In cognitive radio (CR), superposition cooperative spectrum sensing (SPCSS) is able to offer a much improved sensing reliability compared to individual sensing. Because of the differences in sensing channel condition, the reporting order for each cognitive radio user (CU) will highly affect the sensing performance of the network. In this paper, we propose an algorithm to assign the best reporting order to each CU in order to maximize sensing performance under SPCSS. The numerical results show that the proposed scheme can obtain the same performance as the optimal scheme.

We study the problem of transforming one list (vertex) coloring of a graph into another list coloring by changing only one vertex color assignment at a time, while at all times maintaining a list coloring, given a list of allowed colors for each vertex. This problem is known to be PSPACE-complete for bipartite planar graphs. In this paper, we first show that the problem remains PSPACE-complete even for bipartite series-parallel graphs, which form a proper subclass of bipartite planar graphs. We note that our reduction indeed shows the PSPACE-completeness for graphs with pathwidth two, and it can be extended for threshold graphs. In contrast, we give a polynomial-time algorithm to solve the problem for graphs with pathwidth one. Thus, this paper gives sharp analyses of the problem with respect to pathwidth.

In this paper, the multicell distributed beamforming (MDBF) design problem of suppressing intra-cell interference (InCI) and inter-cell interference (ICI) is studied. To start with, in order to decrease the InCI and ICI caused by a user, we propose a gradient-iteration altruistic algorithm to derive the beamforming vectors. The convergence of the proposed iterative algorithm is proved. Second, a metric function is established to restrict the ICI and maximize cell rate. This function depends on only local channel state information (CSI) and does not need additional CSIs. Moreover, an MDBF algorithm with the metric function is proposed. This proposed algorithm utilizes gradient iteration to maximize the metric function to improve sum rate of the cell. Finally, simulation results demonstrate that the proposed algorithm can achieve higher cell rates while offering more advantages to suppress InCI and ICI than the traditional ones.

Since the conventional cascade controller for electric motor drives requires accurate information about the system parameters and load conditions to achieve a desired performance, this paper presents a new practical control structure to improve the robust performance against parameter uncertainties. Two first-order disturbance observers (DOB) are incorporated with the cascade structure, to preserve the nominal performance. The analysis of the robust performance of the DOB is presented by using the singular perturbation theory. Simulation results suggest that the proposed controller can be used effectively as an additional compensator to the conventional cascade scheme.

The MQ problem, which consists of solving a system of multivariate quadratic polynomials over a finite field, has attracted the attention of researchers for the development of public-key cryptosystems because (1) it is NP-complete, (2) there is no known polynomial-time algorithm for its solution, even in the quantum computational model, and (3) it enables cryptographic primitives of practical interest. In 2011, Sakumoto, Shirai and Hiwatari presented two new zero-knowledge identification protocols based exclusively on the MQ problem. The 3-pass identification protocol of Sakumoto et al. has impersonation probability 2/3. In this paper, we propose an improvement that reduces the impersonation probability to 1/2. The result is a protocol that reduces the total computation time, the total communication needed and requires a smaller number of rounds for the same security level. We also present a new extension that achieves an additional communication reduction with the use of some smaller hash commitments, but maintaining the same security level.

In this correspondence, a generic method of constructing optimal p2-ary low correlation zone sequence sets is proposed. Firstly p2-ary column sequence sets are constructed, then p2-ary LCZ sequence sets with parameters (pn-1, pm-1, (pn-1)/(pm-1),1) are constructed by using column sequences and interleaving technique. The resultant p2-ary LCZ sequence sets are optimal with respect to the Tang-Fan-Matsufuji bound.

Packet classification is a fundamental task in the control of network traffic, protection from cyber threats. Most layer 3 and higher network devices have a packet classification capability that determines whether to permit or discard incoming packets by comparing their headers with a set of rules. Although linear search is an intuitive implementation of packet classification, it is very inefficient. Srinivasan et al. proposed a novel lookup scheme using a hierarchical trie instead of linear search, which realizes faster packet classification with time complexity proportional to rule length rather than the number of rules. However, the hierarchical trie and its various improved algorithms allow only single prefix rules to be processed. Since it is necessary for layer 4 and higher packet classifications to deal with arbitrary bitmask rules in the hierarchical trie, we propose a run-based trie based on the hierarchical trie, but extended to deal with arbitrary bitmask rules. Our proposed algorithm achieves O((dW)2) query time and O(NdW) space complexity with N rules of length dW. The query time of our novel alrorithm doesn't depend on the number of rules. It solves the latency problem caused by increase of the rules in firewalls.

