To ensure the security of image transmission, this paper presents a new image encryption algorithm based on a genetic algorithm (GA) and a piecewise linear chaotic map (PWLCM), which adopts the classical diffusion-substitution architecture. The GA is used to identify and output the optimal encrypted image that has the highest entropy value, the lowest correlation coefficient among adjacent pixels and the strongest ability to resist differential attack. The PWLCM is used to scramble pixel positions and change pixel values. Experiments and analyses show that the new algorithm possesses a large key space and resists brute-force, statistical and differential attacks. Meanwhile, the comparative analysis also indicates the superiority of our proposed algorithm over a similar, recently published, algorithm.

Nowadays, computer systems are limited by the power and memory wall. As the Dynamic Random Access Memory (DRAM) has dominated the power consumption in modern devices, developing power-saving approaches on DRAM has become more and more important. Among several techniques on different abstract levels, scheduling-based power management policies can be applied to existing memory controllers to reduce power consumption without causing severe performance degradation. Existing power-aware schedulers cluster memory requests into sets, so that the large portion of the DRAM can be switched into the power saving mode; however, only the target addresses are taken into consideration when clustering, while we observe the types (read or write) of requests can play an important role. In this paper, we propose two scheduling-based power management techniques on the DRAM controller: the inter-rank read-write aware clustering approach greatly reduces the active standby power, and the intra-rank read-write aware reordering approach mitigates the performance degradation. The simulation results show that the proposed techniques effectively reduce 75% DRAM power on average. Compared with the existing policy, the power reduction is 10% more on average with comparable or less performance degradation for the proposed techniques.

Distributed file systems, which manage large amounts of data over multiple commercially available machines, have attracted attention as management and processing systems for Big Data applications. A distributed file system consists of multiple data nodes and provides reliability and availability by holding multiple replicas of data. Due to system failure or maintenance, a data node may be removed from the system, and the data blocks held by the removed data node are lost. If data blocks are missing, the access load of the other data nodes that hold the lost data blocks increases, and as a result, the performance of data processing over the distributed file system decreases. Therefore, replica reconstruction is an important issue to reallocate the missing data blocks to prevent such performance degradation. The Hadoop Distributed File System (HDFS) is a widely used distributed file system. In the HDFS replica reconstruction process, source and destination data nodes for replication are selected randomly. We find that this replica reconstruction scheme is inefficient because data transfer is biased. Therefore, we propose two more effective replica reconstruction schemes that aim to balance the workloads of replication processes. Our proposed replication scheduling strategy assumes that nodes are arranged in a ring, and data blocks are transferred based on this one-directional ring structure to minimize the difference in the amount of transfer data for each node. Based on this strategy, we propose two replica reconstruction schemes: an optimization scheme and a heuristic scheme. We have implemented the proposed schemes in HDFS and evaluate them on an actual HDFS cluster. We also conduct experiments on a large-scale environment by simulation. From the experiments in the actual environment, we confirm that the replica reconstruction throughputs of the proposed schemes show a 45% improvement compared to the HDFS default scheme. We also verify that the heuristic scheme is effective because it shows performance comparable to the optimization scheme. Furthermore, the experimental results on the large-scale simulation environment show that while the optimization scheme is unrealistic because a long time is required to find the optimal solution, the heuristic scheme is very efficient because it can be scalable, and that scheme improved replica reconstruction throughput by up to 25% compared to the default scheme.

Recommendation systems have been widely used in E-commerce sites, social media and etc. An important recommendation task is to predict items that a user will perform actions on with users' historical data, which is called top-K recommendation. Recently, there is huge amount of emerging items which are divided into a variety of categories and researchers have argued or suggested that top-K recommendation of item category could be very beneficial for users to make better and faster decisions. However, the traditional methods encounter some common but crucial problems in this scenario because additional information, such as time, is ignored. The ranking algorithm on graphs and the increasingly growing amount of online user behaviors shed some light on these problems. We propose a construction method of time-aware graphs to use ranking algorithm for personalized recommendation of item category. Experimental results on real-world datasets demonstrate the advantages of our proposed method over competitive baseline algorithms.

Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today's online business world. However, most recommendation approaches exploit only a single source of input data and suffer from the data sparsity problem and the cold start problem. To improve recommendation accuracy in this situation, additional sources of information, such as friend relationship and user-generated tags, should be incorporated in recommendation systems. In this paper, we revise the user-based collaborative filtering (CF) technique, and propose two recommendation approaches fusing user-generated tags and social relations in a novel way. In order to evaluate the performance of our approaches, we compare experimental results with two baseline methods: user-based CF and user-based CF with weighted friendship similarity using the real datasets (Last.fm and Movielens). Our experimental results show that our methods get higher accuracy. We also verify our methods in cold-start settings, and our methods achieve more precise recommendations than the compared approaches.

This paper focuses on system level simulation of heterogeneous networks (HetNet). Aiming at the imbalance offloading of macro cell and pico cell under the macro-pico coexistence case, we propose an adaptive cell-specific association strategy for HetNet to ensure that users can be served equitably by both macro cell and pico cell. The traditional cell range expansion (CRE) scheme with bias-based cell association has fixed bias values for all pico cells. Our proposal, on the other hand, sets different thresholds of attached users for all MeNB (macro enhanced node B) and PeNBs (pico enhanced node B), and all cell-specific biases are obtained by the proposed adaptive association strategy according to different cell-specific predefined thresholds. With this strategy, the load imbalance between MeNB and different PeNBs is well alleviated, and hence the entire network performance is elevated. Moreover, due to the newly deployed low-power nodes in HetNets, the achieved spectral efficiency of users, especially for cell edge users, is also affected by the downlink inter-cell interference. The idea we put forward is to combine the frequency and power coordination, and so suppress the inter-cell interference. Finally in this paper, we present some numerical results to verify the effectiveness of our proposed approach.

Triggered by the explosion of mobile traffic, 5G (5th Generation) cellular network requires evolution to increase the system rate 1000 times higher than the current systems in 10 years. Motivated by this common problem, there are several studies to integrate mm-wave access into current cellular networks as multi-band heterogeneous networks to exploit the ultra-wideband aspect of the mm-wave band. The authors of this paper have proposed comprehensive architecture of cellular networks with mm-wave access, where mm-wave small cell basestations and a conventional macro basestation are connected to Centralized-RAN (C-RAN) to effectively operate the system by enabling power efficient seamless handover as well as centralized resource control including dynamic cell structuring to match the limited coverage of mm-wave access with high traffic user locations via user-plane/control-plane splitting. In this paper, to prove the effectiveness of the proposed 5G cellular networks with mm-wave access, system level simulation is conducted by introducing an expected future traffic model, a measurement based mm-wave propagation model, and a centralized cell association algorithm by exploiting the C-RAN architecture. The numerical results show the effectiveness of the proposed network to realize 1000 times higher system rate than the current network in 10 years which is not achieved by the small cells using commonly considered 3.5GHz band. Furthermore, the paper also gives latest status of mm-wave devices and regulations to show the feasibility of using mm-wave in the 5G systems.

It is very difficult to know evolution state of ACO in its working. To solve the problem, we propose using colony entropy and mean colony entropy to monitor the algorithm. The two functions show fluctuation and declining trends depended on time t in a tour and iteration number. According to the principle, that each updated edge will get the same increment is improper. Then a weighted algorithm is proposed to calculate each arc's increment based on its selected probability. The strategy can provide more exploration to help to find the global optimum value, and experiments show its improved performance.

