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Designing single-board computers (SBCs) is becoming more challenging given the growing number of discrete components that are made available and the rate at which this number grows. Keeping track of all available components options, revisions, and functionalities is challenging for SBC designers who are striving for faster design cycles. Moreover, the procedure of deciding peripheral components, their values, and connections of an SBC is not only difficult because of various parameters that need to be considered but also is time consuming as there exist numerous components on a typical SBC nowadays. In this article, an SBC generator tool, FASCINET, is presented that uses a neural network (NN) model to design customized peripheral circuits for SBCs. The tool creates a large commercial off-the-shelf database (COTS DB) of existing components, efficiently searches through them, and selects optimal components for both main and peripheral components based on the user’s requirements. Creating such a broad COTS DB requires processing abundant datasheets. A manual approach is time consuming, even if only a fraction of all available datasheets is considered. In order to automate this process, this article describes a novel NN-based approach for automatically categorizing datasheets and proposes an extraction technique for parsing relevant functional information from tables within. Our evaluation using a test set that contains over 770 000 components shows that the category of datasheets is identified correctly over 95% of the time. Additionally, the table extractor has a precision above 96%. Our proposed fully autonomous SBC design approach reduces the time for generating the schematic of an SBC to as little as 2 min. For validating the accuracy of our model, the netlists of 400 SBCs designed by FASCINET are compared to the human-designed versions. This evaluation shows that FASCINET is able to design SBCs that are identical to the manually designed ones except for minor differences. [ABSTRACT FROM AUTHOR]

The problem of order and rack assignment to picking stations (ORAPS) is a key joint optimization problem in the order picking process of the robotic mobile fulfillment system (RMFS). Given a set of customer orders and a set of mobile racks, the goal of this problem is to simultaneously assign orders and racks to multiple picking stations so that the racks can provide products to meet the demand of orders by the minimal number of visits to all picking stations. In this article, we build a mathematical model for the ORAPS, which considers all orders assignment with the allocable capacity interval of the picking station and the rack product capacity limitation. To solve the ORAPS problem, we propose a two-stage hybrid heuristic algorithm (TS-HHA) consisting of the reducing stage and the assigning stage. In the reducing stage, a scheme of dynamic programming (DP) is introduced to find a critical rack set, which can focus attention on the most promising racks and increase the speed of problem-solving. In the assigning stage, we propose an optimization strategy that combines a constructive heuristic algorithm and adaptive neighborhood search (CH-ANS). It can generate a high-quality simultaneous assignment scheme and further improve its quality effectively. The computational results show that our proposed algorithm performs better than its competitors on both simulation and practical instances of the ORAPS problem. Note to Practitioners—This article investigates the order and rack assignment to picking stations (ORAPS) problem in the robotic mobile fulfillment system (RMFS) originated from a renowned Chinese logistics platform. We build a more comprehensive and reasonable mathematical programming model for this ORAPS problem and propose a two-stage hybrid heuristic algorithm (TS-HHA) strategy, which can simultaneously assign orders and racks to multiple picking stations so that all customer orders can be processed. The experimental results show that our TS-HHA performs at least 35% better than its competitors on all simulation and practical instances set. We believe that this research work can serve as a kind of generic framework for practical ORAPS problems and be very helpful for operating the RMFS efficiently. [ABSTRACT FROM AUTHOR]

In this paper, we propose a depth-wise flexible block processing method called split unit coding order (SUCO) for video coding. Conventionally, block-based image and video compression frameworks always apply raster scans to process blocks in order from left to right. Owing to the fixed coding order, the available information for prediction in the coding blocks is limited to adjacent blocks on the left and top. To address the limitations of block-based images and video compression frameworks, the proposed SUCO provides more flexibility in handling coding block sequences than predicted on the left and top, and thus coding blocks can take advantage of adjacent right information, such as reconstructed pixels and motion information. The flexibility is achieved by depth-wise signaling of preferred coding order for the given partitions. The experiment results demonstrate that the proposed SUCO can effectively improve the coding efficiency of both intra and inter prediction in the latest video coding standards. [ABSTRACT FROM AUTHOR]

