Search

A component of 100Gbps chip-level optical interconnect is implemented by developing a PHY integrated with an optic transceiver through co-package. It achieved 1/3 power consumption, 1/2 area compared with board-level interconnect. It can be a building block for integration with CPU to implement optical I/O.

Since the existing works in data stream anomaly detection mainly locate abnormal objects from data sequences instead of detecting the abnormal state of a monitored object in real-time. And they need a set of empirical predefined thresholds, which may affect the flexibility of the detection models. These limitations make the existing solutions no longer suitable for the real-time detection of the changes in the status of the monitored object. In this paper, we propose a new Abnormal conditions Detection approach (ADCMO) on the Continuously Monitored Object. ADCMO incorporates the data distribution property to carry out the anomaly quantification for the data in the data stream, and then the abnormal conditions of the monitored object are quantified by the anomaly information of the current data and historical data. Finally, the anomaly state is adaptively determined by the distribution of anomaly coefficients. ADCMO can detect the changes in the status by calculating the outlier coefficient of the object at each moment and adaptively make the abnormal early warning. The experiments show that ADCMO can do this any-time, dynamically, efficiently and effectively.

This paper proposes how to compute division over finite field GF(2^n) that could cover the case of zero divisor based on tile assembly model. Key functions are accomplished by combining n final configurations of two different sub-models. The final configuration of every sub-model contains 2n^2-n computation tiles which perform 10 different functions. The coding ways of most computation tiles are optimized than the previous model to improve the complexity. The highest bit of modulus number is omitted to simplify the encoding way of computation tiles in the assembly process. This model requires 4591 types of computation tiles and 13 types of boundary tiles. The assembly time complexity is 2n^2-1 and the space complexity is n^4.

This paper discusses how to accomplish point-addition, a basic operation in conic curves cryptosystem over finite field GF(2^n), using DNA tile self-assembly. The assembly process is divided into two steps mapping two different sub-models. One of them is a new designed model to generate the input parameters for the second step. The other one is an existing model to compute division over finite field GF(2^n) proposed in our previous work. All possible input cases are considered in the assembly process. The time complexity is T(n)=2n^2+n-1 and space complexity is S(n)=n^4+n^3. It requires 6372 types of computation tiles and 78 types of boundary tiles.

Coupling losses for all-fiber integration of subwavelength core hybrid optical fibers have been first analyzed and optimized in detail. We have taken into account the effects of mode size, mode shape, effective refractive index, tapering beat length, interface, and endface reflections on the coupling loss. By optimizing parameters such as the subwave-length core diameter, single mode condition, and transition length, the coupling losses can be reduced. In addition, the influence of the core fabrication and misalignment tolerances have been explored. The results will benefit the all-fiber integration of hybrid optical fibers and their nanoengineering realization.

Erasure codes, such as Reed–Solomon (RS) codes and local reconstruction codes (LRCs), are being increasingly adopted in distributed storage systems since they offer lower redundancy than data replication. While these codes significantly save storage space, they can incur large I/O overhead and network traffic in reconstructing unavailable data. Most existing storage systems use replication for hot data and an erasure code for warm and cold data, thereby achieving a good tradeoff between storage overhead and recovery performance. However, these storage systems do not take the access characteristics of data into account and tend to use only an erasure code, which hinders the possibility of reducing storage overhead and recovery cost. In this paper, we propose a new adaptive coding selection method that instead uses multiple LRCs for warm data. The LRCs are selected based on the access characteristics of the data. Each time a file is accessed, we assume that each of the involved data blocks is unavailable, in turn. It is necessary to calculate the I/O cost to recover unavailable blocks for different LRCs. The sum of the I/O costs for each LRC is calculated, and the LRC with the minimal I/O cost is selected for warm data. For cold data, we use an RS code that is optimized for storage overhead to reduce the storage burden. Our method is implemented on the top of the Hadoop distributed file system. Evaluations show that it reduces the storage overhead by up to 5% and the reconstruction traffic by up to 22%.

