We propose a method to realize a subjective super-resolution on a high-speed LED display, which dynamically shows a set of four neighboring pixels on every LED pixel. We have experimentally confirmed the subjective super-resolution effect. This paper proposes a subjective super-resolution hypothesis in human visual system and reports simulation results with pseudo fixation eye movements.

Parameter estimation theorems for LFM signals have been developed due to the advantages of fractional Fourier transform (FrFT). The traditional estimation methods in the fractional Fourier domain (FrFD) are almost based on two-dimensional search which have the contradiction between estimation performance and complexity. In order to solve this problem, we introduce the orthogonal matching pursuit (OMP) into the FrFD, propose a modified optimization method to estimate initial frequency and final frequency of fractional bandlimited LFM signals. In this algorithm, the differentiation fractional spectrum which is used to form observation matrix in OMP is derived from the spectrum analytical formulations of the LFM signal, and then, based on that the LFM signal has approximate rectangular spectrum in the FrFD and the correlation between the LFM signal and observation matrix yields a maximal value at the edge of the spectrum (see Sect.3.3 for details), the edge spectrum information can be extracted by OMP. Finally, the estimations of initial frequency and final frequency are obtained through multiplying the edge information by the sampling frequency resolution. The proposed method avoids reconstruction and the traditional peak-searching procedure, and the iterations are needed only twice. Thus, the computational complexity is much lower than that of the existing methods. Meanwhile, Since the vectors at the initial frequency and final frequency points both have larger modulus, so that the estimations are closer to the actual values, better normalized root mean squared error (NRMSE) performance can be achieved. Both theoretical analysis and simulation results demonstrate that the proposed algorithm bears a relatively low complexity and its estimation precision is higher than search-based and reconstruction-based algorithms.

Distributed compressive video sensing (DCVS) has received considerable attention due to its potential in source-limited communication, e.g., wireless video sensor networks (WVSNs). Multi-hypothesis (MH) prediction, which treats the target block as a linear combination of hypotheses, is a state-of-the-art technique in DCVS. The common approach is under the supposition that blocks that are dissimilar from the target block are given lower weights than blocks that are more similar. This assumption can yield acceptable reconstruction quality, but it is not suitable for scenarios with more details. In this paper, based on the joint sparsity model (JSM), the authors present a Tikhonov-regularized MH prediction scheme in which the most similar block provides the similar common portion and the others blocks provide respective unique portions, differing from the common supposition. Specifically, a new scheme for generating hypotheses and a Euclidean distance-based metric for the regularized term are proposed. Compared with several state-of-the-art algorithms, the authors show the effectiveness of the proposed scheme when there are a limited number of hypotheses.

Although correlation filter-based trackers have demonstrated excellent performance for visual object tracking, there remain several challenges to be addressed. In this work, we propose a novel tracker based on the correlation filter framework. Traditional trackers face difficulty in accurately adapting to changes in the scale of the target when the target moves quickly. To address this, we suggest a scale adaptive scheme based on prediction scales. We also incorporate a speed-based adaptive model update method to further improve overall tracking performance. Experiments with samples from the OTB100 and KITTI datasets demonstrate that our method outperforms existing state-of-the-art tracking algorithms in fast motion scenes.

Sparse arrays can usually achieve larger array apertures than uniform linear arrays (ULA) with the same number of physical antennas. However, the conventional direction-of-arrival (DOA) estimation algorithms for sparse arrays usually require the spatial smoothing operation to recover the matrix rank which inevitably involves heavy computational complexity and leads to a reduction in the degrees-of-freedom (DOFs). In this paper, a low-complex DOA estimation algorithm by exploiting the discrete Fourier transform (DFT) is proposed. Firstly, the spatial spectrum of the virtual array constructed from the sparse array is established by exploiting the DFT operation. The initial DOA estimation can obtain directly by searching the peaks in the DFT spectrum. However, since the number of array antennas is finite, there exists spectrum power leakage which will cause the performance degradation. To further improve the angle resolution, an iterative process is developed to suppress the spectrum power leakage. Thus, the proposed algorithm does not require the spatial smoothing operation and the computational complexity is reduced effectively. In addition, due to the extention of DOF with the application of the sparse arrays, the proposed algorithm can resolve the underdetermined DOA estimation problems. The superiority of the proposed algorithm is demonstrated by simulation results.

