In this paper, the distributed cooperative communication of unmanned aerial vehicles (UAVs) is studied, where the condition number (CN) and the inner product (InP) are used to measure the quality of communication links. By optimizing the relative position of UAVs, large channel capacity and stable communication links can be obtained. Using the spherical wave model under the line of sight (LOS) channel, CN expression of the channel matrix is derived when there are Nt transmitters and two receivers in the system. In order to maximize channel capacity, we derive the UAVs position constraint equation (UAVs-PCE), and the constraint between BS elements distance and carrier wavelength is analyzed. The result shows there is an area where no matter how the UAVs' positions are adjusted, the CN is still very large. Then a special scenario is considered where UAVs form a rectangular lattice array, and the optimal constraint between communication distance and UAVs distance is derived. After that, we derive the InP of channel matrix and the gradient expression of InP with respect to UAVs' position. The particle swarm optimization (PSO) algorithm is used to minimize the CN and the gradient descent (GD) algorithm is used to minimize the InP by optimizing UAVs' position iteratively. Both of the two algorithms present great potentials for optimizing the CN and InP respectively. Furthermore, a hybrid algorithm named PSO-GD combining the advantage of the two algorithms is proposed to maximize the communication capacity with lower complexity. Simulations show that PSO-GD is more efficient than PSO and GD. PSO helps GD to break away from local extremum and provides better positions for GD, and GD can converge to an optimal solution quickly by using the gradient information based on the better positions. Simulations also reveal that a better channel can be obtained when those parameters satisfy the UAVs position constraint equation (UAVs-PCE), meanwhile, theory analysis also explains the abnormal phenomena in simulations.

Triboelectric generator is attracting much attention as a power source of electronics application. Electromotive force induced by rubbing is a key for triboelectric generator. From dielectric physics point of view, there are two microscopic origins for electromotive force, i.e., electronic charge displacement and dipolar rotation. A new way for evaluating these two origins is an urgent task. We have been developing an optical second-harmonic generation (SHG) technique as a tool for probing charge displacement and dipolar alignment, selectively, by utilizing wavelength dependent response of SHG to the two origins. In this paper, an experimental way that identifies polarity of electronic charge displacement, i.e., positive charge and negative charge, is proposed. Results showed that the use of local oscillator makes it possible to identify the polarity of charges by means of SHG. As an example, positive and negative charge distribution created by rubbing polyimide surface is illustrated.

Loading test vectors and unloading test responses in shift mode during scan testing cause many scan flip-flops to switch simultaneously. The resulting shift switching activity around scan flip-flops can cause excessive local IR-drop that can change the states of some scan flip-flops, leading to test data corruption. A common approach solving this problem is partial-shift, in which multiple scan chains are formed and only one group of the scan chains is shifted at a time. However, previous methods based on this approach use random grouping, which may reduce global shift switching activity, but may not be optimized to reduce local shift switching activity, resulting in remaining high risk of test data corruption even when partial-shift is applied. This paper proposes novel algorithms (one optimal and one heuristic) to group scan chains, focusing on reducing local shift switching activity around scan flip-flops, thus reducing the risk of test data corruption. Experimental results on all large ITC'99 benchmark circuits demonstrate the effectiveness of the proposed optimal and heuristic algorithms as well as the scalability of the heuristic algorithm.

SPHINCS+ is a state-of-the-art post-quantum hash-based signature that is a candidate for the NIST post-quantum cryptography standard. For a target bit security, SPHINCS+ supports many different tradeoffs between the signature size and the signing speed. SPHINCS+ provides 6 parameter sets: 3 parameter sets for size optimization and 3 parameter sets for speed optimization. We propose new parameter sets with better performance. Specifically, SPHINCS+ implementations with our parameter sets are up to 26.5% faster with slightly shorter signature sizes.