In view of the problem of load imbalance for distributed file systems, the current solutions either adopt centralized algorithm and implement load balancing by increasing the number of files as the replica, or lack considerations for network transmission and network bandwidth. Therefore, the current solutions are relatively inefficient in distributed file system. To solve this problem, this paper proposes DDLB, a dynamic and distributed load balancing algorithm which is completely based on distributed load balancing architecture. DDLB can not only monitor the IO load on the data server dynamically and real time, but also use the load prediction model, which based on weighted mean, to reduce the impact of network delay and collection delay on load decision-making. In order to minimize the frequency of remote load collection and decrease network bandwidth occupation, DDLB adopts the remote distributed load collection method based on threshold and the cooling collection mechanism. Moreover, dynamic replica management mechanism of DDLB takes data blocks instead of files as the basic unit for replica replication, and reduces the IO load of the data server by adding replicas of hotspot data blocks. For proving the effectiveness of DDLB, this paper conducts a comprehensive evaluation in the HDFS cluster environment. The results show that compared with the traditional load balancing algorithm, DDLB can effectively balance the IO load between data servers in the distributed file system and improve the data access performance of IO-intensive applications.

MPI(Message Passing Interface) applications are widely used to solve complex problems in the field of science and engineering. In addition to calculation information concerns, communication information is also an important focal point for MPI application analysis. The communication information is generally obtained through manual reviewing the source code, which is impractical for complex applications. Another way is to develop an instrumentation library and compile the target application with the library. Despite the communication information can be accurately obtained by this type of methods, the target application must be executed. To address these issues, in this paper, a source code analysis method is proposed to get the MPI applications communication information, which does not need to execute the target application and can work with general computers. Furthermore, a parallel strategy is presented to accelerate the source code analysis process. Two frequently-used communication patterns are used to evaluate the proposed method. The experimental results show that the method proposed in this paper not only can be ran on general computers, but also reduce the analysis overhead, and can also achieve efficiency advantages in the MPI applications with large message size.

Understanding the characteristics of workloads of the large-scale Clusters is the key to diagnose the system bottlenecks, making optimal configuration decisions, improving the system throughput and resource usage. Due to the diversity and multiview of the workload traces, featuring the good designs and bottlenecks by analyzing the workloads under realworld scenarios becomes increasingly challenging. This paper introduces a multiview based trace comparative analysis method by comparatively characterizing how the architecture, jobs, tasks, machines, and resources usage were managed among different platforms. A case study is performed which verified the effectiveness of our method by comparatively analyzing two most representative big traces: Google trace and Alibaba 2018 trace. Quantitative findings, together with the performance bottleneck inferences and suggestions are also presented. To the best of our knowledge, we are the first to perform such comparative empirical study on these two traces using a multiview based approach. Our multifaceted analyses and new findings not only reveal insights that we believe are useful for system designers, IT practitioners and users, but also can promote more researches on big trace data comparative analysis in large-scale clusters.

DNA cryptography becomes a burgeoning new area of study along with the fast-developing of DNA computing and modern cryptography. Point-doubling, point-addition and point-multiplication are three fundamental point-operations to construct encryption protocols in some cryptosystem over mathematical curves such as elliptic curves and conic curves. This paper proposes a DNA computing model to calculate point-doubling in conic curves cryptosystem over finite field GF(2^n). By decomposing and rearranging the computing steps of point-doubling, the assembly process could be fulfilled by using 8 different types of computation tiles performing different functions with 1097 encoding ways. This model could also figure out point-multiplication if its coefficient is 2^k. The assembly time complexity is (2k+1)n-k-1, and the space complexity is (kn)^2+k(n^2)-(k^2)n.

This paper presents a fully convolutional neural network to tackle the mapping between single view RGB images and depth maps. To regress the depth maps from monocular images, we leverage deep short skip connections in residual learning for extracting features rather than using hand-crafted features. We further propose long skip connections in up-sampling stage to reuse the feature maps which is proved to enhance the result experimentally. To show the impact of loss functions in monocular depth map predictions, we train our model with kind of loss functions and compare the results qualitatively and quantitatively. The proposed model outperforms all current state-of-the-art results with less training data as well as less than half of training epochs in two standard benchmark data sets without any post-processing procedures or other refinement steps.

At present, many Internet of Things services are monitored and controlled by smart phone applications. The combination of Internet of Things and smart phones provides users with many convenient services. However, these convenient services also have potential security problems, such as privacy leakage or remote intrusion. Attackers are extending the scope of attacks from existing PC and Internet environments to mobile devices. In this paper, we extract the characteristics of the network traffic generated during the Internet connection, then use the information gain algorithm to select the discriminant classification features, establish the classifier by Bayesian model updating method which is an improved algorithm based on Bayesian theory, and compare with other machine learning classifiers such as k-nearest neighbor (KNN), SVM and J48, improved algorithm has good performance on validity, accuracy, efficiency and strong practicability.