In this paper, we propose a 3rd-order nonlinear IIR filter for compensating nonlinear distortions of loudspeaker systems. Nonlinear distortions are common around the lowest resonance frequency for electrodynamic loudspeaker systems. One interesting approach to compensating nonlinear distortions is to employ a mirror filter. The mirror filter is derived from the nonlinear differential equation for loudspeaker systems. The nonlinear parameters of a loudspeaker system, which include the force factor, stiffness, and so forth, depend on the displacement of the diaphragm. The conventional filter structure, which is called the 2nd-order nonlinear IIR filter that originates the mirror filter, cannot reduce nonlinear distortions at high frequencies because it does not take into account the nonlinearity of the self-inductance of loudspeaker systems. To deal with this problem, the proposed filter takes into account the nonlinearity of the self-inductance and has a 3rd-order nonlinear IIR filter structure. Hence, this filter can reduce nonlinear distortions at high frequencies while maintaining a lower computational complexity than that of a Volterra filter-based compensator. Experimental results demonstrate that the proposed filter outperforms the conventional filter by more than 2dB for 2nd-order nonlinear distortions at high frequencies.

This paper presents our investigation of non-orthogonal multiple access (NOMA) as a novel and promising power-domain user multiplexing scheme for future radio access. Based on information theory, we can expect that NOMA with a successive interference canceller (SIC) applied to the receiver side will offer a better tradeoff between system efficiency and user fairness than orthogonal multiple access (OMA), which is widely used in 3.9 and 4G mobile communication systems. This improvement becomes especially significant when the channel conditions among the non-orthogonally multiplexed users are significantly different. Thus, NOMA can be expected to efficiently exploit the near-far effect experienced in cellular environments. In this paper, we describe the basic principle of NOMA in both the downlink and uplink and then present our proposed NOMA scheme for the scenario where the base station is equipped with multiple antennas. Simulation results show the potential system-level throughput gains of NOMA relative to OMA.

In the big data era, messaging systems are required to process large volumes of message traffic with high scalability and availability. However, conventional systems have two issues regarding availability. The first issue is that failover processing itself has a risk of failure. The second issue is to find a trade-off between consistency and availability. We propose a resilient messaging system based on a distributed in-memory key-value store (KVS). Its servers are interconnected with each other and messages are distributed to multiple servers in normal processing state. This architecture can continue messaging services wherever in the messaging system server/process failures occur without using failover processing. Furthermore, we propose two methods for improved resilience: the round-robin method with a slowdown KVS exclusion and the two logical KVS counter-rotating rings to provide short-term-availability in the messaging system. Evaluation results demonstrate that the proposed system can continue service without failover processing. Compared with the conventional method, our proposed distribution method reduced 92% of error responses to clients caused by server failures.

In order to verify the channel sum-rate improvement by multi-user multiple-input multiple-output (MU-MIMO) transmission in distributed antenna systems (DASs), we investigate and compare the characteristics of channel sum-rates in both centralized antenna systems (CASs) and DASs under the effects of path loss, spatially correlated shadowing, correlated multi-path fading, and inter-cell interference. In this paper, we introduce two different types of functions to model the shadowing, auto-correlation and cross-correlation, and a typical exponential decay function to model the multi-path fading correlation. Thus, we obtain the distribution of the channel sum-rate and investigate its characteristics. Computer simulation results indicate that DAS can improve the performance of the channel sum-rate compared to CAS, even in the case under consideration. However, this improvement decreases as interference power increases. Moreover, the decrease in the channel sum-rate due to the increase in the interference power becomes slow under the effect of shadowing correlation. In addition, some other analyses on the shadowing correlation that occurs on both the transmit and receiver sides are provided. These analysis results show that the average channel sum-rate in a DAS without inter-cell interference considerably decreases because of the shadowing correlation. In contrast, there appears to be no change in the CAS. Furthermore, there are two different types of sum-rate changes in a DAS because of the difference in shadowing auto-correlation and cross-correlation.