In semiconductor manufacturing processes, yield analysis aims to increase the yield by determining and managing the causes of low yield. The variable data collected from semiconductor manufacturing processes, in which hundreds of unit processes are implemented according to specific conditions and sequences, are interdependent, and the variables related to previous processes influence the variables in subsequent processes. Therefore, the order of processes should be considered when building a model that searches for the causes of low yield. However, there have been few studies in this area. This paper proposes a low-yield root cause search method considering the order of processes using a long short-term memory with attention mechanism (LSTM-AM) model. Specifically, the LSTM-AM model is applied to data classified according to the process structure of semiconductor products, and the causes of low yield are determined considering the order of processes by extracting attention weights. Experiments are conducted to verify the suitability of the proposed method using real yield data from a semiconductor company. The experimental results confirm that the proposed method outperforms the existing low yield root cause search methods in terms of low yield prediction. [ABSTRACT FROM AUTHOR]

Today's blockchains suffer from low throughput and high latency, which impedes their widespread adoption of more complex applications like smart contracts. In this article, we propose a novel paradigm for smart contract execution. It distinguishes between consensus nodes and execution nodes: different groups of execution nodes can execute transactions in parallel; meanwhile, consensus nodes can asynchronously order transactions and process execution results. Moreover, it requires no coordination among execution nodes and can effectively prevent livelocks. We show two ways of applying this paradigm to blockchains. First, we show how we can make Ethereum support parallel and asynchronous contract execution without hard-forks. Then, we propose a new public, permissionless blockchain. Our benchmark shows that, with a fast consensus layer, it can provide a high throughput even for complex transactions like Cryptokitties gene mixing. It can also protect simple transactions from being starved by complex transactions. [ABSTRACT FROM AUTHOR]

Medium voltage cables are poor transmitters of signals with high frequency spectrum such as partial discharges. The propagating signals are strongly attenuated and distorted depending on the transmitted distance. In order to understand this process, this article provides a model to simulate the transmission of such signals on medium voltage cables. Compared to earlier approaches, this model does not neglect the wave character of the high frequency signals. Therefore, to describe the signal transmission, a comprehensible solution of the telegrapher's equations is provided. To work with this solution, the propagation constant of the medium voltage cable used must also be known. This propagation constant is calculated on the basis of the individual cable layers, considering all ohmic and dielectric losses. In contrast to previous methods, all primary line constants are modeled frequency dependent. The final transmission model is able to predict the spectrum of a transmitted signal at any distance from its origin. Validation measurements show that prediction and measurement agree with good accuracy. A possible application of the developed model is the investigation of the transmission of partial discharges on medium voltage cables. This model is probably also suitable for high voltage cables. [ABSTRACT FROM AUTHOR]

Motivated by applications in data center networks, in this paper, we study the problem of scheduling in an input queued switch. While throughput maximizing algorithms in a switch are well-understood, delay analysis was developed only recently. It was recently shown that the well-known MaxWeight algorithm achieves optimal scaling of mean queue lengths in steady state in the heavy-traffic regime, and is within a factor less than 2 of a universal lower bound. However, MaxWeight is not used in practice because of its high time complexity. In this paper, we study several low complexity algorithms and show that their heavy-traffic performance is identical to that of MaxWeight. We first present a negative result that picking a random schedule does not have optimal heavy-traffic scaling of queue lengths even under uniform traffic. We then show that if one picks the best among two matchings or modifies a random matching even a little, using the so-called flip operation, it leads to MaxWeight like heavy-traffic performance under uniform traffic. We then focus on the case of non-uniform traffic and show that a large class of low time complexity algorithms have the same heavy-traffic performance as MaxWeight, as long as it is ensured that a MaxWeight matching is picked often enough. We also briefly discuss the performance of these algorithms in the large scale heavy-traffic regime when the size of the switch increases simultaneously with the load. Finally, we perform empirical study on a new algorithm to compare its performance with some existing algorithms. [ABSTRACT FROM AUTHOR]

