For cloud data center, Virtual Machine (VM) consolidation is an effective way to save energy and improve efficiency. However, inappropriate consolidation of VMs, especially aggressive consolidation, can lead to performance problems, and even more serious Service Level Agreement (SLA) violations. Therefore, it is very important to solve the tradeoff between reduction in energy use and reduction of SLA violation level. In this paper, we propose two Host State Detection algorithms and an improved VM placement algorithm based on our proposed Host State Binary Decision Tree Prediction model for SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We propose two formulas of conditions for host state estimate, and our model uses them to build a Binary Decision Tree manually for host state detection. We extend Cloudsim simulator to evaluate our algorithms by using PlanetLab workload and random workload. The experimental results show that our proposed model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLAV by at most 98.12% and the metric of Energy by at most 33.96% for real world workload.

In the beam-type microwave power transmission system, it is required to minimize the interference with communication and the influence on the human body. Retrodirective system that re-radiates a beam in the direction of arrival of a signal is well known as a beam control technique for accurate microwave power transmission. In this paper, we newly propose to apply the retrodirective system to both transmitting and receiving antennas. The leakage to the outside of the system is expected to minimize self-convergently while following the atmospheric fluctuation and the antenna movement by repeating the retrodirective between the transmitting and receiving antenna in this system. We considered this phenomenon theoretically using an infinite array antenna model. Finally, it has been shown by the equivalent circuit simulation that stable transmission can be realized by oscillating the system.

The effectiveness of model adaptation in dialogue speech synthesis is explored. The proposed adaptation method is based on a conversion from a base model learned with a large dataset into a target, dialogue-style speech model. The proposed method is shown to improve the intelligibility of synthesized dialogue speech, while maintaining the speaking style of dialogue.

We research device-free (DF) multi-target tracking scheme in this paper. The existing localization and tracking algorithms are always pay attention to the single target and need to collect a large amount of localization information. In this paper, we exploit the sparse property of multiple target locations to achieve target trace accurately with much less sampling both in the wireless links and the time slots. The proposed approach mainly includes the target localization part and target trace recovery part. In target localization part, by exploiting the inherent sparsity of the target number, Compressive Sensing (CS) is utilized to reduce the wireless links distributed. In the target trace recovery part, we exploit the compressive property of target trace, as well as designing the measurement matrix and the sparse matrix, to reduce the samplings in time domain. Additionally, Kronecker Compressive Sensing (KCS) theory is used to simultaneously recover the multiple traces both of the X label and the Y Label. Finally, simulations show that the proposed approach holds an effective recovery performance.

In millimeter wave (mmWave) vehicular communications, multi-hop relay disconnection by line-of-sight (LOS) blockage is a critical problem, particularly in the early diffusion phase of mmWave-available vehicles, where not all vehicles have mmWave communication devices. This paper proposes a distributed position control method to establish long relay paths through road side units (RSUs). This is realized by a scheme via which autonomous vehicles change their relative positions to communicate with each other via LOS paths. Even though vehicles with the proposed method do not use all the information of the environment and do not cooperate with each other, they can decide their action (e.g., lane change and overtaking) and form long relays only using information of their surroundings (e.g., surrounding vehicle positions). The decision-making problem is formulated as a Markov decision process such that autonomous vehicles can learn a practical movement strategy for making long relays by a reinforcement learning (RL) algorithm. This paper designs a learning algorithm based on a sophisticated deep reinforcement learning algorithm, asynchronous advantage actor-critic (A3C), which enables vehicles to learn a complex movement strategy quickly through its deep-neural-network architecture and multi-agent-learning mechanism. Once the strategy is well trained, vehicles can move independently to establish long relays and connect to the RSUs via the relays. Simulation results confirm that the proposed method can increase the relay length and coverage even if the traffic conditions and penetration ratio of mmWave communication devices in the learning and operation phases are different.