To efficiently collect sensor readings in cluster-based wireless sensor networks, we propose a structural compressed network coding (SCNC) scheme that jointly considers structural compressed sensing (SCS) and network coding (NC). The proposed scheme exploits the structural compressibility of sensor readings for data compression and reconstruction. Random linear network coding (RLNC) is used to re-project the measurements and thus enhance network reliability. Furthermore, we calculate the energy consumption of intra- and inter-cluster transmission and analyze the effect of the cluster size on the total transmission energy consumption. To that end, we introduce an iterative reweighed sparsity recovery algorithm to address the all-or-nothing effect of RLNC and decrease the recovery error. Experiments show that the SCNC scheme can decrease the number of measurements required for decoding and improve the network's robustness, particularly when the loss rate is high. Moreover, the proposed recovery algorithm has better reconstruction performance than several other state-of-the-art recovery algorithms.

Direction of arrival (DOA) estimation as a fundamental issue in array signal processing has been extensively studied for many applications in military and civilian fields. Many DOA estimation algorithms have been developed for different application scenarios such as low signal-to-noise ratio (SNR), limited snapshots, etc. However, there are still some practical problems that make DOA estimation very difficult. One of them is the correlation between sources. In this paper, we develop a sparsity-based method to estimate the DOA of coherent signals with sparse linear array (SLA). We adopt the off-grid signal model and solve the DOA estimation problem in the sparse Bayesian learning (SBL) framework. By considering the SLA as a ‘missing sensor’ ULA, our proposed method treats the output of the SLA as a partial output of the corresponding virtual uniform linear array (ULA) to make full use of the expanded aperture character of the SLA. Then we employ the expectation-maximization (EM) method to update the hyper-parameters and the output of the virtual ULA in an iterative manner. Numerical results demonstrate that the proposed method has a better performance in correlated signal scenarios than the reference methods in comparison, confirming the advantage of exploiting the extended aperture feature of the SLA.

Many studies have revealed that the performance of software-based Virtual Network Functions (VNFs) is insufficient for mission-critical networks. Scaling-out approaches, such as auto-scaling of VNFs, could handle a huge amount of traffic; however, the exponential traffic growth confronts us the limitations of both expandability of physical resources and complexity of their management. In this paper, we propose a fast datapath processing method called Packet Aggregation Flow (PA-Flow) that is based on hop-by-hop packet aggregation for more efficient Service Function Chaining (SFC). PA-Flow extends a notion of existing intra-node packet aggregation toward network-wide packet aggregation, and we introduce following three novel features. First, packet I/O overheads at intermediate network devices including NFV-nodes are mitigated by reduction of packet amount. Second, aggregated packets are further aggregated as going through the service chain in a hop-by-hop manner. Finally, next-hop aware packet aggregation is realized using OpenFlow-based flow tables. PA-Flow is designed to be available with various VNF forms (e.g. VM/container/baremetal-based) and virtual I/O technologies (e.g. vhost-user/SR-IOV), and its implementation does not bring noticeable delay for aggregation. We conducted two evaluations: (i) a baseline evaluation for understanding fundamental performance characteristics of PA-Flow (ii) a simulation-based SFC evaluation for proving PA-Flow's effect in a realistic environment. The results showed that throughput of short packet forwarding was improved by 4 times. Moreover, the total number of packets was reduced by 93% in a large-scale SFC.