The efficiency of warehouses is vital to e-commerce. Fast order processing at the warehouses ensures timely deliveries and improves customer satisfaction. However, monitoring, analyzing, and manipulating order processing in the warehouses in real time are challenging for traditional methods due to the sheer volume of incoming orders, the fuzzy definition of delayed order patterns, and the complex decision-making of order handling priorities. In this paper, we adopt a data-driven approach and propose OrderMonitor, a visual analytics system that assists warehouse managers in analyzing and improving order processing efficiency in real time based on streaming warehouse event data. Specifically, the order processing pipeline is visualized with a novel pipeline design based on the sedimentation metaphor to facilitate real-time order monitoring and suggest potentially abnormal orders. We also design a novel visualization that depicts order timelines based on the Gantt charts and Marey's graphs. Such a visualization helps the managers gain insights into the performance of order processing and find major blockers for delayed orders. Furthermore, an evaluating view is provided to assist users in inspecting order details and assigning priorities to improve the processing performance. The effectiveness of OrderMonitor is evaluated with two case studies on a real-world warehouse dataset. [ABSTRACT FROM AUTHOR]

Most robust estimators require tuning the parameters of the algorithm for the particular application, a bottleneck for practical applications. The paper presents the multiple input structures with robust estimator (MISRE), where each structure, inlier or outlier, is processed independently. The same two constants are used to find the scale estimates over expansions for each structure. The inlier/outlier classification is straightforward since the data is processed and ordered with the relevant inlier structures listed first. If the inlier noises are similar, MISRE’s performance is equivalent to RANSAC-type algorithms. MISRE still returns the correct inlier estimates when inlier noises are very different, while RANSAC-type algorithms do not perform as well. MISRE’s failures are gradual when too many outliers are present, beginning with the least significant inlier structure. Examples from 2D images and 3D point clouds illustrate the estimation. [ABSTRACT FROM AUTHOR]

First arrival time picking is one of the critical processing steps of acoustic emission (AE)/microseismic (MS) monitoring for studying rock fracture processes. Because of massive monitoring data, the automatic arrival time picking technique is particularly desired. Inspired by recent successful applications of machine learning (ML) in earthquake phase identification, we propose a deep learning (DL)-based P-wave first arrival time picking method named AE Network (AEnet) for laboratory AE monitoring data. Our approach consists of two steps: classification and picking. The convolutional neural network (CNN) is used to classify each sample point of acoustic waveforms into either noise or signal. Different from prior DL-based phase picking studies using raw waveforms, we combine the waveform and high-order statistics as the input to enrich the input data features and accelerate the CNN model learning process. Our approach is examined using the laboratory AE monitoring data and the performance of each component of AEnet is also analyzed. The results show that the CNN model can classify the sample points accurately for the picking procedure. With this classification result, we pick the first arrival time of each trace using the curve fitting method and an unsupervised clustering algorithm. To evaluate the performance of AEnet, we apply Akaike Information Criterion-Short Term Averaging/Long Term Averaging Method (AIC-STA/LTA), one of the most popular and traditional picking methods, on the same waveforms and use the manual picks as the reference. Error analysis results show that AEnet outperforms AIC-STA/LTA. [ABSTRACT FROM AUTHOR]

In situ visualization is a technique in which plots and other visual analyses are performed in tandem with numerical simulation processes in order to better utilize HPC machine resources. Especially with unattended exploratory engineering simulation analyses, events may occur during the run, which justify supplemental processing. Sometimes though, when the events do occur, the phenomena of interest includes the physics that precipitated the events and this may be the key insight into understanding the phenomena that is being simulated. In situ temporal caching is the temporary storing of produced data in memory for possible later analysis including time varying visualization. The later analysis and visualization still occurs during the simulation run but not until after the significant events have been detected. In this article, we demonstrate how temporal caching can be used with in-line in situ visualization to reduce simulation run-time while still capturing essential simulation results. [ABSTRACT FROM AUTHOR]