Boolean functions and vectorial Boolean functions are the most important components of stream ciphers. Their cryptographic properties are crucial to the security of the underlying ciphers. And how to construct such functions with good cryptographic properties is a nice problem that worth to be investigated. In this paper, using two small nonlinear functions with t-1 resiliency, we provide a method on constructing t-resilient n variables Boolean functions with strictly almost optimal nonlinearity >2n-1-2n/2 and optimal algebraic degree n-t-1. Based on the method, we give another construction so that a large class of resilient vectorial Boolean functions can be obtained. It is shown that the vectorial Boolean functions also have strictly almost optimal nonlinearity and optimal algebraic degree.

In this paper, we investigate the maximum induced matching problem (MaxIM) on C5-free d-regular graphs. The previously known best approximation ratio for MaxIM on C5-free d-regular graphs is $left(rac{3d}{4}-rac{1}{8}+rac{3}{16d-8} ight)$. In this paper, we design a $left(rac{2d}{3}+rac{1}{3} ight)$-approximation algorithm, whose approximation ratio is strictly smaller/better than the previous one when d≥6.

In this paper, we propose multidimensional stochastic modeling of priority broadcast in Vehicular Ad hoc Networks (VANET). We focus on the channel switching operation of IEEE 1609.4 in systems that handle different types of safety messages, such as event-driven urgent messages and periodic beacon messages. The model considers the constraints imposed by the channel switching operation. The model also reflects differentiated services that handle different types of messages. We carefully consider the delivery time limit and the number of transmissions of the urgent messages. We also consider the hidden node problem, which has an increased impact on broadcast communications. We use the model in analyzing the relationship between system variables and performance metrics of each message type. The analysis results include confirming that the differentiated services work effectively in providing class specific quality of services under moderate traffic loads, and that the repeated transmission of urgent message is a meaningful countermeasure against the hidden node problem. It is also confirmed that the delivery time limit of urgent message is a crucial factor in tuning the channel switching operation.

Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness.

Social networks often demonstrate hierarchical community structure with communities embedded in other ones. Most existing hierarchical community detection methods need one or more tunable parameters to control the resolution levels, and the obtained dendrograms, a tree describing the hierarchical community structure, are extremely complex to understand and analyze. In the paper, we propose a parameter-free hierarchical community detection method based on micro-community and minimum spanning tree. The proposed method first identifies micro-communities based on link strength between adjacent vertices, and then, it constructs minimum spanning tree by successively linking these micro-communities one by one. The hierarchical community structure of social networks can be intuitively revealed from the merging order of these micro-communities. Experimental results on synthetic and real-world networks show that our proposed method exhibits good accuracy and efficiency performance and outperforms other state-of-the-art methods. In addition, our proposed method does not require any pre-defined parameters, and the output dendrogram is simple and meaningful for understanding and analyzing the hierarchical community structure of social networks.

This paper considers Peer-to-Peer (P2P) video streaming systems, in which a given video stream is divided into b stripes and those stripes are delivered to n peers through b spanning trees under the constraint such that each peer including the source can forward at most b stripes. The delivery of a stripe to n peers is said to be a k-hop delivery if all peers receive the stripe through a path of length at most k. Let Bk=∑i=0k-1bi. It is known that under the above constraint, k-hop delivery of b stripes to n peers is possible only if n≤Bk. This paper proves that (k+1)-hop delivery of b stripes to n peers is possible for any n≤Bk; namely, we can realize the delivery of stripes with a guaranteed latency while it is slightly larger than the minimum latency. In addition, we derive a necessary and sufficient condition on n to enable a k-hop delivery of b stripes for Bk-b+2≤n≤Bk-1; namely for n's close to Bk.

A key-order preserving structured overlay network is a class of structured overlay network that preserves, in its structure, the order of keys to support efficient range queries. This paper presents a novel key-order preserving structured overlay network “Suzaku”. Similar to the conventional Chord#, Suzaku uses a periodically updated finger table as a routing table, but extends its uni-directional finger table to bi-directional, which achieves ⌈log2 n⌉-1 maximum lookup hops in the converged state. Suzaku introduces active and passive bi-directional finger table update algorithms for node insertion and deletion. This method maintains good lookup performance (lookup hops increase nearly logarithmically against n) even in churn situations. As well as its good performance, the algorithms of Suzaku are simple and easy to implement. This paper describes the principles of Suzaku, followed by simulation evaluations, in which it showed better performance than the conventional networks, Chord# and Skip Graph.

