This paper first formulates the optimal instantaneous resource allocation, including path selection, power allocation and subchannel scheduling with proportional fairness in MIMO, OFDMA and relay-enhanced network. The joint optimization problem is a NP-hard one with non-linear constraints. To simplify this problem, we first propose a water-filling method named 'CP-AP w PF' to adaptively allocate power only among transmitting antennas. Then, a modified iterative water-filling algorithm named 'AP-AP w PF' is proposed to achieve adaptive power allocation on each subchannel by using the Jensen's inequality. Simulation shows that 'AP-AP w PF' algorithm improves the throughput for cell-edge users, and achieve a tradeoff between maximizing system throughput and assuring individual QoS.

To support mobile multicasting in wireless networks, we present a new active multicasting mechanism which makes use of the state characteristic of multicast agent. In this mechanism, a multicast agent just locates the position for roaming hosts when it does not forward multicast packets. Upon reception of multicast packets, the multicast agent adjusts the service range to achieve an appropriate balance between routing efficiency and the overhead of multicast tree reconstruction. Therefore, a lot of unnecessary tree reconstructions are eliminated during the time when none multicast packet is transferred and multicast delivery path is near optimal because of the limited service range of multicast agent in the active state. To justify the effectiveness of our proposed scheme, we develop an analytical model to evaluate the signaling overhead. Our performance analysis shows that the proposed scheme can significantly reduce the system overhead and multicast routing is near optimal. The other important contribution is the novel analytical approach in evaluating the performance of mobile multicast routing protocol.

This work investigates the cell range expansion (CRE) possible with time-domain multiplexing inter-cell interference coordination (TDM ICIC) in heterogeneous cellular networks (HCN). CRE is proposed to enable a user to connect to a picocell even when it is not the cell with the strongest received power. However, the users in the expanded region suffer severe interference from the macrocells. To alleviate the cross-tier interference, TDM ICIC is proposed to improve the SIR of pico users. In contrast to previous studies on CRE with TDM ICIC, which rely mostly on simulations, we give theoretical analysis results for different types of users in HCN with CRE and TDM ICIC under the Poisson Point Process (PPP) model, especially for the users in the expanded region of picocells. We analyze the outage probability and average ergodic rate based on the connect probability and statistical distance we obtain in advance. Furthermore, we analyze the optimal ratio of almost blank subframes (ABS) and bias factor of picocells in terms of the network fairness, which is useful in the parameter design of a two-tier HCN.

The measurement accuracy of frequency difference of arrival (FDOA) is usually determinant for emitters location system using rapidly moving receivers. The classic technique of expanding the integration time of the cross ambiguity function (CAF) to achieve better performance of FDOA is likely to incur a significant computational burden especially for wideband signals. In this paper, a nonconsecutive short-time CAF's methods is proposed with expansion of root mean square (RMS) integration time, instead of the integration time, and a factor of estimation precision improvement is given which is relative to the general consecutive method. Furthermore, by analyzing the characteristic of coherent CAF and the influence of FDOA rate, an upper bound of the precision improvement factor is derived. Simulation results are provided to confirm the effectiveness of the proposed method.

The compressive sensing (CS) theory has been widely used in synthetic aperture radar (SAR) imaging for its ability to reconstruct image from an extremely small set of measurements than what is generally considered necessary. Because block-based CS approaches in SAR imaging always cause block boundaries between two adjacent blocks, resulting in namely the block artefacts. In this paper, we propose a weighted overlapped block-based compressive sensing (WOBCS) method to reduce the block artefacts and accomplish SAR imaging. It has two main characteristics: 1) the strategy of sensing small and recovering big and 2) adaptive weighting technique among overlapped blocks. This proposed method is implemented by the well-known CS recovery schemes like orthogonal matching pursuit (OMP) and BCS-SPL. Promising results are demonstrated through several experiments.

Spatial multiplexing (SM) offers a linear increase in transmission rate without bandwidth expansion or power increase. In SM systems, the LMMSE receiver establishes a good tradeoff between the complexity and performance. The performance of the LMMSE receiver would be degraded by MIMO channel estimation errors. This letter focus on obtaining the asymptotic convergence of output interference power and SIR performance for the LMMSE receiver with channel uncertainty. Exactly matched simulation results verify the validity of analysis in the large-system assumption. Furthermore, we find that the analytical results are also valid in the sense of average results for limited-scale system in spite of the asymptotic assumption used in derivation.

A text-dependent i-vector extraction scheme and a lexicon-based binary vector (L-vector) representation are proposed to improve the performance of text-dependent speaker verification. I-vector and L-vector are used to represent the utterances for enrollment and test. An improved cosine distance kernel is constructed by combining i-vector and L-vector together and is used to distinguish both speaker identity and lexical (or text) diversity with back-end support vector machine (SVM). Experiments are conducted on RSR 2015 Corpus part 1 and part 2, the results indicate that at most 30% improvement can be obtained compared with traditional i-vector baseline.