Once a software product has been released, a large number of software products may be derived from an original single product. Management and maintenance of product variants are important, but those are hardly cared because developers do not make efforts for the further maintainability in the initial phase of software development. However, history of products would be lost in typical cases and developers have only source code of products in the worst case. In this paper, we approximate the evolution history of software products using source code of them. Our key idea is that two successive products are the most similar pair of products in evolution history, and have many similar source files. We did an experiment to compare the analysis result with actual evolution history. The result shows 78% (on average) of edges in the extracted trees are consistent with the actual evolution history of the products.

The number of states is a very important matter for model checking approach in Petri net's analysis. We first gave a formal definition of state number calculation problem: For a Petri net with an initial state (marking), how many states does it have? Next we showed the problem cannot be solved in polynomial time for a popular subclass of Petri nets, known as free choice workflow nets, if P≠NP. Then we proposed a polynomial time algorithm to solve the problem by utilizing a representational bias called as process tree. We also showed effectiveness of the algorithm through an application example.

In recent years, hybrid typestate analysis has been proposed to eliminate unnecessary monitoring instrumentations for runtime monitors at compile-time. Nop-shadows Analysis (NSA) is one of these hybrid typestate analyses. Before generating residual monitors, NSA performs the data-flow analysis which is intra-procedural flow-sensitive and partially context-sensitive to improve runtime performance. Although NSA is precise, there are some cases on which it has little effects. In this paper, we propose three optimizations to further improve the precision of NSA. The first two optimizations try to filter interferential states of objects when determining whether a monitoring instrumentation is necessary. The third optimization refines the inter-procedural data-flow analysis induced by method invocations. We have integrated our optimizations into Clara and conducted extensive experiments on the DaCapo benchmark. The experimental results demonstrate that our first two optimizations can further remove unnecessary instrumentations after the original NSA in more than half of the cases, without a significant overhead. In addition, all the instrumentations can be removed for two cases, which implies the program satisfy the typestate property and is free of runtime monitoring. It comes as a surprise to us that the third optimization can only be effective on 8.7% cases. Finally, we analyze the experimental results and discuss the reasons why our optimizations fail to further eliminate unnecessary instrumentations in some special situations.

We propose a novel bias-free adaptive beamformer employing an affine projection algorithm with the optimal regularization parameter. The generalized sidelobe canceller affine projection algorithm suffers from a bias of a weight vectors under the condition of no reference signals for output of an array in the beamforming application. First, we analyze the bias in the algorithm and prove that the bias can be eliminated through a large regularization parameter. However, this causes slow convergence at the initial state, so the regularization parameter should be controlled. Through the optimization of the regularization parameter, the proposed method achieves fast convergence without the bias at the steady-state. Experimental results show that the proposed beamformer not only removes the bias but also achieves both fast convergence and high steady-state output signal-to-interference-plus-noise ratio.

Memory leak occurs when useless objects cannot be released for a long time during program execution. Memory leaked objects may cause memory overflow, system performance degradation and even cause the system to crash when they become serious. This paper presents a dynamic approach for detecting and measuring memory leaked objects in Java programs. First, our approach tracks the program by JDI and records heap information to find out the potentially leaked objects. Second, we present memory leaking confidence to measure the influence of these objects on the program. Finally, we select three open-source programs to evaluate the efficiency of our approach. Furthermore, we choose ten programs from DaCapo 9.12 benchmark suite to reveal the time overhead of our approach. The experimental results show that our approach is able to detect and measure memory leaked objects efficiently.