5G is supposed to support not only explosive growth of mobile traffic but also massive number of connected devices. Simply evolving current 4G Long Term Evolution (LTE) networks can't meet the demand of 5G. 3rd Generation Partnership Project (3GPP) has proposed Service-Based Architecture (SBA) core network in release 15. But SBA also brings great complexity and big challenges to core network development and deployment. To solve challenging requirements related to SBA, 3GPP is working on enhancements to SBA design in release 16. We propose an enhanced SBA design called Not-Only-Stack (NoStack), including efficient inter-service communication mechanism and management plane for Network Functions (NFs) managing. The main idea of NoStack is to decouple business logic from inter-service communication to improve service agility. We also carry out some evaluations on performance of different inter-service communication mechanisms.

Heterogeneous wireless network selection is considered as one of the key technologies to utilize the network resources in next generation communication networks. In this paper, we propose a network selection scheme with multiple traffics based on analytic hierarchy process (AHP) and evolutionary game. The utility function used in this paper is designed as a compromise between available capacity and other network attributes. After analyzing the dynamics of populations' behavior, the existence of evolutionary equilibrium is analyzed and proved by simulations. Simulations show that our scheme has a better performance of per-user switchings than the commonly used AHP scheme.

Pattern division multiple access (PDMA) is a multi- carrier non-orthogonal multiple access (NOMA), which can meet the requirements of massive user connections and super-high data rate in the fifth generation (5G) wireless networks. In this paper, we work on the optimization of power allocation to improve the performance of the downlink PDMA system significantly. Considering the perfect channel state information (CSI) is acquired at the transmitter, we propose an iterative power allocation (IPA) scheme based on the integration of the iterative subgradient method and the Mann iterative method. Moreover, Lagrange multipliers and the allocated power are updated until converged in each iteration. It is demonstrated in simulation results that the achievable sum throughput superiority of our proposed scheme over other schemes.

Bandwidth is an important performance metric for high-performance computer network (HINET). How to determine large-scale HINET path bandwidths quickly and precisely is a highly pivotal challenge for parallel application performance analysis and prediction, optimizing end-to-end data transport performance, network management, etc. However, it is hard to determine the entire network point-to-point bandwidths of HINET since it often costs amounts of measurement time. Existing path bandwidth measurement methods focus on improving the accuracy of single path bandwidth measurement, however, lack considerations for reducing the entire network path bandwidths measurement times. To address this issue, we present an efficient method to generate the full-scale point-to-point bandwidth measurement plan for rapidly obtaining each path bandwidth in HINET. For evaluating, a source routing Fat-tree simulator and comprehensive experiments were conducted for different application scenarios. Both experimental and analysis showed that our method can reduce the measurement times significantly. In particular, compared to the exhaustive method, the measurement times of our method are one in 185 for the entire path bandwidth fault and can save 95% measurement times in 1024 nodes network with synthetic traffic.

Massive multiple-input-multiple-output (MIMO) offers huge advantages in terms of energy efficiency, spectral efficiency, robustness, and reliability and has been considered as a leading 5G technology candidate. However, high cost and energy consumption are major challenges in massive MIMO systems. Antenna selection at the base station side by reducing the number of radio-frequency (RF) chains is a practical and effective technology to solve high cost and energy consumption problems. In this paper, we investigate indoor massive MIMO antenna selection based on the measured data in a indoor office scenario at 3.52 GHz by using a uniform linear array (ULA) with 128 transmit (Tx) antennas and 7 receiving (Rx) users. A convex optimization algorithm is applied to select optimal Tx antenna subset by maximizing dirty-paper coding (DPC) capacity. Then, the antenna selection system performance is researched. The investigation shows that indoor antenna selection based on measured data can both improve the system performance and reduce the cost.

The least mean square (LMS) algorithm is a kind of typical adaptive filter algorithms. The algorithm of channel estimation algorithm based on LMS is no need to know the characteristics of channel and noise statistics, which make full use of channel correlation between front and back to reduce the influence of noise on channel estimation performance. In this paper, the LMS algorithm has been improved under the MIMO-OFDM systems. Then, a revised method for variable step size has been put forward, which accelerates the rate of convergence to improve channel estimation performance preferably. The simulation results show that the algorithm proposed in this paper not only has the fastest convergence compared with LMS and NLMS algorithm, but also has a better channel estimation(CE) performance than other estimation algorithms.

