Finding the correspondence between two images of the same object or scene is an active research field in computer vision. This paper develops a rapid and effective Content-based Superpixel Image matching and Stitching (CSIS) scheme, which utilizes the content of superpixel through multi-features fusion technique. Unlike popular keypoint-based matching method, our approach proposes a superpixel internal feature-based scheme to implement image matching. In the beginning, we make use of a novel superpixel generation algorithm based on content-based feature representation, named Content-based Superpixel Segmentation (CSS) algorithm. Superpixels are generated in terms of a new distance metric using color, spatial, and gradient feature information. It is developed to balance the compactness and the boundary adherence of resulted superpixels. Then, we calculate the entropy of each superpixel for separating some superpixels with significant characteristics. Next, for each selected superpixel, its multi-features descriptor is generated by extracting and fusing local features of the selected superpixel itself. Finally, we compare the matching features of candidate superpixels and their own neighborhoods to estimate the correspondence between two images. We evaluated superpixel matching and image stitching on complex and deformable surfaces using our superpixel region descriptors, and the results show that new method is effective in matching accuracy and execution speed.

As a key technology for 5G and beyond, Multi-User Multi-Input Multi-Output (MU-MIMO) can achieve Gbps downlink rate by allowing concurrent transmission from one Access Point (AP) to multiple users. However, the huge overhead of full CSI feedback may overwhelm the gain yielded by beamforming. Although there have been many works on compress CSI to reduce the feedback overhead, the performance of beamforming may decrease because the accuracy of channel state degrades. To address the tradeoff between feedback overhead and accuracy, we present a two-stage Multipath Profile based Feedback protocol (MPF). In the first stage, compared with CSI feedback, the channel state is represented by multipath profile which has a smaller size but is accurate enough for user selection. Meanwhile, we propose an implicit polling scheme to decrease the feedback further. In the second stage, only the selected users send their CSI information to the AP to guarantee the low overhead and accuracy of steering matrix calculation. We implement and evaluate MPF with USRP N210. Experiments show that MPF can outperform alternative schemes in a variety of radio environments.

This manuscript discusses a new indoor positioning method and proposes a multi-distance function trilateration over k-NN fingerprinting method using radio signals. Generally, the strength of radio signals, referred to received signal strength indicator or RSSI, decreases as they travel in space. Our method employs a list of fingerprints comprised of RSSIs to absorb interference between radio signals, which happens around the transmitters and it also employs multiple distance functions for conversion from distance between fingerprints to the physical distance in order to absorb the interference that happens around the receiver then it performs trilateration between the top three closest fingerprints to locate the receiver's current position. An experiment in positioning performance is conducted in our laboratory and the result shows that our method is viable for a position-level indoor positioning method and it could improve positioning performance by 12.7% of positioning error to 0.406 in meter in comparison with traditional methods.

This paper proposes a chirp-BOK modulation scheme for VLF (Very low frequency, 3-30kHz) communication under symmetric alpha-stable (SαS) noise. The atmospheric noise which is the main interference in VLF communication is more accurately characterized as SαS distribution in the previous literatures. Chirp-BOK, one of the chirp spread spectrum (CSS) technologies is widely used for its anti-interference performance and constant envelope properties. However, up-chirp and down-chirp are not strictly orthogonal, the bit error rate (BER) performance of chirp-BOK system is no longer improved with the increase of time-bandwidth product. So in this paper, the influence of non-orthogonal modulation waveform on the system is considered, and the model of the optimal parameters for chirp-BOK is derived from the perspective of minimum BER under gaussian noise and SαS noise respectively. Simulations for chirp-BOK scheme under gaussian noise and SαS noise with different α validate the effectiveness of the proposed method.

This article introduces our investigation on learning state estimation in e-learning on the condition that visual observation and recording of a learner's behaviors is possible. In this research, we examined methods of adaptation for a new learner for whom a small number of ground truth data can be obtained.

The modern radar signals are in a wide frequency space. The receiving bandwidth of the radar reconnaissance receiver should be wide enough to intercept the modern radar signals. The Nyquist folding receiver (NYFR) is a novel wideband receiving architecture and it has a high intercept probability. Chirp signals are widely used in modern radar system. Because of the wideband receiving ability, the NYFR will receive the concurrent multiple chirp signals. In this letter, we propose a novel parameter estimation algorithm for the multiple chirp signals intercepted by single channel NYFR. Compared with the composite NYFR, the proposed method can save receiving resources. In addition, the proposed approach can estimate the parameters of the chirp signals even the NYFR outputs are under frequency aliasing circumstance. Simulation results show the efficacy of the proposed method.

