In the context of compressed sensing (CS), simultaneous orthogonal matching pursuit (SOMP) algorithm is an important iterative greedy algorithm for multiple measurement matrix vectors sharing the same non-zero locations. Restricted isometry property (RIP) of measurement matrix is an effective tool for analyzing the convergence of CS algorithms. Based on the RIP of measurement matrix, this paper shows that for the K-row sparse recovery, the restricted isometry constant (RIC) is improved to $delta_{K+1}<rac{sqrt{4K+1}-1}{2K}$ for SOMP algorithm. In addition, based on this RIC, this paper obtains sufficient conditions that ensure the convergence of SOMP algorithm in noisy case.

Toward the actualization of an automatic navigation system for fetoscopic tracheal occlusion (FETO) surgery, this paper proposes a 3D ultrasound (US) calibration-based approach that can locate the fetal facial surface, oral cavity, and airways by a registration between a 3D fetal model and 3D US images. The proposed approach consists of an offline process and online process. The offline process first reconstructs the 3D fetal model with the anatomies of the oral cavity and airways. Then, a point-based 3D US calibration system based on real-time 3D US images, an electromagnetic (EM) tracking device, and a novel cones' phantom, computes the matrix that transforms the 3D US image space into the world coordinate system. In the online process, by scanning the mother's body with a 3D US probe, 3D US images containing the fetus are obtained. The fetal facial surface extracted from the 3D US images is registered to the 3D fetal model using an ICP-based (iterative closest point) algorithm and the calibration matrices, so that the fetal facial surface as well as the oral cavity and airways are located. The results indicate that the 3D US calibration system achieves an FRE (fiducial registration error) of 1.49±0.44mm and a TRE (target registration error) of 1.81±0.56mm by using 24 fiducial points from two US volumes. A mean TRE of 1.55±0.46 mm is also achieved for measuring location accuracy of the 3D fetal facial surface extracted from 3D US images by 14 target markers, and mean location errors of 2.51±0.47 mm and 3.04±0.59 mm are achieved for indirectly measuring location accuracy of the pharynx and the entrance of the trachea, respectively, which satisfy the requirement of the FETO surgery.

An improved nonlinear circuit model for a GaAs Gunn diode in an oscillator is proposed based on the physical mechanism of the diode. This model interprets the nonlinear harmonic character on the Gunn diode. Its equivalent nonlinear circuit of which can assist in the design of the Gunn oscillator and help in the analysis of the fundamental and harmonic characteristics of the GaAs Gunn diode. The simulation prediction and the experiment of the Gunn oscillator show the feasibility of the nonlinear circuit model for the GaAs Gunn oscillator.

Wireless sensor networks usually deploy sensor nodes with limited energy resources in unattended environments so that people have difficulty in replacing or recharging the depleted devices. In order to balance the energy dissipation and prolong the network lifetime, this paper proposes a routing spanning tree-based clustering algorithm (RSTCA) which uses routing spanning tree to analyze clustering. In this study, the proposed scheme consists of three phases: setup phase, cluster head (CH) selection phase and steady phase. In the setup phase, several clusters are formed by adopting the K-means algorithm to balance network load on the basis of geographic location, which solves the randomness problem in traditional distributed clustering algorithm. Meanwhile, a conditional inter-cluster data traffic routing strategy is created to simplify the networks into subsystems. For the CH selection phase, a novel CH selection method, where CH is selected by a probability based on the residual energy of each node and its estimated next-time energy consumption as a function of distance, is formulated for optimizing the energy dissipation among the nodes in the same cluster. In the steady phase, an effective modification that counters the boundary node problem by adjusting the data traffic routing is designed. Additionally, by the simulation, the construction procedure of routing spanning tree (RST) and the effect of the three phases are presented. Finally, a comparison is made between the RSTCA and the current distributed clustering protocols such as LEACH and LEACH-DT. The results show that RSTCA outperforms other protocols in terms of network lifetime, energy dissipation and coverage ratio.

