There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ‘big data’. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mechanism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the existing approaches using Brewer’s CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.
The consensus problem for general linear multi-agent systems (MASs) under directed topology is investigated. First, a novel consensus protocol based on proportional-integral-derivative (PID) control is proposed. Second, the consensus problem is converted into an asymptotic stability problem through transformations. Third, through a state projection method the consensus condition is proved and the explicit expression of the consensus function is given. Then, a Lyapunov function is constructed and the gain matrices of the protocol are given based on the linear matrix inequality. Finally, two experiments are conducted to explain the advantages of the method. Simulation results show the effectiveness of the proposed algorithm.
Various binary similarity measures have been employed in clustering approaches to make homogeneous groups of similar entities in the data. These similarity measures are mostly based only on the presence or absence of features. Binary similarity measures have also been explored with different clustering approaches (e.g., agglomerative hierarchical clustering) for software modularization to make software systems understandable and manageable. Each similarity measure has its own strengths and weaknesses which improve and deteriorate the clustering results, respectively. We highlight the strengths of some well-known existing binary similarity measures for software modularization. Furthermore, based on these existing similarity measures, we introduce several improved new binary similarity measures. Proofs of the correctness with illustration and a series of experiments are presented to evaluate the effectiveness of our new binary similarity measures.
Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination, rotation, and source differences. The scale-invariant feature transform (SIFT) algorithm has been used successfully in satellite image registration problems. Also, many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness, this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore, high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information, which are important factors in content-based image retrieval (CBIR) applications. In this paper, a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class, linear discriminant analysis (LDA) is used to transform the local features to global features while reducing dimensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.
For accurate and stable haptic rendering, collision detection for interactive haptic applications has to be done by filling in or covering target objects as tightly as possible with bounding volumes (spheres, axis-aligned bounding boxes, oriented bounding boxes, or polytopes). In this paper, we propose a method for creating bounding spheres with respect to the contact levels of details (CLOD), which can fit objects while maintaining the balance between high speed and precision of collision detection. Our method is composed mainly of two parts: bounding sphere formation and two-level collision detection. To specify further, bounding sphere formation can be divided into two steps: creating spheres and clustering spheres. Two-level collision detection has two stages as well: fast detection of spheres and precise detection in spheres. First, bounding spheres are created for initial fast probing to detect collisions of spheres. Once a collision is probed, a more precise detection is executed by examining the distance between a haptic pointer and each mesh inside the colliding boundaries. To achieve this refined level of detection, a special data structure of a bounding volume needs to be defined to include all mesh information in the sphere. After performing a number of experiments to examine the usefulness and performance of our method, we have concluded that our algorithm is fast and precise enough for haptic simulations. The high speed detection is achieved through the clustering of spheres, while detection precision is realized by voxel-based direct collision detection. Our method retains its originality through the CLOD by distance-based clustering.
The Clifford Fourier transform (CFT) can be applied to both vector and scalar fields. However, due to problems with big data, CFT is not efficient, because the algorithm is calculated in each semaphore. The sparse fast Fourier transform (sFFT) theory deals with the big data problem by using input data selectively. This has inspired us to create a new algorithm called sparse fast CFT (SFCFT), which can greatly improve the computing performance in scalar and vector fields. The experiments are implemented using the scalar field and grayscale and color images, and the results are compared with those using FFT, CFT, and sFFT. The results demonstrate that SFCFT can effectively improve the performance of multivector signal processing.
A new force is introduced in the social force model (SFM) for computing following behavior in pedestrian counterflow, whereby an individual tries to approach others in the same direction to avoid conflicts with pedestrians from the opposite direction. The force, like a kind of gravitation, is modeled based on the movement state and visual field of the pedestrian, and is added to the classical SFM. The modified model is presented to study the impact of following behavior on the process of lane formation, the conflict, the number of lanes formed, and the traffic efficiency in the simulations. Simulation results show that the following behavior has a significant effect on the phenomenon of lane formation and the traffic efficiency.
Electrical pole-changing technology leads to torque ripple and speed fluctuation despite broadening the constant power speed range of the multiphase induction machine (IM) system. To reduce the torque ripple and speed fluctuation of the machine, we investigate an exponential response electrical pole-changing method for five-phase IM with a current sliding-mode control strategy. This control strategy employs the dual-plane (d1–q1 and d2–q2) vector control method, which allows the IM to operate under different pole modes. Current sliding-mode controllers are applied instead of conventional proportional integral (PI) controllers to adjust the current vectors, and exponential current response achieves a smooth transition between the d1–q1 and d2–q2 planes. Compared with the step response pole-changing with PI control method, the proposed pole-changing method greatly reduces the torque ripple and speed fluctuation of the IM during the pole-changing process. Experimental results verify the exceptional performance of the proposed electrical pole-changing strategy.
Passive source localization via a maximum likelihood (ML) estimator can achieve a high accuracy but involves high calculation burdens, especially when based on time-of-arrival and frequency-of-arrival measurements for its internal nonlinearity and nonconvex nature. In this paper, we use the Pincus theorem and Monte Carlo importance sampling (MCIS) to achieve an approximate global solution to the ML problem in a computationally efficient manner. The main contribution is that we construct a probability density function (PDF) of Gaussian distribution, which is called an important function for efficient sampling, to approximate the ML estimation related to complicated distributions. The improved performance of the proposed method is attributed to the optimal selection of the important function and also the guaranteed convergence to a global maximum. This process greatly reduces the amount of calculation, but an initial solution estimation is required resulting from Taylor series expansion. However, the MCIS method is robust to this prior knowledge for point sampling and correction of importance weights. Simulation results show that the proposed method can achieve the Cramér-Rao lower bound at a moderate Gaussian noise level and outperforms the existing methods.
A two-step gate-recess process combining high selective wet-etching and non-selective digital wet-etching techniques has been proposed for InAlAs/InGaAs InP-based high electron mobility transistors (HEMTs). High etching-selectivity ratio of InGaAs to InAlAs material larger than 100 is achieved by using mixture solution of succinic acid and hydrogen peroxide (H2O2). Selective wet-etching is validated in the gate-recess process of InAlAs/InGaAs InP-based HEMTs, which proceeds and automatically stops at the InAlAs barrier layer. The non-selective digital wet-etching process is developed using a separately controlled oxidation/de-oxidation technique, and during each digital etching cycle 1.2 nm InAlAs material is removed. The two-step gate-recess etching technique has been successfully incorporated into device fabrication. Digital wet-etching is repeated for two cycles with about 3 nm InAlAs barrier layer being etched off. InP-based HEMTs have demonstrated superior extrinsic transconductance and RF characteristics to devices fabricated during only the selective gate-recess etching process because of the smaller gate to channel distance.
We investigate a multifunctional n-step honeycomb network which has not been studied before. By adjusting the circuit parameters, such a network can be transformed into several different networks with a variety of functions, such as a regular ladder network and a triangular network. We derive two new formulae for equivalent resistance in the resistor network and equivalent impedance in the LC network, which are in the fractional-order domain. First, we simplify the complex network into a simple equivalent model. Second, using Kirchhoff’s laws, we establish a fractional difference equation. Third, we construct an equivalent transformation method to obtain a general solution for the nonlinear differential equation. In practical applications, several interesting special results are obtained. In particular, ann-step impedance LC network is discussed and many new characteristics of complex impedance have been found.