Non-orthogonal multiple access (NOMA) has been considered as a key candidate technology for 5G wireless systems due to its superior spectral efficiency and system capacity. With the ever-development of wireless body sensor networks (WBSNs) and wireless communications, supporting real-time communications in the telemedicine communication becomes a very important challenge. In this article, our goal is to maximize the overall system throughput by optimizing sub-band allocation and power allocation, thereby improving real-time performance in telemedicine communications. Assuming that the base station could obtain perfect channel state information (CSI), we propose a suboptimal sub-band allocation algorithm with low complexity. In the power allocation scheme, the closed-form expressions of power allocation factors for multiplexed users on each subband are obtained based on the Karush-Kuhn-Tucker (KKT) optimality conditions. Simulation results demonstrate that the proposed resource allocation scheme is closest to the ideal subband allocation based on exhaustive search and is superior to other schemes.

We consider the cooperative rate splitting scheme in device-to-device (D2D) underlay cellular network to reduce the interference causing performance degradation of communication. At the transmitter end, the transmitted information is split into two parts: common information and private information. The common information is exchanged between base station and D2D transmitter, and encoded together. At the receiver end, the common information is firstly decoded jointly and cancelled out. In this paper, we derive the optimal power allocation factors for the common information and the private information to maximize the sum rate, just using the large-scale fading information which is known at the base station which can reduce the computational complexity greatly. The simulation results show that the proposed scheme achieves higher sum rate than traditional schemes.

Since the Local Outlier Factor (LOF) was first proposed, there is a large family of approaches that is derived from it. For the reason that the existing local outliers detection approaches only focus on the extent of overall separation between an object and its neighbors, and do not pay attention to the degree of dispersion between them, the precision of these approaches will be seriously affected in the scattered data sets for outlier detection. In this paper, we redefine the local outliers by combining the degree of dispersion of the object and its neighbors, and propose a new local outlier detection approach (N2DLOF). Compared to conventional approaches, the outliers obtained by N2DLOF are more sensitive to the degree of anomaly of the scattered data sets. Experiments show that our approach has a significant improvement on outlier detection precision in the case of scattered datasets with similar time complexity. In short, we extend the ecosystem of the local outlier detection approaches from a new perspective.

With the vigorous development of the information era, the volume of data in file systems has grown to TB or EB. Metadata search determines the speed of retrieving required files in such large-scale file systems. There are many researches on metadata search based on different theories, and have made great achievements. Some researches show that the distribution of metadata presents sub-tree locality and horizontal locality, and metadata search presents heavy-tailed distribution. However, current methods do not make full use of the spatial locality and load characteristics in metadata search. In this paper, we put forward a metadata search method based on keyword-index. Our method fully exploits characteristics of metadata distribution and metadata search by reasonably partitioning indexes. The experimental result shows that our partitioning method is more efficient than current non-partitioning method.

Device-to-device (D2D) communication has been proposed as a solution for vehicle-to-vehicle (V2V) communication, which has strict requirements on reliability and latency. Previous works on V2V communication typically assume single-antenna users, which ignore the potential of multiple antennas. By contrast, this paper studies an uplink power control problem in multi-antenna V2V underlay cellular networks. We first derive the lower bounds on ergodic rates of multi-antenna V2V users and cellular users for maximum-ratio combining and zero-forcing detection. Using these rate lower bounds, we formulate a large-scale fading based power control problem to maximize the sum rate of cellular users while introducing the rate constraints of V2V users. Moreover, the geometric programming technique is adopted to solve the optimization power control problem. Simulation results show that the proposed rate-constrained power control scheme provides more reliable V2V communications with higher data rates at the price of a small loss of network sum rate, compared to the fixed maximum power scheme.

A brand new design of slot waveguide is proposed using a subwavelength hollow-core fiber, which enables light to be confined in an extremely small hollow-core with long interaction length. The slot-waveguide fibers (SWFs) have specific advantages in ultra-low volume optofluidic sensing compared with other waveguide structures. The guiding properties and optimization of the design to maximize its sensitive have also been investigated.