This paper presents our investigation into improving the performance of our previous automatic reading quality assessment system. The method of the baseline system is calculating the average value of the Phone Log-Posterior Probability (PLPP) of all phones in the voice to be assessed, and the average value is used as the reading quality assessment feature. In this paper, we presents three improvements. First, we cluster the triphones, and then calculate the average value of the normalized PLPP for each classification separately, and use this average values as the multi-dimensional assessment features instead of the original one-dimensional assessment feature. This method is simple but effective, which made the score difference of the machine scoring and manual scoring decrease by 30.2% relatively. Second, in order to assess the reading rhythm, we train Gaussian Mixture Models (GMM), which contain the information of each triphone's relative duration under standard pronunciation. Using the GMM, we can calculate the probability that the relative duration of each phone is conform to the standard pronunciation, and the average value of the probabilities is added to the assessment feature vector as a dimension of feature, which decreased the score difference between the machine scoring and manual scoring by 9.7% relatively. Third, we detect Filled Pauses (FP) by analyzing the formant curve, and then calculate the relative duration of FP, and add the relative duration of FP to the assessment feature vector as a dimension of feature. This method made the score difference between the machine scoring and manual scoring be further decreased by 10.2% relatively. Finally, when the feature vector extracted by the three methods are used together, the score difference between the machine scoring and manual scoring was decreased by 43.9% relatively compared to the baseline system.

Many successful methods for recognizing human action are spatio-temporal interest point (STIP) based methods. Given a test video sequence, for a matching-based method using a voting mechanism, each test STIP casts a vote for each action class based on its mutual information with respect to the respective class, which is measured in terms of class likelihood probability. Therefore, two issues should be addressed to improve the accuracy of action recognition. First, effective STIPs in the training set must be selected as references for accurately estimating probability. Second, discriminative STIPs in the test set must be selected for voting. This work uses ε-nearest neighbors as effective STIPs for estimating the class probability and uses a variance filter for selecting discriminative STIPs. Experimental results verify that the proposed method is more accurate than existing action recognition methods.

Block compressed sensing with random permutation (BCS-RP) has been shown to be very effective for image Encryption-then-Compression (ETC) applications. However, in the BCS-RP scheme, the statistical information of the blocks is disclosed, because the encryption is conducted within each small block of the image. To solve this problem, a two-dimension compressed sensing (2DCS) with 2D random permutation (2DRP) strategy for image ETC applications is proposed in this letter, where the 2DRP strategy is used for encrypting the image and the 2DCS scheme is used for compressing the encrypted image. Compared with the BCS-RP scheme, the proposed approach has two benefits. Firstly, it offers better security. Secondly, it obtains a significant gain of peak signal-to-noise ratio (PSNR) of the reconstructed-images.

A low loss intelligent power module (IPM) that specifically designed for high performance frequency-alterable air conditioner applications is proposed. This IPM utilizes 600 V trench gate field stop insulated gate bipolar transistors (TFS-IGBTs) as the main switching devices to deliver extremely low conduction and switching losses. In addition, 600 V SiC schottky barrier diodes (SBDs) are employed as the freewheeling diodes. Compared to conventional silicon fast recovery diodes (FRDs) SiC SBDs exhibit practically no reverse recovery loss, hence can further reduce the power loss of the IPM. Experimental results reveal that the power loss of the proposed IPM is between 3.5∼21.7 W at different compressor frequencies from 10 to 70 Hz, which achieving up to 12.5%∼25.5% improvement when compared to the state-of-the-art conventional Si-based IGBT IPM.

In this paper, we presented a novel method for automatic pronunciation quality assessment. Unlike the popular “Goodness of Pronunciation” (GOP) method, this method does not map the decoding confidence into pronunciation quality score, but differentiates the different pronunciation quality utterances directly. In this method, the student's utterance need to be decoded for two times. The first-time decoding was for getting the time points of each phone of the utterance by a forced alignment using a conventional trained acoustic model (AM). The second-time decoding was for differentiating the pronunciation quality for each triphone using a specially trained AM, where the triphones in different pronunciation qualities were trained as different units, and the model was trained in discriminative method to ensure the model has the best discrimination among the triphones whose names were same but pronunciation quality scores were different. The decoding network in the second-time decoding included different pronunciation quality triphones, so the phone-level scores can be obtained from the decoding result directly. The phone-level scores were combined into the sentence-level scores using maximum entropy criterion. The experimental results shows that the scoring performance was increased significantly compared to the GOP method, especially in sentence-level.