For a family H of connected graphs and an integer k≥1, let Gk(H) denote the family of k-connected graphs which contain no element of H as an induced subgraph. Let H+ be the family of those connected graphs of order 5 which contain K1,3 as an induced subgraph. In this paper, for each integer k≥1, we characterize the families H⊆H+ such that the symmetric difference of Gk(K1,3) and Gk(H) is finite.

Conventional approaches to statistical parametric speech synthesis use context-dependent hidden Markov models (HMMs) clustered using decision trees to generate speech parameters from linguistic features. However, decision trees are not always appropriate to model complex context dependencies of linguistic features efficiently. An alternative scheme that replaces decision trees with deep neural networks (DNNs) was presented as a possible way to overcome the difficulty. By training the network to represent high-dimensional feedforward dependencies from linguistic features to acoustic features, DNN-based speech synthesis systems convert a text into a speech. To improved the naturalness of the synthesized speech, this paper presents a novel pre-training method for DNN-based statistical parametric speech synthesis systems. In our method, a deep relational model (DRM), which represents a joint probability of two visible variables, is applied to describe the joint distribution of acoustic and linguistic features. As with DNNs, a DRM consists several hidden layers and two visible layers. Although DNNs represent feedforward dependencies from one visible variables (inputs) to other visible variables (outputs), a DRM has an ability to represent the bidirectional dependencies between two visible variables. During the maximum-likelihood (ML) -based training, the model optimizes its parameters (connection weights between two adjacent layers, and biases) of a deep architecture considering the bidirectional conversion between 1) acoustic features given linguistic features, and 2) linguistic features given acoustic features generated from itself. Owing to considering whether the generated acoustic features are recognizable, our method can obtain reasonable parameters for speech synthesis. Experimental results in a speech synthesis task show that pre-trained DNN-based systems using our proposed method outperformed randomly-initialized DNN-based systems, especially when the amount of training data is limited. Additionally, speaker-dependent speech recognition experimental results also show that our method outperformed DNN-based systems, by setting the initial parameters of our method are the same as that in the synthesis experiments.

Power line communication (PLC) networks play an important role in home networks and in next generation hybrid networks, which provide higher data rates (Gbps) and easier connectivity. The standard medium access control (MAC) protocol of PLC networks, IEEE 1901, uses a special carrier sense multiple access with collision avoidance (CSMA/CA) mechanism, in which the deferral counter technology is introduced to avoid unnecessary collisions. Although PLC networks have achieved great commercial success, MAC layer analysis for IEEE 1901 PLC networks received limited attention. Until now, a few studies used renewal theory and strong law of large number (SLLN) to analyze the MAC performance of IEEE 1901 protocol. These studies focus on saturated conditions and neglect the impacts of buffer size and traffic rate. Additionally, they are valid only for homogeneous traffic. Motivated by these limitations, we develop a unified and scalable analytical model for IEEE 1901 protocol in unsaturated conditions, which comprehensively considers the impacts of traffic rate, buffer size, and traffic types (homogeneous or heterogeneous traffic). In the modeling process, a multi-layer discrete Markov chain model is constructed to depict the basic working principle of IEEE 1901 protocol. The queueing process of the station buffer is captured by using Queueing theory. Furthermore, we present a detailed analysis for IEEE 1901 protocol under heterogeneous traffic conditions. Finally, we conduct extensive simulations to verify the analytical model and evaluate the MAC performance of IEEE 1901 protocol in PLC networks.