Distributing codes to specific target sensors in order to fix bugs and/or install a new application is an important management task in WSNs (Wireless Sensor Networks). For the energy efficient dissemination of such codes to specific target sensors, it is required to select the minimum required number of forwarders with the fewest control messages. In this paper, we propose a novel RPL (Routing Protocol for Low-power and lossy networks)-based tree construction scheme for target-specific code dissemination, which is called R-TCS. The main idea of R-TCS is that by leveraging the data collection tree created by a standard routing protocol RPL, it is possible to construct the code dissemination tree with the minimum numbers of non-target sensors and control messages. Since by creating a data collection tree each sensor exchanges RPL messages with the root of the tree, every sensor knows which sensors compose its upwards route, i.e. the route towards the root, and downwards route, i.e. the route towards the leaves. Because of these properties, a target sensor can select the upward route that contains the minimum number of non-target sensors. In addition, a sensor whose downward routes do not contain a target sensor is not required to transmit redundant control messages which are related to the code dissemination operation. In this way, R-TCS can reduce the energy consumption which typically happens in other target-specific code dissemination schemes by the transmission of control messages. In fact, various performance evaluation results obtained by means of computer simulations show that R-TCS reduces by at least 50% energy consumption as compared to the other previous known target-specific code dissemination scheme under the condition where ratio of target sensors is 10% of all sensors.

This letter proposes a new superpixel segmentation algorithm based on global similarity and contour region transformation. The basic idea is that pixels surrounded by the same contour are more likely to belong to the same object region, which could be easily clustered into the same superpixel. To this end, we use contour scanning to estimate the global similarity between pixels and corresponded centers. In addition, we introduce pixel's gradient information of contour transform map to enhance the pixel's global similarity to overcome the missing contours in blurred region. Benefited from our global similarity, the proposed method could adherent with blurred and low contrast boundaries. A large number of experiments on BSDS500 and VOC2012 datasets show that the proposed algorithm performs better than traditional SLIC.

In this letter, we propose a novel deep domain-adaptive convolutional neural network (DDACNN) model to handle the challenging cross-corpus speech emotion recognition (SER) problem. The framework of the DDACNN model consists of two components: a feature extraction model based on a deep convolutional neural network (DCNN) and a domain-adaptive (DA) layer added in the DCNN utilizing the maximum mean discrepancy (MMD) criterion. We use labeled spectrograms from source speech corpus combined with unlabeled spectrograms from target speech corpus as the input of two classic DCNNs to extract the emotional features of speech, and train the model with a special mixed loss combined with a cross-entrophy loss and an MMD loss. Compared to other classic cross-corpus SER methods, the major advantage of the DDACNN model is that it can extract robust speech features which are time-frequency related by spectrograms and narrow the discrepancies between feature distribution of source corpus and target corpus to get better cross-corpus performance. Through several cross-corpus SER experiments, our DDACNN achieved the state-of-the-art performance on three public emotion speech corpora and is proved to handle the cross-corpus SER problem efficiently.

Radio signals show small characteristic differences between radio transmitters resulted from their idiosyncratic hardware properties. Based on the parameters estimation of transmitter imperfections, a novel radiometric identification method is presented in this letter. The fingerprint features of the radio are extracted from the mismatches of the modulator and the nonlinearity of the power amplifier, and used to train a support vector machine classifier to identify the class label of a new data. Experiments on real data sets demonstrate the validation of this method.

Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks. Since a trained layered neural network consists of a complex nonlinear relationship between large number of parameters, we failed to understand how they could achieve input-output mappings with a given data set. In this paper, we propose the non-negative task matrix decomposition method, which applies non-negative matrix factorization to a trained layered neural network. This enables us to decompose the inference mechanism of a trained layered neural network into multiple principal tasks of input-output mapping, and reveal the roles of hidden units in terms of their contribution to each principal task.

Homomorphic encryption allows computation over encrypted data, and can be used for delegating computation: data providers encrypt their data and send them to an aggregator, who can then perform computation over the encrypted data on behalf of a client, without the underlying data being exposed to the aggregator. However, since the aggregator is merely a third party, it may be malicious, and in particular, may submit an incorrect aggregation result to the receiver. Ohara et al. (APKC2014) studied secure aggregation of time-series data while enabling the correctness of aggregation to be verified. However, they only provided a concrete construction in the smart metering system and only gave an intuitive argument of security. In this paper, we define verifiable homomorphic encryption (VHE) which generalizes their scheme, and introduce formal security definitions. Further, we formally prove that Ohara et al.'s VHE scheme satisfies our proposed security definitions.

Blob detection is an important part of computer vision and a special case of region detection with important applications in the image analysis. In this paper, the dilation operator in standard mathematical morphology is firstly extended to the order dilation operator of soft morphology, three soft morphological filters are designed by using the operator, and a novel blob detection algorithm called SMBD is proposed on that basis. SMBD had been proven to have better performance of anti-noise and blob shape detection than similar blob filters based on mathematical morphology like Quoit and N-Quoit in terms of theoretical and experimental aspects. Additionally, SMBD was also compared to LoG and DoH in different classes, which are the most commonly used blob detector, and SMBD also achieved significantly great results.

