A cost-optimal parallel algorithm for the 0–1 knapsack problem and its performance on multicore CPU and GPU implementations

The 0–1 knapsack problem has been extensively studied in the past years due to its immediate applications in industry and financial management, such as cargo loading, stock cutting, and budget control. Many algorithms have been proposed to solve this problem, most of which are heuristic, as the problem is well-known to be NP-hard. Only a few optimal algorithms have been designed to solve this problem but with high time complexity. This paper proposes the cost-optimal parallel algorithm (COPA) on an EREW PRAM model with shared memory to solve this problem. COPA is scalable and yields optimal solutions consuming less computational time. Furthermore, this paper implements COPA on two scenarios – multicore CPU based architectures using Open MP and GPU based configurations using CUDA. A series of experiments are conducted to examine the performance of COPA under two different test platforms. The experimental results show that COPA could reduce a significant amount of execution time. Our approach achieves the speedups of up to 10.26 on multicore CPU implementations and 17.53 on GPU implementations when the sequential dynamic programming algorithm for KP01 is considered as a baseline. Importantly, GPU implementations outstand themselves in the experimental results.     

Mobile application for hydrogeologic field investigations

An Android application, HyDroid, was developed to help hydrogeologists conduct field investigations. By employing the powerful hardware and operation systems in the Android smartphone, HyDroid not only facilitates geospatial data collection and management, but also helps to conduct some essential hydrogeologic field tests, and to visualize field data instantly.     

Some characteristics of high strength fiber reinforced lightweight aggregate concrete

The effect of polypropylene and steel fibers on high strength lightweight aggregate concrete is investigated. Sintered fly ash aggregates were used in the lightweight concrete; the fines were partially replaced by fly ash. The effects on compressive strength, indirect tensile strength, modulus of rupture, modulus of elasticity, stress–strain relationship and compression toughness are reported. Compared to plain sintered fly ash lightweight aggregate concrete, polypropylene fiber addition at 0.56% by volume of the concrete, caused a 90% increase in the indirect tensile strength and a 20% increase in the modulus of rupture. Polypropylene fiber addition did not significantly affect the other mechanical properties that were investigated. Steel fibers at 1.7% by volume of the concrete caused an increase in the indirect tensile strength by about 118% and an increase in the modulus of rupture by about 80%. Steel fiber reinforcement also caused a small decrease in the modulus of elasticity and changed the shape of the stress–strain relationship to become more curvilinear. A large increase in the compression toughness was recorded. This indicated a significant gain in ductility when steel fiber reinforcement is used.     

Logarithm-approximate floating-point multiplier is applicable to power-efficient neural network training

Recently, emerging “edge computing” moves data and services from the cloud to nearby edge servers to achieve short latency and wide bandwidth, and solve privacy concerns. However, edge servers, often embedded with GPU processors, highly demand a solution for power-efficient neural network (NN) training due to the limitation of power and size. Besides, according to the nature of the broad dynamic range of gradient values computed in NN training, floating-point representation is more suitable. This paper proposes to adopt a logarithm-approximate multiplier (LAM) for multiply-accumulate (MAC) computation in neural network (NN) training engines, where LAM approximates a floating-point multiplication as a fixed-point addition, resulting in smaller delay, fewer gates, and lower power consumption. We demonstrate the efficiency of LAM in two platforms, which are dedicated NN training hardware, and open-source GPU design. Compared to the NN training applying the exact multiplier, our implementation of the NN training engine for a 2-D classification dataset achieves 10% speed-up and 2.3X efficiency improvement in power and area, respectively. LAM is also highly compatible with conventional bit-width scaling (BWS). When BWS is applied with LAM in five test datasets, the implemented training engines achieve more than 4.9X power efficiency improvement, with at most 1% accuracy degradation, where 2.2X improvement originates from LAM. Also, the advantage of LAM can be exploited in processors. A GPU design embedded with LAM executing an NN-training workload, which is implemented in an FPGA, presents 1.32X power efficiency improvement, and the improvement reaches 1.54X with LAM + BWS. Finally, LAM-based training in deeper NN is evaluated. Up to 4-hidden layer NN, LAM-based training achieves highly comparable accuracy as that of the accurate multiplier, even with aggressive BWS.     

基于任务链的实时多任务软件可靠性建模

提出了以实时多任务系统中的任务链为组件,任务链运行时间比例作为组件的权重系数,建立实时多任务软件的可靠性建模方法。该方法符合实时多任务系统应用软件运行的实时情况,拓展了实时系统可靠性建模的思路,其可靠性模型可以给出单个任务链的可靠性参数,且建模方法还可以根据不同任务链的重要程度,进行可靠性分配并决定软件测试和投放时间。     

基于B/S 模式的毕业设计全过程质量监控管理系统

毕业设计是对本科生四年所学知识的综合分析和运用能力的培养,也是对高校教学水平的检验．毕业设计管理是高校教学管理工作的重要环节,对毕业设计论文质量影响很大．由于传统的毕业设计管理存在着选题效率低、换题频率大、过程跟踪、指导不及时的弊病,为加强毕业设计过程监控管理和规范,提高毕业论文质量,在对现有毕业设计管理系统进行研究的情况下,融入了全面质量管理思想和工作流技术,研究开发了B／S模式的毕业设计管理信息系统,通过6年来的应用已非常完善,为高校毕业设计过程管理提供了一个功能强大的信息平台．     

Supervised bilateral two-dimensional locality preserving projection algorithm based on Gabor wavelet

Bilateral two-dimensional locality preserving projection (B2DLPP) is an effective method for unsupervised linear dimensionality reduction, which directly extracts face features from image matrices based on locality criterion. Motivated by B2DLPP, this paper proposes a supervised bilateral two-dimensional locality preserving projection (SB2DLPP). Different from B2DLPP, the proposed method takes into account the class information when constructing the similarity matrix. It increases inter-class distance in the projection space so that better right and left-projection matrices are obtained. Furthermore, a Gabor-based supervised bilateral two-dimensional locality preserving projection method is proposed for face recognition. Gabor wavelet representations are adopted for face images to make the proposed method robust to illumination variations and facial expression changes. Then, SB2DLPP is applied to reduce feature dimension. The performance of the proposed method is evaluated and compared with other traditional face recognition schemes on the FERET, Yale and JAFFE databases. The experiment results demonstrate the effectiveness and superiority of the proposed approach.