FPGA implementation and performance evaluation of a high throughput crypto coprocessor

This paper describes the FPGA implementation of FastCrypto, which extends a general-purpose processor with a crypto coprocessor for encrypting/decrypting data. Moreover, it studies the trade-offs between FastCrypto performance and design parameters, including the number of stages per round, the number of parallel Advance Encryption Standard (AES) pipelines, and the size of the queues. Besides, it shows the effect of memory latency on the FastCrypto performance. FastCrypto is implemented with VHDL programming language on Xilinx Virtex V FPGA. A throughput of 222 Gb/s at 444 MHz can be achieved on four parallel AES pipelines. To reduce the power consumption, the frequency of four parallel AES pipelines is reduced to 100 MHz while the other components are running at 400 MHz. In this case, our results show a FastCrypto performance of 61.725 bits per clock cycle (b/cc) when 128-bit single-port L2 cache memory is used. However, increasing the memory bus width to 256-bit or using 128-bit dual-port memory, improves the performance to 112.5 b/cc (45 Gb/s at 400 MHz), which represents 88% of the ideal performance (128 b/cc).     

Parallel processing of continuous queries over data streams

In this paper, we propose parallel processing of continuous queries over data streams to handle the bottleneck of single processor DSMSs. Queries are executed in parallel over the logical machines in a multiprocessing environment. Scheduling parallel execution of operators is performed via finding the shortest path in a weighted graph called Query Mega Graph (QMG), which is a logical view of K machines. By lapse of time, number of tuples waiting in queues of different operators may be very different. When a queue becomes full, re-scheduling is done by updating weight of edges of QMG. In the new computed path, machines with more workload will be used less. The proposed system is formally presented and its correctness is proved. It is also modeled in PetriNets and its performance is evaluated and compared with serial query processing as well as the Min-Latency scheduling algorithm. The presented system is shown to outperform them w.r.t. tuple latency (response time), memory usage, throughput and also tuple loss- critical parameters in any data stream management systems.     

Investigating the effect of varying block size on power and energy consumption of GPU kernels

The Journal of Supercomputing - Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have...     

A hybrid algorithm for scheduling scientific workflows in IaaS cloud with deadline constraint

The Journal of Supercomputing - Scientific workflows are used to process large amounts of data and perform complex analyses; thus, they require powerful computing resources to produce the desired...     

A finite difference/finite element technique with error estimate for space fractional tempered diffusion-wave equation

An efficient numerical technique is proposed to solve one- and two-dimensional space fractional tempered fractional diffusion-wave equations. The space fractional is based on the Riemann–Liouville fractional derivative. At first, the temporal direction is discretized using a second-order accurate difference scheme. Then a classic Galerkin finite element is employed to obtain a full-discrete scheme. Furthermore, for the time-discrete and the full-discrete schemes error estimate has been presented to show the unconditional stability and convergence of the developed numerical method. Finally, two test problems have been illustrated to verify the efficiency and simplicity of the proposed technique.     

Corresponding regions for shadow restoration in satellite high-resolution images

ABSTRACT

High spatial resolution images available by satellites such as Ikonos, Quickbird, and WorldView-2 provide more information for remote sensing applications, such as object detection, classification, change detection, and object mapping. The presence of shadow reduces the amount of information that can be extracted and consequently makes these applications more difficult or even impossible. In this article, a shadow restoration approach for high-resolution satellite images is proposed. The approach detects the shadow area and segments the image into regions according to the land surface type. Then, shadow restoration is carried out for each region based on the degree of correspondence between shadow and neighbouring non-shadow regions. The proposed approach is applied to study areas from Ikonos and WorldView-2 satellite images. A comparison to the standard approaches for shadow restoration is performed, and an accuracy assessment is carried out by visual inspection and land-cover classification. The results show that the enhanced shadow regions using the proposed approach have better appearances and are highly compatible with their surrounding non-shadow regions. In addition, the overall accuracy is higher than those of the standard approaches.     

A tuned hybrid intelligent fruit fly optimization algorithm for fuzzy rule generation and classification

Fuzzy rule-based systems (FRBSs) are well-known soft computing methods commonly used to tackle classification problems characterized by uncertainties and imprecisions. We propose a hybrid intelligent fruit fly optimization algorithm (FOA) to generate and classify fuzzy rules and select the best rules in a fuzzy if–then rule system. We combine a FOA and a heuristic algorithm in a hybrid intelligent algorithm. The FOA is used to create, evaluate and update triangular fuzzy rule-based and orthogonal fuzzy rule-based systems. The heuristic algorithm is used to calculate the certainty grade of the rules. The parameters in the proposed hybrid algorithm are tuned using the Taguchi method. An experiment with 27 benchmark datasets and a tenfold cross-validation strategy is designed and carried out to compare the proposed hybrid algorithm with nine different FRBSs. The results show that the hybrid algorithm proposed in this study is significantly more accurate than the nine competing FRBSs.

     

Swing angle estimation for anti-sway overhead crane control using load cell

Overhead crane movement results in suspended load sway, which may cause dangers and damages. Common anti-sway methods are based on swing angle information. This paper presents a novel method that uses load cell sensors for swing angle estimation. According to our proposed method, a damping signal is generated and added to the speed reference in order to suppress the suspended load sway. Conventional methods of swing angle estimation are based on measurement by camera vision, acceleration, or some other type of sensor. Compared to conventional methods, the proposed method based on load cell is simpler to build and less sensitive to ambient conditions. The effectiveness of the method is verified by computer simulation.