Exact Solutions for (

The construction of exact solution for higher-dimensional nonlinear equation plays an important role in knowing some facts that are not simply understood through common observations. In our work, (4+1)-dimensional nonlinear Fokas equation, which is an important physical model, is discussed by using the extended F-expansion method and its variant. And some new exact solutions expressed by Jacobi elliptic function, Weierstrass elliptic function, hyperbolic function, and trigonometric function are obtained. The related results are enriched

Novel N-Type π-Conjugated Polymers for All-Polymer Solar Cell

Organic solar cells (OSCs), also known as, organic photovoltaics (OPVs), appear as a promising technology for renewable energy owing to their light weight, great flexibility and low-cost fabrication process. So far most of the OPVs have been using fullerene derivatives, such as PCBM or PC71BM, as the electron acceptor in the active layer, which have been proven to a bottleneck for this technology. Therefore, developing non-fullerene acceptors has become the new driving force for this field. Allpolymer solar cells (all-PSCs) that have the advantages of robustness, stability and tunability have already achieved PCE up to 9%. However, there is still a significant gap between the all-PSCs and fullerene-based OSCs (PCE approaching 12%) despite tremendous effort that has been put into the optimization of both material and device. Thus, developing novel acceptor materials is imperative for improving the performance of all-PSCs. In this thesis, three classes of π-conjugated polymers were designed and synthesized for the application of all-PSC. The first class of polymers is based on an novel electron-deficient moiety, (3E,7E)-3,7-bis(2-oxoindolin-3-ylidene)-5,7-dihydropyrrolo[2,3-f]indole- 2,6(1H,3H)-dione (IBDP). The IBDP-based polymers (P1 and P2) showed balanced ambipolar transport property (electron mobility up to 0.10 cm2 V-1 s-1 and hole mobility up to 0.19 cm2 V-1 s-1) in OTFTs. In addition to the good charge transport properties, the IBDP polymers exhibited strong and broad adsorption profile across the visible and NIR region up 1100 nm as well as elevated LUMO levels at -3.70 eV. With these advantageous features, these IBDP polymers were used as acceptor with poly(3- hexylthiophene-2,5-diyl) (P3HT) as the donor in all-PSCs. After donor/acceptor ratio optimization, the resultant all-PSC devices showed high PCE of 3.38%, which is the highest PCE that has been obtained from P3HT-based all-PSCs so far. The second class consists of three (3E,7E)-3,7-bis(2-oxoindolin-3- ylidene)benzo[1,2-b:4,5-b’]difuran-2,6(3H,7H)-dione (IBDF)-based polymers that feature a new type of side chains that contain an ester group. The resultant IBDF polymers exhibited excellent electron transport properties with electron mobility up to 0.35 cm2 V-1 s-1 in OTFTs. When used as acceptor in all-PSCs with PTB7-Th as donor, low PCEs (<0.4%) were obtained, which was found to be caused by the poor miscibility of the donor and acceptor, as well as the inferior bulk charge transport properties of the IBDF polymers. Finally, a new building block, dihydroxylnaphthalene diimide (NDIO), was introduced for the first time into π-conjugated polymers. Due to the alkoxy groups, the electron affinity of the NDIO polymer is significantly higher than the NDI analogues, which led to an enhanced electron transport property and more stable performance in OTFTs upon air-exposure. When used as acceptor in all-PSCs with PTB7-Th as the donor, a decent PCE of 3.25 % was realized. In particular, the FF (0.61) of the solar cell devices is much higher than those of the NDI polymers based all-PSCs, which was attributed to the balanced charge transport for both hole and electron in the active layer, as well as the suppressed bimolecular recombination

Integrated Sensing, Computation, and Communication: System Framework and Performance Optimization

Integrated sensing, computation, and communication (ISCC) has been recently considered as a promising technique for beyond 5G systems. In ISCC systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module. It can reduce the overhead of computation resource by detecting whether the sensing target is static. Subsequently, we analyze the sensing performance of the proposed framework and theoretically prove its effectiveness with the help of the sampling theorem. Then, we formulate a sensing accuracy maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy, in which the minimal resource is allocated to computation tasks, and the rest is devoted to sensing tasks. Besides, a threshold selection policy is derived. Compared with the conventional schemes, the results further demonstrate the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis, and extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes

