Edwin H.-M. Sha

Bio: Edwin H.-M. Sha is an academic researcher from East China Normal University. The author has contributed to research in topics: Scheduling (computing) & Dynamic priority scheduling. The author has an hindex of 40, co-authored 388 publications receiving 5837 citations. Previous affiliations of Edwin H.-M. Sha include University of Texas at Dallas & Chongqing University.

Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems

Abstract: In high-level synthesis for real-time embedded systems using heterogeneous functional units (FUs), it is critical to select the best FU type for each task. However, some tasks may not have fixed execution times. This article models each varied execution time as a probabilistic random variable and solves heterogeneous assignment with probability (HAP) problem. The solution of the HAP problem assigns a proper FU type to each task such that the total cost is minimized while the timing constraint is satisfied with a guaranteed confidence probability. The solutions to the HAP problem are useful for both hard real-time and soft real-time systems. Optimal algorithms are proposed to find the optimal solutions for the HAP problem when the input is a tree or a simple path. Two other algorithms, one is optimal and the other is near-optimal heuristic, are proposed to solve the general problem. The experiments show that our algorithms can effectively reduce the total cost while satisfying timing constraints with guaranteed confidence probabilities. For example, our algorithms achieve an average reduction of 33.0p on total cost with 0.90 confidence probability satisfying timing constraints compared with the previous work using worst-case scenario.

A novel multiplexer-based low-power full adder

Abstract: The 1-bit full adder circuit is a very important component in the design of application specific integrated circuits. This paper presents a novel low-power multiplexer-based 1-bit full adder that uses 12 transistors (MBA-12T). In addition to reduced transition activity and charge recycling capability, this circuit has no direct connections to the power-supply nodes, leading to a noticeable reduction in short-current power consumption. Intensive HSPICE simulation shows that the new adder has more than 26% in power savings over conventional 28-transistor CMOS adder and it consumes 23% less power than 10-transistor adders (SERF and 10T ) and is 64% faster.

Trust prediction and trust-based source routing in mobile ad hoc networks

Abstract: Mobile ad hoc networks (MANETs) are spontaneously deployed over a geographically limited area without well-established infrastructure. The networks work well only if the mobile nodes are trusty and behave cooperatively. Due to the openness in network topology and absence of a centralized administration in management, MANETs are very vulnerable to various attacks from malicious nodes. In order to reduce the hazards from such nodes and enhance the security of network, this paper presents a dynamic trust prediction model to evaluate the trustworthiness of nodes, which is based on the nodes’ historical behaviors, as well as the future behaviors via extended fuzzy logic rules prediction. We have also integrated the proposed trust predication model into the Source Routing Mechanism. Our novel on-demand trust-based unicast routing protocol for MANETs, termed as Trust-based Source Routing protocol (TSR), provides a flexible and feasible approach to choose the shortest route that meets the security requirement of data packets transmission. Extensive experiments have been conducted to evaluate the efficiency and effectiveness of the proposed mechanism in malicious node identification and attack resistance. The results show that TSR improves packet delivery ratio and reduces average end-to-end latency.

Rotation Scheduling: A Loop Pipelining Algorithm

Abstract: We consider the resource-constrained scheduling of loops with inter-iteration dependencies. A loop is modeled as a data flow graph (DFG), where edges are labeled with the number of iterations between dependencies. We design a novel and flexible technique, called rotation scheduling, for scheduling cyclic DFGs using loop pipelining. The rotation technique repeatedly transforms a schedule to a more compact schedule. We provide a theoretical basis for the operations based on retiming. We propose two heuristics to perform rotation scheduling, and give experimental results showing that they have very good performance.

crowddeliver : Planning City-Wide Package Delivery Paths Leveraging the Crowd of Taxis

Abstract: Despite the great demand on and attempts at package express shipping services, online retailers have not yet had a practical solution to make such services profitable. In this paper, we propose an economical approach to express package delivery, i.e., exploiting relays of taxis with passengers to help transport package collectively, without degrading the quality of passenger services. Specifically, we propose a two-phase framework called crowddeliver for the package delivery path planning. In the first phase, we mine the historical taxi trajectory data offline to identify the shortest package delivery paths with estimated travel time given any Origin–Destination pairs. Using the paths and travel time as the reference, in the second phase we develop an online adaptive taxi scheduling algorithm to find the near-optimal delivery paths iteratively upon real-time requests and direct the package routing accordingly. Finally, we evaluate the two-phase framework using the real-world data sets, which consist of a point of interest, a road network, and the large-scale trajectory data, respectively, that are generated by 7614 taxis in a month in the city of Hangzhou, China. Results show that over 85% of packages can be delivered within 8 hours, with around 4.2 relays of taxis on average.

Implementing smart factory of Industrie 4.0: an outlook

Abstract: With the application of Internet of Things and services to manufacturing, the fourth stage of industrialization, referred to as Industrie 4.0, is believed to be approaching. For Industrie 4.0 to come true, it is essential to implement the horizontal integration of inter-corporation value network, the end-to-end integration of engineering value chain, and the vertical integration of factory inside. In this paper, we focus on the vertical integration to implement flexible and reconfigurable smart factory. We first propose a brief framework that incorporates industrial wireless networks, cloud, and fixed or mobile terminals with smart artifacts such as machines, products, and conveyors. Then, we elaborate the operational mechanism from the perspective of control engineering, that is, the smart artifacts form a self-organized system which is assisted with the feedback and coordination blocks that are implemented on the cloud and based on the big data analytics. In addition, we outline the main technical features and beneficial outcomes and present a detailed design scheme. We conclude that the smart factory of Industrie 4.0 is achievable by extensively applying the existing enabling technologies while actively coping with the technical challenges.

Disease Prediction by Machine Learning Over Big Data From Healthcare Communities

Abstract: With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care, and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. In this paper, we streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities. We experiment the modified prediction models over real-life hospital data collected from central China in 2013–2015. To overcome the difficulty of incomplete data, we use a latent factor model to reconstruct the missing data. We experiment on a regional chronic disease of cerebral infarction. We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital. To the best of our knowledge, none of the existing work focused on both data types in the area of medical big data analytics. Compared with several typical prediction algorithms, the prediction accuracy of our proposed algorithm reaches 94.8% with a convergence speed, which is faster than that of the CNN-based unimodal disease risk prediction algorithm.