Providing images captured by an on-board camera to surrounding vehicles is an effective method to achieve smooth road traffic and to avoid traffic accidents. We consider providing images using WiFi technology based on the IEEE802.11p standard for vehicle-to-vehicle (V2V) communication media. We want to compress images to suppress communication traffic, because the communication capacity of the V2V system is strictly limited. However, there are difficulties in image compression and transmission using wireless communication especially in a vehicular broadcast environment, due to transmission errors caused by fading, packet collision, etc. In this letter, we propose an image transmission technique based on compressed sensing. Through computer simulations, we show that our proposed technique can achieve stable image reconstruction despite frequent packet error.

In this paper, we consider a decentralized failure diagnosis problem for discrete event systems. For a conjunctively codiagnosable system, there exists a conjunctive decentralized diagnoser that can detect the occurrence of any failure within a uniformly bounded number of steps. We present a method for synthesizing such a conjunctive decentralized diagnoser as an online diagnoser.

In this paper, optimal control of multi-vehicle systems is studied. In the case where collision avoidance between vehicles and obstacle avoidance are imposed, state discretization is effective as one of the simplified approaches. Furthermore, using state discretization, cooperative actions such as rendezvous can be easily specified by linear temporal logic (LTL) formulas. However, it is not necessary to discretize all states, and partial states (e.g., the position of vehicles) should be discretized. From this viewpoint, a new control method for multi-vehicle systems is proposed in this paper. First, the system in which partial states are discretized is formulated. Next, the optimal control problem with constraints described by LTL formulas is formulated, and its solution method is proposed. Finally, numerical simulations are presented. The proposed method provides us a useful method in control of multi-vehicle systems.

A cloud system is defined as a large scale computer system that contains running high performance computers and responds to a large number of incoming tasks over the Internet. In this paper, we consider the problem to schedule computational jobs efficiently regarding system resource constraint and introduce a cuckoo search (CS) algorithm. Experimental results show that CS outperforms the genetic algorithm in terms of fitness value.

The bidirectional DC-DC converters that are used in backup power supplies, energy storage systems, and electric vehicles, are described in this paper, because they have recently attracted a lot of attention. First, this paper shows the main use of the bidirectional DC-DC converter, the optimum circuit topology in accordance with its use, and the characteristic properties of the circuits. In addition, the expected characteristics for the next generations of power semiconductor devices for each bidirectional converter circuit are shown.

Social recommendation systems that make use of the user's social information have recently attracted considerable attention. These recommendation approaches partly solve cold-start and data sparsity problems and significantly improve the performance of recommendation systems. The essence of social recommendation methods is to utilize the user's explicit social connections to improve recommendation results. However, this information is not always available in real-world recommender systems. In this paper, a solution to this problem of explicit social information unavailability is proposed. The existing user-item rating matrix is used to compute implicit social information, and then an ISRec (implicit social recommendation algorithm) which integrates this implicit social information and the user-item rating matrix for social recommendation is introduced. Experimental results show that our method performs much better than state-of-the-art approaches; moreover, complexity analysis indicates that our approach can be applied to very large datasets because it scales linearly with respect to the number of observations in the matrices.

In this paper, we present a new method for diagnosis of stochastic discrete event system. The method is based on anomaly detection for sequences. We call the method sequence profiling (SP). SP does not require any system models and any system-specific knowledge. The only information necessary for SP is event logs from the target system. Using event logs from the system in the normal situation, N-gram models are learned, where the N-gram model is used as approximation of the system behavior. Based on the N-gram model, the diagnoser estimates what kind of faults has occurred in the system, or may conclude that no faults occurs. Effectiveness of the proposed method is demonstrated by application to diagnosis of a multi-processor system.

In elevator-group control, the average number of running cars should be finely adjusted by the dynamically controlling the number of running cars (DCNRC). Traffic demand in an office building varies throughout the day. In this paper, we propose a new energy-saving method for elevator-group control that adjusts the number of running cars according to the traffic demand, simulate the proposed energy-saving method under nearly real traffic demand conditions of an office building, and reduce the daily energy consumption to the target level after several days.