Constrained Clustering Based on the Link Structure of a Directed Graph

In many segmentation applications, data objects are often clustered based purely on attribute-level similarities. This practice has neglected the useful information that resides in the link structure among data objects and the valuable expert domain knowledge about the desirable cluster assignment. Link structure can carry worthy information about the similarity between data objects (e.g. citation), and we should also incorporate the existing domain information on preferred outcome when segmenting data. In this paper, we investigate the segmentation problem combining these three sources of information, which has not been addressed in the existing literature. We propose a segmentation method for directed graphs that incorporates the attribute values, link structure and expert domain information (represented as constraints). The proposed method combines these three types of information to achieve good quality segmentation on data which can be represented as a directed graph. We conducted comprehensive experiments to evaluate various aspects of our approach and demonstrate the effectiveness of our method

Pansharpening via Frequency-Aware Fusion Network with Explicit Similarity Constraints

The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening. With the development of convolutional neural networks, the performance of pansharpening methods has been improved, however, the blurry effects and the spectral distortion still exist in their fusion results due to the insufficiency in details learning and the frequency mismatch between MSand PAN. Therefore, the improvement of spatial details at the premise of reducing spectral distortion is still a challenge. In this paper, we propose a frequency-aware fusion network (FAFNet) together with a novel high-frequency feature similarity loss to address above mentioned problems. FAFNet is mainly composed of two kinds of blocks, where the frequency aware blocks aim to extract features in the frequency domain with the help of discrete wavelet transform (DWT) layers, and the frequency fusion blocks reconstruct and transform the features from frequency domain to spatial domain with the assistance of inverse DWT (IDWT) layers. Finally, the fusion results are obtained through a convolutional block. In order to learn the correspondence, we also propose a high-frequency feature similarity loss to constrain the HF features derived from PAN and MS branches, so that HF features of PAN can reasonably be used to supplement that of MS. Experimental results on three datasets at both reduced- and full-resolution demonstrate the superiority of the proposed method compared with several state-of-the-art pansharpening models.Comment: 14 page

Robust Sparse Mean Estimation via Incremental Learning

In this paper, we study the problem of robust sparse mean estimation, where the goal is to estimate a $k$-sparse mean from a collection of partially corrupted samples drawn from a heavy-tailed distribution. Existing estimators face two critical challenges in this setting. First, they are limited by a conjectured computational-statistical tradeoff, implying that any computationally efficient algorithm needs $\tilde\Omega(k^2)$ samples, while its statistically-optimal counterpart only requires $\tilde O(k)$ samples. Second, the existing estimators fall short of practical use as they scale poorly with the ambient dimension. This paper presents a simple mean estimator that overcomes both challenges under moderate conditions: it runs in near-linear time and memory (both with respect to the ambient dimension) while requiring only $\tilde O(k)$ samples to recover the true mean. At the core of our method lies an incremental learning phenomenon: we introduce a simple nonconvex framework that can incrementally learn the top-$k$ nonzero elements of the mean while keeping the zero elements arbitrarily small. Unlike existing estimators, our method does not need any prior knowledge of the sparsity level $k$. We prove the optimality of our estimator by providing a matching information-theoretic lower bound. Finally, we conduct a series of simulations to corroborate our theoretical findings. Our code is available at https://github.com/huihui0902/Robust_mean_estimation

Design and Performance Analysis of Wireless Legitimate Surveillance Systems with Radar Function

Integrated sensing and communication (ISAC) has recently been considered as a promising approach to save spectrum resources and reduce hardware cost. Meanwhile, as information security becomes increasingly more critical issue, government agencies urgently need to legitimately monitor suspicious communications via proactive eavesdropping. Thus, in this paper, we investigate a wireless legitimate surveillance system with radar function. We seek to jointly optimize the receive and transmit beamforming vectors to maximize the eavesdropping success probability which is transformed into the difference of signal-to-interference-plus-noise ratios (SINRs) subject to the performance requirements of radar and surveillance. The formulated problem is challenging to solve. By employing the Rayleigh quotient and fully exploiting the structure of the problem, we apply the divide-and-conquer principle to divide the formulated problem into two subproblems for two different cases. For the first case, we aim at minimizing the total transmit power, and for the second case we focus on maximizing the jamming power. For both subproblems, with the aid of orthogonal decomposition, we obtain the optimal solution of the receive and transmit beamforming vectors in closed-form. Performance analysis and discussion of some insightful results are also carried out. Finally, extensive simulation results demonstrate the effectiveness of our proposed algorithm in terms of eavesdropping success probability