In this article, we focus on the usage of MQTT (Message Queuing Telemetry Transport) within Connected Vehicles (CVs). Indeed, in the original version of MQTT protocol, the broker is responsible “only” for sending received data to subscribers; abstracting then the underlying mechanism of data exchange. However, within CVs context, subscribers (i.e., the processing infrastructure) may be overloaded with irrelevant data, in particular when the requirement is real or near real-time processing. To overcome this issue, we propose MQTT-CV; a new variant of MQTT protocol, in which the broker is able to perform local processing in order to reduce the workload at the infrastructure; i.e., filtering data before sending them. In this article, we first validate formally the correctness of MQTT-CV protocol (i.e., the three components of the proposed protocol are correctly interacting), through the use of Promela language and its system verification tool; the model checker SPIN. Secondly, using real-world data provided by our car manufacturer partner, we have conducted real implementation and experiments. The obtained results show the effectiveness of our approach in term of data workload reduction at the processing infrastructure. The mean improvement, besides the fact that it is dependent of the target application, was in general about 10 times less in comparison to native MQTT protocol. [ABSTRACT FROM AUTHOR]

An essential approach for guaranteeing the safety of a cyber-physical system is to monitor its execution in real time. The execution trace of such a system typically consists of one or more signals, and a key computational task for safety monitoring is the online processing of these signals in order to identify events that need to be acted upon in a timely manner. There are several existing proposals for the specification of signal monitors: temporal logics, reactive languages, and dataflow formalisms. A shared feature of most of these proposals is that they describe online signal transformations that are causal. The causality requirement enables a real-time implementation, where the input and output signals are perfectly synchronized. We propose a new specification formalism for signal monitors that relaxes the causality restriction and allows the output to depend on a bounded amount of future input. It follows that an online implementation of such a monitor must have a certain amount of lag in the computation. We introduce a formal framework for signal transformations that allow bounded lag (the output has fallen behind the input) and bounded lead (the output is running ahead of the input), and we propose a type discipline for classifying these transformations according to their lead/lag. We show that this typed framework provides a modular approach for succinctly specifying: 1) monitors for temporal properties that involve both past and bounded-future connectives and 2) complex signal processing computations, such as those arising in the monitoring of physiological signals in medical devices. We have implemented the proposed specification formalism and we have compared it against state-of-the-art tools for the online monitoring of temporal properties: MonPoly, StreamLAB, Aerial, and Reelay. [ABSTRACT FROM AUTHOR]

A robotic mobile fulfillment system (RMFS) performs the order fulfillment process by bringing inventory to workers at pick-pack-and-ship warehouses. In the RMFS, robots lift and carry shelving units, called inventory pods, from storage locations to picking stations where workers pick items off the pods and put them into shipping cartons. The robots then return the pods to the storage area and transport other pods. In this article, we consider an item assignment problem in the RMFS in order to maximize the sum of similarity values of items in each pod. We especially focus on a reoptimization heuristic to address the situation where the similarity values are altered so that a good assignment solution can be obtained quickly with the changed similarity values. A constructive heuristic algorithm for the item assignment problem is developed, and then, a reoptimization heuristic is proposed based on the constructive heuristic algorithm. Then, computational results for several instances of the problem with 10–500 items are presented. We further analyze the case for which an item type can be placed into two pods. Note to Practitioners—This article proposes an efficient heuristic algorithm for assigning items to pods in a robotic mobile fulfillment system (RMFS) so that items ordered together frequently are put into the same pod. Computational results with 10–500 items show that the gaps from upper bounds are very small on average. For cases where the similarity values between items change or their estimation is not accurate due to the fluctuations in demand, a reoptimization heuristic algorithm that alters the original assignment is developed. The experimental results show that the reoptimization algorithm is robust when perturbation levels are approximately 40%–50% of the original similarity values with much less computation times. We believe that this research work can be very helpful for operating the RMFS efficiently. [ABSTRACT FROM AUTHOR]