Source retrieval is the primary task of plagiarism detection. It searches the documents that may be the sources of plagiarism to a suspicious document. The state-of-the-art approaches usually rely on the classical information retrieval models, such as the probability model or vector space model, to get the plagiarism sources. However, the goal of source retrieval is to obtain the source documents that contain the plagiarism parts of the suspicious document, rather than to rank the documents relevant to the whole suspicious document. To model the “partial matching” between documents, this paper proposes a Partial Matching Convolution Neural Network (PMCNN) for source retrieval. In detail, PMCNN exploits a sequential convolution neural network to extract the plagiarism patterns of contiguous text segments. The experimental results on PAN 2013 and PAN 2014 plagiarism source retrieval corpus show that PMCNN boosts the performance of source retrieval significantly, outperforming other state-of-the-art document models.

This paper reports the evolution and application potential of image sensors with high-speed brightness gradient sensors. We propose an adaptive exposure time control method using the apparent motion estimated by this sensor, and evaluate results for the change in illuminance and global / local motion.

In this study, a novel decoupling method using parasitic elements (PEs) connected by a bridge line (BL) for two planar inverted-F antennas (PIFAs) is proposed. The proposed method is developed from a well-known decoupling method that uses a BL to directly connect antenna elements. When antenna elements are connected directly by a BL, strong mutual coupling can be reduced, but the resonant frequency shifts to a different frequency. Hence, to shift the resonant frequency toward the desired frequency, the original size of the antenna elements must be adjusted. This is disadvantageous if the method is applied in cases where the design conditions render it difficult to connect the antennas directly or adjust the original antenna size. Therefore, to easily reduce mutual coupling in such a case, a decoupling method that does not require both connecting antennas directly and adjusting the original antenna size is necessitated. This study demonstrates that using PEs connected by a BL reduces the mutual coupling from -6.6 to -14.1dB, and that the resonant frequency is maintained at the desired frequency (2.0GHz) without having to adjust the original PIFAs size. In addition, impedance matching can be adjusted to the desired frequency, resulting in an improved total antenna efficiency from 77.4% to 94.6%. This method is expected to be a simple and effective approach for reducing the mutual coupling between larger numbers of PIFA elements in the future.

To date, many studies have employed clustering for the classification of unlabeled data. Deep separate clustering applies several deep learning models to conventional clustering algorithms to more clearly separate the distribution of the clusters. In this paper, we employ a convolutional autoencoder to learn the features of input images. Following this, k-means clustering is conducted using the encoded layer features learned by the convolutional autoencoder. A center loss function is then added to aggregate the data points into clusters to increase the intra-cluster homogeneity. Finally, we calculate and increase the inter-cluster separability. We combine all loss functions into a single global objective function. Our new deep clustering method surpasses the performance of existing clustering approaches when compared in experiments under the same conditions.

Currently, the IEEE 802.11n wireless local-area network (WLAN) has been extensively deployed world-wide. For the efficient channel assignment to access-points (APs) from the limited number of partially overlapping channels (POCs) at 2.4GHz band, we have studied the throughput drop estimation model for concurrently communicating links using the channel bonding (CB). However, non-CB links should be used in dense WLANs, since the CB links often reduce the transmission capacity due to high interferences from other links. In this paper, we examine the throughput drop estimation model for concurrently communicating links without using the CB in 802.11n WLAN, and its application to the POC assignment to the APs. First, we verify the model accuracy through experiments in two network fields. The results show that the average error is 9.946% and 6.285% for the high and low interference case respectively. Then, we verify the effectiveness of the POC assignment to the APs using the model through simulations and experiments. The results show that the model improves the smallest throughput of a host by 22.195% and the total throughput of all the hosts by 22.196% on average in simulations for three large topologies, and the total throughput by 12.89% on average in experiments for two small topologies.