A novel design of an exposed antiresonant (AR) nodeless hollow core photonic crystal fiber (NHC-PCF) for gas sensing was proposed, which could enable gas samples to diffuse in or out of the AR NHC-PCF through the open cladding within a very short time. The unique properties of the designed AR NHC-PCF were investigated in detail, such as mode pattern, fraction of power in the central core, confinement loss and response time. The advantages and potential applications of these exposed AR NHC-PCF are discussed.

Ultra dense networks (UDN) can be considered as one of the key technologies of 5G, since it can improve capacity and spectral efficiency. With the densification of cell networks, the signals received by cell edge users may be from more than one cell. In this situation, coordinated multi-points (CoMP) technology can be used to improve the sum rate in the system. Considering the constraints of resource allocation posed by CoMP, a CoMP based on cell load-aware (CLA-CoMP) is proposed. It can balance the cell load by adjusting the number of users serviced by small base stations. The relationship between small base stations and users being finalized. And then, the power allocation is considered. In order to improve the sum rate in the system, a power allocation based on proportional fairness (PF-PA) is proposed. It can allocate more power to better users with good channel state. The simulation results show that, CLA-CoMP can improve sum rate compared with traditional CoMP and PF-PA can improve sum rate compared with the average power allocation in the system. As a result, joint CLA-CoMP and PF-PA can obtain the maximum system performance gain.

Sparse code multiple access (SCMA) is a new non-orthogonal multiple-access technique in future wireless networks. In SCMA uplink burst transmission, user detection and channel estimation require significant pilot overhead. Dispersive channels with relative small coherent frequency / time, as well as fading diversity techniques, both lead to a result that multiple fading blocks are taken during one transmission. In this paper, a new method of separating SCMA user detection and channel estimation among different fading blocks in dispersive channels is proposed to reduce pilot overhead. Further more, according to the sparsity of SCMA codebooks, a new mapping pattern from SCMA layers to physical resources is proposed to improve channel estimation performance with given pilots. The effectiveness of the proposal are described and analyzed, meanwhile the complexity is evaluated. Numerical simulations are performed to substantiate the performance of the proposed algorithm.

Fixed polarity Reed-Muller expressions (FPRMs) are well-suited for many practical applications due to they have many excellent properties. In order to obtain an optimal FPRM with fewest product terms, we propose an efficient minimization algorithm (EMA-FPRMs) for FPRMs. The main idea behind the EMA-FPRMs is that, firstly, the incompletely specified Boolean function is transformed into the zero polarity incompletely specified fixed polarity RM expression (ISFPRM) by using the proposed ISFPRM acquisition algorithm; secondly, the polarity and allocation of don't care terms of ISFPRM is encoded as chromosome; lastly, the optimal FPRM with fewest product terms is obtained by using genetic algorithm (GA), in which the FPRM that corresponds to the given chromosome is obtained by using the proposed chromosome conversion algorithm. The experimental results on MCNC benchmark circuits show that compared with the traditional polarity optimization approach which neglects the don't care terms, the EMA-FPRMs is highly effective in minimizing the number of product terms of FPRMs. Moreover, the EMA-FPRMs is faster than the GA based minimization algorithm which also considers the don't care terms.

With the advent of DNA computing, some traditional scientific disciplines present new developing orientations. One of them is DNA-based cryptography, a new developing interdisciplinary area which combines cryptography, mathematical modeling, biochemistry and molecular biology. There are some questions worth study that how to implement the arithmetic operations used in cryptosystem based on DNA computing. This paper proposes a DNA computing model to show how to calculate inversion over finite field GF(2n) with DNA self-assembly. 4567 types of computation tiles with 7 different functions assemble into the seed configuration with inputs to figure out the solution. The assembly time complexity is Q(n2) and the space complexity is Q(n4).

With the rapid development of DNA computing, there are some questions worth study that how to implement the arithmetic operations used in cryptosystem based on DNA computing models. This paper proposes an improved DNA computing model to calculate modular-multiplication over finite field GF(2n). Comparing to related works, both assembly time complexity and space complexity are more optimal. The computation tiles performing 4 different functions assemble into the seed configuration with inputs to figure out the result. It is given that how the computation tiles be bitwise coded and how assembly rules work. The assembly time complexity is Θ(n) and the space complexity is Θ(n2). This model requires 148 types of computation tiles and 8 types of boundary tiles.