Recently, more and more people start investing. Understanding the factors affecting financial products is important for making investment decisions. However, it is difficult to understand factors for novices because various factors affect each other. Various technique has been studied, but conventional factor analysis methods focus on revealing the impact of factors over a certain period locally, and it is not easy to predict net asset values. As a reasonable solution for the prediction of net asset values, in this paper, we propose a trend shift model for the global analysis of factors by introducing trend change points as shift interference variables into state space models. In addition, to realize the trend shift model efficiently, we propose an effective trend detection method, TP-TBSM (two-phase TBSM), by extending TBSM (trend-based segmentation method). Comparing with TBSM, TP-TBSM could detect trends flexibly by reducing the dependence on parameters. We conduct experiments with eleven investment trust products and reveal the usefulness and effectiveness of the proposed model and method.

MDS transformation plays an important role in resisting against differential cryptanalysis (DC) and linear cryptanalysis (LC). Recently, M. Sajadieh, et al.[15] designed an efficient recursive diffusion layer with Feistel-like structures. Moreover, they obtained an MDS transformation which is related to a linear function and the inverse is as lightweight as itself. Based on this work, we consider one specific form of linear functions to get the diffusion layer with low XOR gates for the hardware implementation by using temporary registers. We give two criteria to reduce the construction space and obtain six new classes of lightweight MDS transformations. Some of our constructions with one bundle-based LFSRs have as low XOR gates as previous best known results. We expect that these results may supply more choices for the design of MDS transformations in the (lightweight) block cipher algorithm.

A pupil function of aperture in image capturing systems is theoretically derived such that one can perfectly reconstruct all-in-focus image through linear filtering of the focal stack. The perfect reconstruction filters are also designed based on the derived pupil function. The designed filters are space-invariant; hence the presented method does not require region segmentation. Simulation results using synthetic scenes shows effectiveness of the derived pupil function and the filters.

Multi-Mouse Quiz (MMQ) is a quiz application based on the Single Display Groupware (SDG)[1] concept through which several users can answer quizzes by sharing a computer to take the quiz in a classroom or any other learning environment. We conducted a practice, where we used the MMQ to support collaborative learning, which was combined with a museum visit. In the previous research, we found that the 3rd-grade children were able to operate the MMQ without any special assistance from the researchers, and that their use of the MMQ was characterized by high engagement[2]. In this study, we also conducted qualitative evaluation in the form of observation data and a free description of the questionnaire; we found that, compared to previous studies, which used MMQ with 6th-grade children, the 3rd-grade were more willing to use body language to express their emotions, and this tendency made the whole class more active. Furthermore, MMQ quiz learning inspired children with reflection perspectives to participate in the museum activity and activities in the computer room.

Semi-bent functions have important applications in cryptography and coding theory. 2-rotation symmetric semi-bent functions are a class of semi-bent functions with the simplicity for efficient computation because of their invariance under 2-cyclic shift. However, no construction of 2-rotation symmetric semi-bent functions with algebraic degree bigger than 2 has been presented in the literature. In this paper, we introduce four classes of 2m-variable 2-rotation symmetric semi-bent functions including balanced ones. Two classes of 2-rotation symmetric semi-bent functions have algebraic degree from 3 to m for odd m≥3, and the other two classes have algebraic degree from 3 to m/2 for even m≥6 with m/2 being odd.

This paper presents an efficient wideband two-dimensional direction-of-arrival (DOA) estimation for an L-shaped microphone array. We propose a way to construct a wideband sample cross-correlation matrix without any process of DOA preliminary estimation, such as beamforming technique, by exploiting sample cross-correlation matrices of two different frequencies for all frequency bins. Subsequently, wideband DOAs can be estimated by using this wideband matrix along with a scheme of estimating DOA in a narrowband subspace method. Therefore, a contribution of our study is providing an alternative framework for recent narrowband subspace methods to estimating the DOA of wideband sources directly. It means that this framework enables cutting-edge techniques in the existing narrowband subspace methods to implement the wideband direction estimation for reducing the computational complexity and facilitating the estimation algorithm. Theoretical analysis and effectiveness of the proposed method are substantiated through numerical simulations and experiments, which are performed in reverberating environments. The results show that performance of the proposed method performs better than others over a range of signal-to-noise ratio with just a few microphones. All these advantages make the proposed method a powerful tool for navigation systems based on acoustic signal processing.