This paper proposes a parallel peak cancellation (PC) process for the computational complexity-efficient algorithm called PC with a channel-null constraint (PCCNC) in the adaptive peak-to-average power ratio (PAPR) reduction method using the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. By simultaneously adding multiple PC signals to the time-domain transmission signal vector, the required number of iterations of the iterative algorithm is effectively reduced along with the PAPR. We implement a constraint in which the PC signal is transmitted only to the null space in the MIMO channel by beamforming (BF). By doing so the data streams do not experience interference from the PC signal on the receiver side. Since the fast Fourier transform (FFT) and inverse FFT (IFFT) operations at each iteration are not required unlike the previous algorithm and thanks to the newly introduced parallel processing approach, the enhanced PCCNC algorithm reduces the required total computational complexity and number of iterations compared to the previous algorithms while achieving the same throughput-vs.-PAPR performance.

This work presents a DNN accelerator architecture specifically designed for performing efficient inference on compressed and sparse DNN models. Leveraging the data sparsity, a runtime processing scheme is proposed to deal with the encoded weights and activations directly in the compressed domain without decompressing. Furthermore, a new data flow is proposed to facilitate the reusage of input activations across the fully-connected (FC) layers. The proposed design is implemented and verified using the Xilinx Virtex-7 FPGA. Experimental results show it achieves 1.99×, 1.95× faster and 20.38×, 3.04× more energy efficient than CPU and mGPU platforms, respectively, running AlexNet.

This paper presents a method to optimize 2-D sparse array configurations along with a technique to interpolate holes to accurately estimate the direction of arrival (DOA). Conventional 2-D sparse arrays are often defined using a closed-form representation and have the property that they can create hole-free difference co-arrays that can estimate DOAs of incident signals that outnumber the physical elements. However, this property restricts the array configuration to a limited structure and results in a significant mutual coupling effect between consecutive sensors. In this paper, we introduce an optimization-based method for designing 2-D sparse arrays that enhances flexibility of array configuration as well as DOA estimation accuracy. We also propose a method to interpolate holes in 2-D co-arrays by nuclear norm minimization (NNM) that permits holes and to extend array aperture to further enhance DOA estimation accuracy. The performance of the proposed optimum arrays is evaluated through numerical examples.

Deep learning is playing an increasingly important role in signal processing field due to its excellent performance on many inference problems. Parametric bilinear generalized approximate message passing (P-BiG-AMP) is a new approximate message passing based approach to a general class of structure-matrix bilinear estimation problems. In this letter, we propose a novel feed-forward neural network architecture to realize P-BiG-AMP methodology with deep learning for the inference problem of compressive sensing under matrix uncertainty. Linear transforms utilized in the recovery process and parameters involved in the input and output channels of measurement are jointly learned from training data. Simulation results show that the trained P-BiG-AMP network can achieve higher reconstruction performance than the P-BiG-AMP algorithm with parameters tuned via the expectation-maximization method.

This paper proposes a high-quality computed tomography (CT) image reconstruction method from low-dose X-ray projection data. A state-of-the-art method, proposed by Xu et al., exploits dictionary learning for image patches. This method generates an overcomplete dictionary from patches of standard-dose CT images and reconstructs low-dose CT images by minimizing the sum of a data fidelity and a regularization term based on sparse representations with the dictionary. However, this method does not take characteristics of each patch, such as textures or edges, into account. In this paper, we propose to classify all patches into several classes and utilize an individual dictionary with an individual regularization parameter for each class. Furthermore, for fast computation, we introduce the orthogonality to column vectors of each dictionary. Since similar patches are collected in the same cluster, accuracy degradation by the orthogonality hardly occurs. Our simulations show that the proposed method outperforms the state-of-the-art in terms of both accuracy and speed.