Controlling interference from the secondary system (SS) to the receiver of the primary system (PS) is an important issue when the SS uses the same frequency band as the television broadcast system. The reason includes that the SS is unaware of the interference imposed on the primary receiver (PS-Rx), which does not have a transmitter. In this paper, we propose an interference control method between PS-Rx and SS, where a load modulation scheme is introduced to the PS-Rx. In this method, the signal from the PS transmitting station is scattered by switching its load impedance. The SS observes the scattered channel and calculates the interference suppression weights for transmitting, and controls interference by transmit beamforming. A simulation shows that the Signal-to-Interference Ratio (SIR) with interference control is improved by up to 41.5dB compared to that without interference control at short distances; the results confirm that the proposed method is effective in controlling interference between PS-Rx and SS. Furthermore, we evaluate the Signal-to-Noise Ratio (SNR) and channel capacity at SS.

Recently, we proposed an interference-aware channel segregation based dynamic channel assignment (IACS-DCA). In IACS-DCA, each base station (BS) measures the instantaneous co-channel interference (CCI) power on each available channel, computes the moving average CCI power using past CCI measurement results, and selects the channel having the lowest moving average CCI power. In this way, the CCI-minimized channel reuse pattern can be formed. In this paper, we introduce the autocorrelation function of channel reuse pattern, the fairness of channel reuse, and the minimum co-channel BS distance to quantitatively examine the channel reuse pattern formed by the IACS-DCA. It is shown that the IACS-DCA can form a CCI-minimized channel reuse pattern in a distributed manner and that it improves the signal-to-interference ratio (SIR) compared to the other channel assignment schemes.

Millimeter-wave (mm-wave) radio is attracting attention as one of the key enabling physical layer technologies for the fifth-generation (5G) mobile access and backhaul. This paper aims at clarifying possible roles of mm-wave radio in the 5G development and performing a comprehensive literature survey on mm-wave radio channel modeling essential for the feasibility study. Emphasis in the literature survey is laid on grasping the typical behavior of mm-wave channels, identifying missing features in the presently available channel models for the design and evaluation of the mm-wave radio links within the 5G context, and exemplifying different channel modeling activities through analyses performed in the authors' group. As a key technological element of the mm-wave radios, reduced complexity beamforming is also addressed. Design criteria of the beamforming are developed based on the spatial multipath characteristics of measured indoor mm-wave channels.

The capacity maximization of line-of-sight (LoS) two-input and multiple-output (TIMO) channels in indoor environments is investigated in this paper. The 3×2 TIMO channel is mainly studied. First, the capacity fluctuation number (CFN) which reflects the variation of channel capacity is proposed. Then, the expression of the average capacity against the CFN is derived. The CFN is used as a criterion for optimization of the capacity by changing inter-element spacings of transmit and receive antenna arrays. Next, the capacity sensitivity of the 3×2 TIMO channel to the orientation and the frequency variation is studied and compared with those of 2×2 and 4×2 TIMO channels. A small capacity sensitivity of the 3×2 TIMO channel is achieved and verified by both simulation and measurement results. Furthermore, the CFN can also be used as a criterion for optimization of average capacity and the proposed optimization method is validated through numerical results.

Small cell networks have been promoted as an enabling solution to enhance indoor coverage and improve spectral efficiency. Users usually deploy small cells on-demand and pay no attention to global profile in residential areas or offices. The reduction of cell radius leads to dense deployment which brings intractable computation complexity for resource allocation. In this paper, we develop a semi-distributed resource allocation algorithm by dividing small cell networks into clusters with limited inter-cluster interference and selecting a reference cluster for interference estimation to reduce the coordination degree. Numerical results show that the proposed algorithm can maintain similar system performance while having low complexity and reduced information exchange overheads.

Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.

This paper presents a novel peer-to-peer protocol to efficiently distribute virtual machine images in a datacenter. A primary idea of it is to improve the performance of peer-to-peer content delivery by employing deduplication to take advantage of similarity both among and within VM images in cloud datacenters. The efficacy of the proposed scheme is validated through an evaluation that demonstrates substantial performance gains.

Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition model. Based on the topic transition model, regular topic transition probability is used during generating summary. This approach eliminates the topic independence assumption in the Latent Dirichlet Allocation (LDA) model. Meanwhile, the results of experiments show the advantage of the combination of the three kinds of models. This paper includes alleviating topic independency, and integrating surface texture and shallow semantic in documents to improve summarization. In short, this paper attempts to realize an advanced summarization system.