As a research hotspot and difficulty in the field of computer vision, pedestrian detection has been widely used in intelligent driving and traffic monitoring. The popular detection method at present uses region proposal network (RPN) to generate candidate regions, and then classifies the regions. But the RPN produces many erroneous candidate areas, causing region proposals for false positives to increase. This letter uses improved residual attention network to capture the visual attention map of images, then normalized to get the attention score map. The attention score map is used to guide the RPN network to generate more precise candidate regions containing potential target objects. The region proposals, confidence scores, and features generated by the RPN are used to train a cascaded boosted forest classifier to obtain the final results. The experimental results show that our proposed approach achieves highly competitive results on the Caltech and ETH datasets.

This study designs a low-cost portable device that functions as a coaching assistant system which can support table tennis practice. Although deep learning technology is a promising solution to realizing human activity recognition, we propose using cosine similarity in making inferences. Our experiments show that the cosine similarity based inference can be a good alternative to the deep learning based inference for the assistant system when resources are limited.

As a generalization of perfect nonlinear (PN) functions, zero-difference balanced (ZDB) functions play an important role in coding theory, cryptography and communications engineering. Inspired by a foregoing work of Liu et al. [1], we present a class of ZDB functions with new parameters based on the cyclotomy in finite fields. Employing these ZDB functions, we obtain simultaneously optimal constant composition codes and perfect difference systems of sets.

This paper presents high efficiency Class-E and compact Doherty power amplifiers (PAs) with novel harmonics termination for handset applications using a GaAs/InGaP heterojunction bipolar transistor (HBT) process. The novel harmonics termination circuit effectively reduces the insertion loss of the matching circuit, allowing a device with a compact size. The Doherty PA uses a lumped-element transformer which consists of metal-insulator-metal (MIM) capacitors on an IC substrate, a bonding-wire inductor and short micro-strip lines on a printed circuit board (PCB). The fabricated Class-E PA exhibits a power added efficiency (PAE) as high as 69.0% at 1.95GHz and as high as 67.6% at 2.535GHz. The fabricated Doherty PA exhibits an average output power of 25.5dBm and a PAE as high as 50.1% under a 10-MHz band width quadrature phase shift keying (QPSK) 6.16-dB peak-to-average-power-ratio (PAPR) LTE signal at 1.95GHz. The fabricated chip size is smaller than 1mm2. The input and output Doherty transformer areas are 0.5mm by 1.0mm and 0.7mm by 0.7mm, respectively.

A retrodirective system utilizing harmonic reradiation from a rectenna is developed and verified for long-range wireless power transfer applications, such as low-power or battery-less devices and lightweight aerial vehicles. The second harmonic generated by the rectifying circuit is used instead of a pilot signal, and thus an oscillator for creating the pilot signal is not required. The proposed retrodirective system consists of a 2.45 GHz transmitter with a two-element phased array antenna, a 4.9 GHz direction-of-arrival (DoA) estimation system, a phase control system, and a rectenna. The rectenna, consisting of a half-wave dipole antenna, receives microwave power from the 2.45 GHz transmitter and reradiates the harmonic toward the 4.9 GHz DoA estimation system. The rectenna characteristics and experimental demonstrations of the proposed retrodirective system are described. From measurement results, the dc output power pattern for the developed retrodirective system is in good agreement with that obtained using manual beam steering. The measured DoA estimation errors are within the range of -2.4° to 4.8°.

The security of Unmanned Aerial Vehicle (UAV) swarms is threatened by the deployment of anti-UAV systems under complicated environments such as battlefield. Specifically, the faults caused by anti-UAV systems exhibit sparse and compressible characteristics. In this paper, in order to improve the survivability of UAV swarms under complicated environments, we propose a novel multi-abnormality self-detecting and faults location method, which is based on compressed sensing (CS) and takes account of the communication characteristics of UAV swarms. The method can locate the faults when UAV swarms are suffering physical damages or signal attacks. Simulations confirm that the proposed method performs well in terms of abnormalities detecting and faults location when the faults quantity is less than 17% of the quantity of UAVs.