Gate delay evaluation is always a vital concern for high-performance digital VLSI designs. As the feature size of VLSIs decreases to the nano-meter region, the work to obtain an accurate gate delay value becomes more difficult and time consuming than ever. The conventional methods usually use iterative algorithms to ensure the accuracy of the effective capacitance Ceff, which is usually used to compute the gate delay with interconnect loads and to capture the output signal shape of the real gate response. Accordingly, the efficiency is sacrificed. In this paper, an accurate and efficient approach is proposed for gate delay estimation. With the linear relationship of gate output time points and Ceff, a polynomial approximation is used to make the nonlinear effective capacitance equation be solved without iterative method. Compared to the conventional methods, the proposed method improves the efficiency of gate delay calculation. Meanwhile, experimental results show that the proposed method is in good agreement with SPICE results and the average error is 2.8%.

A new circuit design of Josephson ternary δ-gate composed of Josephson junction devices is presented. Mathematical theory for synthesizing, analyzing, and realizing any given function in ternary system using Josephson ternary δ-gate is introduced. The Josephson ternary δ-gate is realized using SQUID technique. Circuit simulation results using J-SPICE demonstrated the feasibility and the reliability operations of Josephson ternary δ-gate with very high performances for both speed and power consumption (max. propagation delay time44 ps and max. power consumption2.6µW). The Josephson ternary δ-gate forms a complete set (completeness) with the ternary constants (1, 0, 1). The number of SQUIDs that are needed to perform the operation of δ-gate is 6. Different design with less than 6 SQUIDs is not possible because it can not perform the operation of δ-gate. The advantages of Josephson ternary δ-gate compared with different Josephson logic circuits are as follows: The δ-gate has the property that a simple realization to any given ternary logic function as the building blocks can be achieved. The δ-gate has simple construction with small number of SQUIDs. The δ-gate can realize a large number of ternary functions with small number of input/output pins. The performances of δ-gate is very high, very low power consumption and ultra high speed switching operation.

In this paper, a nanopower supply-insensitive complementary metal-oxide-semiconductor (CMOS) unit threshold voltage (Vth) extractor circuit is proposed. It meets the contemporary industry demand for portable devices that operate with very low power consumption and small output sensitivity. An α times Vth (αVth) extractor is also described, in which α varies continuously. Both incremental and decremental αVth voltages are obtained. A post-layout simulation results using HSPICE with CMOS 0.18um process show that the proposed unit Vth extractor consumes 265nW of power given a 1.6V power supply. Sensitivity to temperature is 0.022%/°C ranging from 0°C to 100°C. Sensitivity to supply voltage is 0.027%/V.

This paper investigates dynamic spectrum access based on MAC-Layer spectrum sensing and prior channel pre-allocation strategy. We first combine channel utilization with channel state transition probability from idle to busy to reflect the channel opportunity quality in cognitive radio systems. Then a MAC-Layer spectrum sensing algorithm based on Channel Opportunity Quality Descending Order (COQDO) is proposed for the single secondary user scenario, so that the single secondary user can be provided with dynamic spectrum access. For the multi-secondary users scenario, in order to solve the channel collision problem among secondary users in dynamic spectrum access, a joint MAC-Layer spectrum sensing and prior channel pre-allocation algorithm is proposed and analyzed. Channel collision problem occurs when more than one secondary users detect the channel as idle and access it at the same time. Furthermore, the prior channel pre-allocation is optimized by using the conventional Color Sensitive Graph Coloring (CSGC) algorithm. Extensive simulation results are presented to compare our proposed algorithms with existing algorithms in terms of idle channel search delay and accumulated channel handoff delay.

In this letter, a micro-expression recognition method is investigated by integrating both spatio-temporal facial features and a regression model. To this end, we first perform a multi-scale facial region division for each facial image and then extract a set of local binary patterns on three orthogonal planes (LBP-TOP) features corresponding to divided facial regions of the micro-expression videos. Furthermore, we use GSLSR model to build the linear regression relationship between the LBP-TOP facial feature vectors and the micro expressions label vectors. Finally, the learned GSLSR model is applied to the prediction of the micro-expression categories for each test micro-expression video. Experiments are conducted on both CASME II and SMIC micro-expression databases to evaluate the performance of the proposed method, and the results demonstrate that the proposed method is better than the baseline micro-expression recognition method.

In this letter we introduce new generalized cyclotomic sequences of order two and length pq firstly, then we determine the linear complexity and autocorrelation values of these sequences. Our results show that these sequences are rather good from the linear complexity viewpoint.