Sparse modeling is one of the most active research areas in engineering and science. The technique provides solutions from far fewer samples exploiting sparsity, that is, the majority of the data are zero. This paper reviews sparse modeling in radio techniques. The first half of this paper introduces direction-of-arrival (DOA) estimation from signals received by multiple antennas. The estimation is carried out using compressed sensing, an effective tool for the sparse modeling, which produces solutions to an underdetermined linear system with a sparse regularization term. The DOA estimation performance is compared among three compressed sensing algorithms. The second half reviews channel state information (CSI) acquisitions in multiple-input multiple-output (MIMO) systems. In time-varying environments, CSI estimated with pilot symbols may be outdated at the actual transmission time. We describe CSI prediction based on sparse DOA estimation, and show excellent precoding performance when using the CSI prediction. The other topic in the second half is sparse Bayesian learning (SBL)-based channel estimation. A base station (BS) has many antennas in a massive MIMO system. A major obstacle for using the massive MIMO system in frequency-division duplex mode is an overhead for downlink CSI acquisition because we need to send many pilot symbols from the BS and to get the feedback from user equipment. An SBL-based channel estimation method can mitigate this issue. In this paper, we describe the outline of the method, and show that the technique can reduce the downlink pilot symbols.

The PCHS (Park-Chang-Hong-Seo) algorithm is a varied Karatsuba algorithm (KA) that utilizes a different splitting strategy with no overlap module. Such an algorithm has been applied to develop efficient hybrid GF(2m) multipliers for irreducible trinomials and pentanomials. However, compared with KA-based hybrid multipliers, these multipliers usually match space complexity but require more gates delay. In this paper, we proposed a new design of hybrid multiplier using PCHS algorithm for irreducible all-one polynomial. The proposed scheme skillfully utilizes redundant representation to combine and simplify the subexpressions computation, which result in a significant speedup of the implementation. As a main contribution, the proposed multiplier has exactly the same space and time complexities compared with the KA-based scheme. It is the first time to show that different splitting strategy for KA also can develop the same efficient multiplier.

Sparse orthogonal matrices are proposed to improve the convergence property of expectation propagation (EP) for sparse signal recovery from compressed linear measurements subject to known dense and ill-conditioned multiplicative noise. As a typical problem, this letter addresses generalized spatial modulation (GSM) in over-loaded and spatially correlated multiple-input multiple-output (MIMO) systems. The proposed sparse orthogonal matrices are used in precoding and constructed efficiently via a generalization of the fast Walsh-Hadamard transform. Numerical simulations show that the proposed sparse orthogonal precoding improves the convergence property of EP in over-loaded GSM MIMO systems with known spatially correlated channel matrices.

Part based models have been proved to be beneficial for person re-identification (Re-ID) in recent years. Existing models usually use fixed horizontal stripes or rely on human keypoints to get each part, which is not consistent with the human visual mechanism. In this paper, we propose a Self-Channel Attention Weighted Part model (SCAWP) for Re-ID. In SCAWP, we first learn a feature map from ResNet50 and use 1x1 convolution to reduce the dimension of this feature map, which could aggregate the channel information. Then, we learn the weight map of attention within each channel and multiply it with the feature map to get each part. Finally, each part is used for a special identification task to build the whole model. To verify the performance of SCAWP, we conduct experiment on three benchmark datasets, including CUHK03-NP, Market-1501 and DukeMTMC-ReID. SCAWP achieves rank-1/mAP accuracy of 70.4%/68.3%, 94.6%/86.4% and 87.6%/76.8% on three datasets respectively.

To reduce peak-to-average power ratio, we propose a method of choosing suitable vectors in a partial transmit sequence technique. Conventional approaches require that a suitable vector be selected from a large number of candidates. By contrast, our method does not include such a selecting procedure, and instead generates random vectors from the Gaussian distribution whose covariance matrix is a solution of a relaxed problem. The suitable vector is chosen from the random vectors. This yields lower peak-to-average power ratio than a conventional method.

Let G11 (resp., G12) be the ternary Golay code of length 11 (resp., 12). In this letter, we investigate the separating redundancies of G11 and G12. In particular, we determine the values of sl(G11) for l = 1, 3, 4 and sl(G12) for l = 1, 4, 5, where sl(G11) (resp., sl(G12)) is the l-th separating redundancy of G11 (resp., G12). We also provide lower and upper bounds on s2(G11), s2(G12), and s3(G12).