This paper proposes a method for searching for graphs in the database which are contained as subgraphs by a given query. In the proposed method, the search index does not require any knowledge of the query set or the frequent subgraph patterns. In conventional techniques, enumerating and selecting frequent subgraph patterns is computationally expensive, and the distribution of the query set must be known in advance. Subsequent changes to the query set require the frequent patterns to be selected again and the index to be reconstructed. The proposed method overcomes these difficulties through graph coding, using a tree structured index that contains infrequent subgraph patterns in the shallow part of the tree. By traversing this code tree, we are able to rapidly determine whether multiple graphs in the database contain subgraphs that match the query, producing a powerful pruning or filtering effect. Furthermore, the filtering and verification steps of the graph search can be conducted concurrently, rather than requiring separate algorithms. As the proposed method does not require the frequent subgraph patterns and the query set, it is significantly faster than previous techniques; this independence from the query set also means that there is no need to reconstruct the search index when the query set changes. A series of experiments using a real-world dataset demonstrate the efficiency of the proposed method, achieving a search speed several orders of magnitude faster than the previous best.

A novel superpixel segmentation approach driven by uniform mixture model with spatially constrained (UMMS) is proposed. Under this algorithm, each observation, i.e. pixel is first represented as a five-dimensional vector which consists of colour in CLELAB space and position information. And then, we define a new uniform distribution through adding pixel position, so that this distribution can describe each pixel in input image. Applied weighted 1-Norm to difference between pixels and mean to control the compactness of superpixel. In addition, an effective parameter estimation scheme is introduced to reduce computational complexity. Specifically, the invariant prior probability and parameter range restrict the locality of superpixels, and the robust mean optimization technique ensures the accuracy of superpixel boundaries. Finally, each defined uniform distribution is associated with a superpixel and the proposed UMMS successfully implements superpixel segmentation. The experiments on BSDS500 dataset verify that UMMS outperforms most of the state-of-the-art approaches in terms of segmentation accuracy, regularity, and rapidity.

In this paper, we propose the decomposition ring homomorphic encryption scheme, that is a homomorphic encryption scheme built on the decomposition ring, which is a subring of cyclotomic ring. By using the decomposition ring the structure of plaintext slot becomes ℤpl, instead of GF(pd) in conventional schemes on the cyclotomic ring. For homomorphic multiplication of integers, one can use the full of ℤpl slots using the proposed scheme, although in conventional schemes one can use only one-dimensional subspace GF(p) in each GF(pd) slot. This allows us to realize fast and compact homomorphic encryption for integer plaintexts. In fact, our benchmark results indicate that our decomposition ring homomorphic encryption schemes are several times faster than HElib for integer plaintexts due to its higher parallel computation.

In this paper, we propose an acceleration method for the Held-Karp algorithm that solves the symmetric traveling salesman problem by dynamic programming. The proposed method achieves acceleration with two techniques. First, we locate data-independent subproblems so that the subproblems can be solved in parallel. Second, we reduce the number of subproblems by a meet in the middle (MITM) technique, which computes the optimal path from both clockwise and counterclockwise directions. We show theoretical analysis on the impact of MITM in terms of the time and space complexities. In experiments, we compared the proposed method with a previous method running on a single-core CPU. Experimental results show that the proposed method on an 8-core CPU was 9.5-10.5 times faster than the previous method on a single-core CPU. Moreover, the proposed method on a graphics processing unit (GPU) was 30-40 times faster than that on an 8-core CPU. As a side effect, the proposed method reduced the memory usage by 48%.

The windowed interpolation DFT methods have been utilized to estimate the parameters of a single frequency and multi-frequency signal. Nevertheless, they do not work well for the real-valued sinusoids with closely spaced positive- and negative- frequency. In this paper, we describe a novel three-point windowed interpolation DFT method for frequency measurement of real-valued sinusoid signal. The exact representation of the windowed DFT with maximum sidelobe decay window (MSDW) is constructed. The spectral superposition of positive- and negative-frequency is considered and calculated to improve the estimation performance. The simulation results match with the theoretical values well. In addition, computer simulations demonstrate that the proposed algorithm provides high estimation accuracy and good noise suppression capability.

For cross-corpus speech emotion recognition (SER), how to obtain effective feature representation for the discrepancy elimination of feature distributions between source and target domains is a crucial issue. In this paper, we propose a Target-adapted Subspace Learning (TaSL) method for cross-corpus SER. The TaSL method trys to find a projection subspace, where the feature regress the label more accurately and the gap of feature distributions in target and source domains is bridged effectively. Then, in order to obtain more optimal projection matrix, ℓ1 norm and ℓ2,1 norm penalty terms are added to different regularization terms, respectively. Finally, we conduct extensive experiments on three public corpuses, EmoDB, eNTERFACE and AFEW 4.0. The experimental results show that our proposed method can achieve better performance compared with the state-of-the-art methods in the cross-corpus SER tasks.

For image restoration, an image prior that is obtained from the morphological gradient has been proposed. In the field of mathematical morphology, the optimization of the structuring element (SE) used for this morphological gradient using a genetic algorithm (GA) has also been proposed. In this paper, we introduce a new image prior that is the sum of the morphological gradients and total variation for an image restoration problem to improve the restoration accuracy. The proposed image prior makes it possible to almost match the fitness to a quantitative evaluation such as the mean square error. It also solves the problem of the artifact due to the unsuitability of the SE for the image. An experiment shows the effectiveness of the proposed image restoration method.