Controlled optimization of phases (COP) is a core implementation in the future intelligent traffic signal system (I-SIG), which has been deployed and tested in countries including the U.S. and China. In such a system design, optimal signal control depends on dynamic traffic situation awareness via connected vehicles. Unfortunately, I-SIG suffers data spoofing from any hacked vehicle; in particular, the spoofing of the last vehicle can break the system and cause severe traffic congestion. Specifically, coordinated attacks on multiple intersections may even bring cascading failure of the road traffic network. To mitigate this security issue, a blockchain-based multi-intersection joint defense mechanism upon COP planning is designed. The major contributions of this paper are the following. 1) A blockchain network constituted by road-side units at multiple intersections, which are originally distributed and decentralized, is proposed to obtain accurate and reliable spoofing detection. 2) COP-oriented smart contract is implemented and utilized to ensure the credibility of spoofing vehicle detection. Thus, an I-SIG can automatically execute a signal planning scheme according to traffic information without spoofing data. Security analysis for the data spoofing attack is carried out to demonstrate the security. Meanwhile, experiments on the simulation platform VISSIM and Hyperledger Fabric show the efficiency and practicality of the blockchain-based defense mechanism.

In this work, we present one novel rust detection method based upon one-class classification and L2 sparse representation (SR) with decision fusion. Firstly, a new color contrast descriptor is proposed for extracting the rust features of steel structure images. Considering that the patterns of rust features are more simplified than those of non-rust ones, one-class support vector machine (SVM) classifier and L2 SR classifier are designed with these rust image features, respectively. After that, a multiplicative fusion rule is advocated for combining the one-class SVM and L2 SR modules, thereby achieving more accurate rust detecting results. In the experiments, we conduct numerous experiments, and when compared with other developed rust detectors, the presented method can offer better rust detecting performances.

Knowledge distillation approaches can transfer information from a large network (teacher network) to a small network (student network) to compress and accelerate deep neural networks. This paper proposes a novel knowledge distillation approach called multi-knowledge distillation (MKD). MKD consists of two stages. In the first stage, it employs autoencoders to learn compact and precise representations of the feature maps (FM) from the teacher network and the student network, these representations can be treated as the essential of the FM, i.e., EFM. In the second stage, MKD utilizes multiple kinds of knowledge, i.e., the magnitude of individual sample's EFM and the similarity relationships among several samples' EFM to enhance the generalization ability of the student network. Compared with previous approaches that employ FM or the handcrafted features from FM, the EFM learned from autoencoders can be transferred more efficiently and reliably. Furthermore, the rich information provided by the multiple kinds of knowledge guarantees the student network to mimic the teacher network as closely as possible. Experimental results also show that MKD is superior to the-state-of-arts.

To accurately evaluate and manage future distributed wireless networks, it is indispensable to fully understand cooperative propagation channels. In this contribution, we propose cascaded multi-keyhole channel models for analyzing cooperative diversity wireless communications. The cascaded Wishart distribution is adopted to investigate the eigenvalue distribution of the multi-keyhole MIMO (multiple input multiple output) channel matrix, and the capacity performance is also presented for the wireless systems over such channels. A diversity order approximation method is proposed for better evaluating the eigenvalue and capacity distributions. The good match of analytical derivations and numerical simulations validates the proposed models and analysis methods. The proposed models can provide an important reference for the optimization and management of cooperative diversity wireless networks.

Mobility load balancing (MLB) is a key technology for self-organization networks (SONs). In this paper, we explore the mobility load balancing problem and propose a unified cell specific offset adjusting algorithm (UCSOA) which more accurately adjusts the largely uneven load between neighboring cells and is easily implemented in practice with low computing complexity and signal overhead. Moreover, we evaluate the UCSOA algorithm in two different traffic conditions and prove that the UCSOA algorithm can get the lower call blocking rates and handover failure rates. Furthermore, the interdependency of the proposed UCSOA algorithm's performance and that of the inter-cell interference coordination (ICIC) algorithm is explored. A self-organization soft frequency reuse scheme is proposed. It demonstrates UCSOA algorithm and ICIC algorithm can obtain a positive effect for each other and improve the network performance in LTE system.

The ignored side effect reflecting in the introduction of mismatching brought by contrast enhancement in representative SIFT based vein recognition model is investigated. To take advantage of contrast enhancement in increasing keypoints generation, hierarchical keypoints selection and mismatching removal strategy is designed to obtain state-of-the-art recognition result.

Convolutional Neural Networks (CNNs) usually have millions or even billions of parameters, which make them hard to be deployed into mobile devices. In this work, we present a novel filter-level pruning method to alleviate this issue. More concretely, we first construct an undirected fully connected graph to represent a pre-trained CNN model. Then, we employ the spectral clustering algorithm to divide the graph into some subgraphs, which is equivalent to clustering the similar filters of the CNN into the same groups. After gaining the grouping relationships among the filters, we finally keep one filter for one group and retrain the pruned model. Compared with previous pruning methods that identify the redundant filters by heuristic ways, the proposed method can select the pruning candidates more reasonably and precisely. Experimental results also show that our proposed pruning method has significant improvements over the state-of-the-arts.