This paper addresses the optimum routing problem of multipoint connection in large-scale networks. A number of algorithms for routing of multipoint connection have been studied so far, most of them, however, assume the availability of complete network information. Herein, we study the problem under the condition that only partial information is available to routing nodes and that routing decision is carried out in a distributed cooperative manner. We consider the network being partitioned into clusters and propose a cluster-based routing approach for multipoint connection. Some basic principles for network clustering are discussed first. Next, the original multipoint routing problem is defined and is divided into two types of subproblems. The global optimum multicast tree then can be obtained asymptotically by solving the subproblems one after another iteratively. We propose an algorithm and evaluate it with computer simulations. By measuring the running time of the algorithm and the optimality of resultant multicast tree, we show analysis on the convergent property with varying network cluster sizes, multicast group sizes and network sizes. The presented approach has two main characteristics, 1) it can yield asymptotical optimum solutions for the routing of multipoint connection, and 2) the routing decisions can be made in the environment where only partial information is available to routing nodes.

Block compressed sensing (CS) with optimal permutation is a promising method to improve sampling efficiency in CS-based image compression. However, the existing optimal permutation scheme brings a large amount of extra data to encode the permutation information because it needs to know the permutation information to accomplish signal reconstruction. When the extra data is taken into consideration, the improvement in sampling efficiency of this method is limited. In order to solve this problem, a new optimal permutation strategy for block CS (BCS) is proposed. Based on the proposed permutation strategy, an improved optimal permutation based BCS method called BCS-NOP (BCS with new optimal permutation) is proposed in this paper. Simulation results show that the proposed approach reduces the amount of extra data to encode the permutation information significantly and thereby improves the sampling efficiency compared with the existing optimal permutation based BCS approach.

Spectrum sensing is the first task performed by cognitive radio (CR) networks. In this paper we propose a spectrum sensing algorithm for orthogonal frequency division multiplex (OFDM) signal based on deep learning and covariance matrix graph. The advantage of deep learning in image processing is applied to the spectrum sensing of OFDM signals. We start by building the spectrum sensing model of OFDM signal, and then analyze structural characteristics of covariance matrix (CM). Once CM has been normalized and transformed into a gray level representation, the gray scale map of covariance matrix (GSM-CM) is established. Then, the convolutional neural network (CNN) is designed based on the LeNet-5 network, which is used to learn the training data to obtain more abstract features hierarchically. Finally, the test data is input into the trained spectrum sensing network model, based on which spectrum sensing of OFDM signals is completed. Simulation results show that this method can complete the spectrum sensing task by taking advantage of the GSM-CM model, which has better spectrum sensing performance for OFDM signals under low SNR than existing methods.

This paper studies the routing algorithms for multi-destination connections where each destination may require different amount of data streams. This asymmetric feature can arise mostly in a large and/or heterogeneous network environment. There are mainly two reasons for this. One is that terminal equipments may have different capabilities. The other is that users may have various interests in the same set of information. We first define the asymmetric multicast problem and describe an original routing method for this type of multicast. The method is then employed in the presented routing algorithms, which can be run in multi-cluster environment. The multi-cluster architecture is considered to be effective for running routing in the networks, where a variety of operating methods might be applied in different clusters but global network performance is required. Our algorithms are designed based on some classical Steiner tree heuristics. The basic goal of our algorithms is to make routing decisions for the asymmetric multicast connections with minimum-cost purpose. In addition, we also consider delay constraint requirements in the multicast connections and propose correspondent algorithms. We compare the performance between SPT (Shortest Path Tree)-based algorithms and the presented algorithms by simulations. We show that performance difference exists among the different types of the algorithms.

In compressed sensing, most previous researches have studied the recovery performance of a sparse signal x based on the acquired model y=Φx+n, where n denotes the noise vector. There are also related studies for general perturbation environment, i.e., y=(Φ+E)x+n, where E is the measurement perturbation. IHT and HTP algorithms are the classical algorithms for sparse signal reconstruction in compressed sensing. Under the general perturbations, this paper derive the required sufficient conditions and the error bounds of IHT and HTP algorithms.

In this study, the design and fabrication of a 110–140-GHz varistor mode frequency tripler made with four Schottky diodes pair are presented. Nonlinear simulations were performed to calculate the optimum diode embedding impedance and the required input power. A compact microstrip resonant cell (CMRC) filter was introduced for the first time in submillimeter multiplier, instead of the traditional low-and-high impedance microstrip filter. The shorter size and the wider stop band of the CMRC filter improved the performance of the tripler. The tripler exhibited the best conversion efficiency of 5.2% at 129 GHz and peak output power of 5.3 mW at 125 GHz. Furthermore, within the output bandwidth from 110 to 140 GHz, the conversion efficiency was greater than 1.5%.