Accurate estimation of the state-of-charge is a crucial need for the battery, which is the most important power source in electric vehicles. To achieve better estimation result, an accurate battery model with optimum parameters is required. In this paper, a gradient-free optimization technique, namely tree seed algorithm (TSA), is utilized to identify specific parameters of the battery model. In order to strengthen the search ability of TSA and obtain more quality results, the original algorithm is improved. On one hand, the DE/rand/2/bin mechanism is employed to maintain the colony diversity, by generating mutant individuals in each time step. On the other hand, the control parameter in the algorithm is adaptively updated during the searching process, to achieve a better balance between the exploitation and exploration capabilities. The battery state-of-charge can be estimated simultaneously by regarding it as one of the parameters. Experiments under different dynamic profiles show that the proposed method can provide reliable and accurate estimation results. The performance of conventional algorithms, such as genetic algorithm and extended Kalman filter, are also compared to demonstrate the superiority of the proposed method in terms of accuracy and robustness.

Since software becomes more complex during its life cycle, the verification cost becomes higher, especially for such methods which are using model checking in general and assume-guarantee reasoning in specific. To address the problem of reducing the assume-guarantee verification cost, this paper presents a method to generate locally minimum and strongest assumptions for verification of component-based software. For this purpose, we integrate a variant of membership queries answering technique to an algorithm which considers candidate assumptions that are smaller and stronger first, larger and weaker later. Because the algorithm stops as soon as it reaches a conclusive result, the generated assumptions are the locally minimum and strongest ones. The correctness proof of the proposed algorithm is also included in the paper. An implemented tool, test data, and experimental results are presented and discussed.

This paper improves our previously proposed semi-blind uplink interference suppression scheme for multicell multiuser massive MIMO systems by incorporating the beamspace approach. The constant modulus algorithm (CMA), a known blind adaptive array scheme, can fully exploit the degree of freedom (DoF) offered by massive antenna arrays to suppress inter-user interference (IUI) and inter-cell interference (ICI). Unfortunately, CMA wastes a lot of the benefit of DoF for null-steering even when the number of incoming signal is fewer than that of receiving antenna elements. Our new proposal introduces the beamspace method which degenerates the number of array input for CMA from element-space to beamspace. It can control DoF expended for subsequent interference suppression by CMA. Optimizing the array beamforming gain and null-steering ability, can further improve the output signal-to-interference and noise power ratio (SINR). Computer simulation confirmed that our new proposal reduced the required number of data symbols by 34.6%. In addition, the 5th percentile SINR was also improved by 14.3dB.

A W-band corporate-feed 16×16-slot array antenna with low sidelobe level is designed and fabricated. The basic unit of the array is a 2×2-circular-slot subarray with step square cavities and uses an E-plane waveguide as the feeding line. An efficient method to design an unequal power-splitting ratio but equal phase (UPEP) E-plane waveguide T-junction (E-T) is proposed for constructing a 1-to-64 power-tapering feed network, which is the critical part to realize low sidelobe level. The whole array is fabricated with aluminum by milling and bonded by the vacuum brazing process. The measured results demonstrate that the array can achieve a 7.2% bandwidth with VSWR<1.5 and holistic sidelobe levels lower than -23.5dB in E-plane and H-plane from 89GHz ∼ 95.8GHz. The measured gain is higher than 31.7dBi over the working band with the antenna efficiency better than 67.5%.

Deep Graphical Model (DGM) based on Generative Adversarial Nets (GANs) has shown promise in image generation and latent variable inference. One of the typical models is the Iterative Adversarial Inference model (GibbsNet), which learns the joint distribution between the data and its latent variable. We present RGNet (Re-inference GibbsNet) which introduces a re-inference chain in GibbsNet to improve the quality of generated samples and inferred latent variables. RGNet consists of the generative, inference, and discriminative networks. An adversarial game is cast between the generative and inference networks and the discriminative network. The discriminative network is trained to distinguish between (i) the joint inference-latent/data-space pairs and re-inference-latent/data-space pairs and (ii) the joint sampled-latent/generated-data-space pairs. We show empirically that RGNet surpasses GibbsNet in the quality of inferred latent variables and achieves comparable performance on image generation and inpainting tasks.