyond 5G applications. They presented a class of round functions that achieved impressive performance in software by improving the design strategy for constructing an efficient AES-based round function that was proposed by Jean and Nikolić at FSE 2016. In this paper, we revisit their design strategy for finding more efficient round functions. We add new requirements further to improve speed of Rocca. Specifically, we focus on the number of temporary registers for updating the round function and search for round functions with the minimum number of required temporary registers. As a result, we find a class of round functions with only one required temporary register, while round function of Rocca requires two temporary registers. We show that new round functions are significantly faster than that of Rocca on the latest Ice Lake and Tiger Lake architectures. We emphasize that, regarding speed, our round functions are optimal among the Rocca class of round functions because the search described in this paper covers all candidates that satisfy the requirements of Rocca.

Solar energy is an important energy resource for a sustainable society and is massively introduced these days. Household generally sells their excess solar energy by the reverse power flow, but the massive reverse power flow usually sacrifices the grid stability. In order to utilize renewable energy effectively and reduce solar energy waste, electric vehicles (EVs) takes an important role to fill in the spatiotemporal gap of solar energy. This paper proposes a novel EV aggregation framework for spatiotemporal shifting of solar energy without any reverse power flow. The proposed framework causes charging and discharging via an EV aggregator by intentionally changing the price, and the solar energy waste is expected to reduce by the energy trade. Simulation results show the proposed framework reduced the solar energy waste by 68%.

For a long time, the compensation effect of hearing aid is mainly evaluated subjectively, and there are fewer studies of objective evaluation. Furthermore, a pure speech signal is generally required as a reference in the existing objective evaluation methods, which restricts the practicality in a real-world environment. Therefore, this paper presents a non-intrusive speech quality evaluation method for hearing aid, which combines the audiogram and weighted frequency information. The proposed model mainly includes an audiogram information extraction network, a frequency information extraction network, and a quality score mapping network. The audiogram is the input of the audiogram information extraction network, which helps the system capture the information related to hearing loss. In addition, the low-frequency bands of speech contain loudness information and the medium and high-frequency components contribute to semantic comprehension. The information of two frequency bands is input to the frequency information extraction network to obtain time-frequency information. When obtaining the high-level features of different frequency bands and audiograms, they are fused into two groups of tensors that distinguish the information of different frequency bands and used as the input of the attention layer to calculate the corresponding weight distribution. Finally, a dense layer is employed to predict the score of speech quality. The experimental results show that it is reasonable to combine the audiogram and the weight of the information from two frequency bands, which can effectively realize the evaluation of the speech quality of the hearing aid.

In this letter, we consider a global stabilization problem for a class of feedforward systems by an event-triggered control. This is an extended work of [10] in a way that there are uncertain feedforward nonlinearity and time-varying input delay in the system. First, we show that the considered system is globally asymptotically stabilized by a proposed event-triggered controller with a gain-scaling factor. Then, we also show that the interexecution times can be enlarged by adjusting a gain-scaling factor. A simulation example is given for illustration.

Construction of resilient Boolean functions in odd variables having strictly almost optimal (SAO) nonlinearity appears to be a rather difficult task in stream cipher and coding theory. In this paper, based on the modified High-Meets-Low technique, a general construction to obtain odd-variable SAO resilient Boolean functions without directly using PW functions or KY functions is presented. It is shown that the new class of functions possess higher resiliency order than the known functions while keeping higher SAO nonlinearity, and in addition the resiliency order increases rapidly with the variable number n.

Locally repairable codes have attracted lots of interest in Distributed Storage Systems. If a symbol of a code can be repaired respectively by t disjoint groups of other symbols, each groups has size at most r, we say that the code symbol has (r, t)-locality. In this paper, we employ parity-check matrix to construct information single-parity (r, t)-locality LRCs. All our codes attain the Singleton-like bound of LRCs where each repair group contains a single parity symbol and thus are optimal.

To improve the performance of underwater acoustic communications, this letter proposes a polar coding scheme with adaptive channel equalization, which can reduce the amount of feedback information. Furthermore, a hybrid automatic repeat request (HARQ) mechanism is provided to mitigate the impact of estimation errors. Simulation results show that the proposed scheme outperforms the turbo equalization in bit error rate. Computational complexity analysis is also provided for comparison.

This letter proposes a novel intelligent dynamic channel assignment (DCA) scheme with small-cells to improve the system performance for uplink machine-type communications (MTC) based on OFDMA-FDD. Outdoor MTC devices (OMDs) have serious interference from indoor MTC devices (IMDs) served by small-cell access points (SAPs) with frequency reuse. Thus, in the proposed DCA scheme, the macro base station (MBS) first measures the received signal strength from both OMDs and IMDs after setting the transmission power. Then, the MBS dynamically assigns subchannels to each SAP with consideration of strong interference from IMDs to the MBS. Through simulation results, it is shown that the proposed DCA scheme outperforms other schemes in terms of the capacity of OMDs and IMDs.

Recently, the face hallucination method based on deep learning understands the mapping between low-resolution (LR) and high-resolution (HR) facial patterns by exploring the priors of facial structure. However, how to maintain the face structure consistency after the reconstruction of face images at different scales is still a challenging problem. In this letter, we propose a novel multi-scale structure prior learning (MSPL) for face hallucination. First, we propose a multi-scale structure prior block (MSPB). Considering the loss of high-frequency information in the LR space, we mainly process the input image in three different scale ascending dimensional spaces, and map the image to the high dimensional space to extract multi-scale structural prior information. Then the size of feature maps is recovered by downsampling, and finally the multi-scale information is fused to restore the feature channels. On this basis, we propose a local detail attention module (LDAM) to focus on the local texture information of faces. We conduct extensive face hallucination reconstruction experiments on a public face dataset (LFW) to verify the effectiveness of our method.