Existing Adaptive Bitrate (ABR) algorithms pick future video chunks’ bitrates via fixed rules or offline trained models to ensure good quality of experience (QoE) for Internet video. Nevertheless, data analysis demonstrates that a good ABR algorithm is required to continually and fast update for adapting itself to time-varying network conditions. Therefore, we propose Comyco, a video quality-aware learning-based ABR approach that enormously improves recent schemes by i) picking the chunk with higher perceptual video qualities rather than video bitrates; ii) training the policy via imitating expert trajectories given by the expert strategy; iii) employing the lifelong learning method to continually train the model w.r.t the fresh trace collected by the users. To achieve this, we develop a complete quality-aware lifelong imitation learning-based ABR system, construct quality-based neural network architecture, collect a quality-driven video dataset, and estimate QoE metrics with video quality features. Using trace-driven and real-world experiments, we demonstrate Comyco reaches 1700-fold improvements in the number of samples required and 16-fold speedup in the training time compared with the prior work. Meanwhile, Comyco outperforms existing methods, with the improvements on average QoE of 7.5%–16.79%. Moreover, experimental results on continual training also illustrate that lifelong learning helps Comyco further improve the average QoE of 1.07%–9.81% in comparison to the offline trained model. [ABSTRACT FROM AUTHOR]

Hardware/software co-designs are usually defined at high levels of abstractions at the beginning of the design process in order to provide a variety of options on how to realize a system. This allows for design exploration which relies on knowing the costs of different design configurations (with respect to hardware usage and firmware metrics). To this end, methods for cost estimation are frequently applied in industrial practice. However, currently used methods oversimplify the problem and ignore important features, leading to estimates which are far off from real values. In this article, we address this problem for memory systems. To this end, we borrow and re-adapt solutions based on Machine Learning (ML) which have been found suitable for problems from the domain of Computer Vision (CV). Based on that, an approach is proposed which outperforms existing methods for cost estimation. Experimental evaluations within an industrial context show that, while the accuracy of the state-of-the-art approach is frequently off by more than 20 percent for area estimation and more than 15 percent for firmware estimation, the method proposed in this article comes rather close to the actual values (just 5–7 percent off for both area and firmware). Furthermore, our approach outperforms existing methods for scalability, generalization, and decrease in manual effort. [ABSTRACT FROM AUTHOR]

In this paper, we describe a video coding design that enables a higher coding efficiency than the HEVC standard. The proposed video codec follows the design of block-based hybrid video coding, but includes a number of advanced coding tools. A part of the incorporated advanced concepts was developed by the Joint Video Exploration Team, while others are newly proposed. The key aspects of these newly proposed tools are the following. A video frame is subdivided into rectangles of variable size using a binary partitioning with variable split ratios. Three new approaches for generating spatial intra prediction signals are supported: A line-wise application of conventional intra prediction modes, coupled with a mode-dependent processing order, a region-based template matching prediction method and intra prediction modes based on neural networks. For motion-compensated prediction, a multi-hypothesis mode with more than two motion hypotheses can be used. In transform coding, mode dependent combinations of primary and secondary transforms are applied. Moreover, scalar quantization is replaced by trellis-coded quantization and the entropy coding of the quantized transform coefficients is improved. The intra and inter prediction signals can be filtered using an edge-preserving diffusion filter or a non-linear DCT-based thresholding operation. The video codec includes an adaptive in-loop filter for which one of three classifiers can be chosen on a picture basis. We also incorporated an optional encoder control, which adjusts the quantization parameters based on a perceptually motivated distortion measure. In a random access scenario, our proposed video codec achieves luma BD-rate savings between 32.5% for HDR HLG UHD and 39.6% for SDR UHD over the HEVC (HM software) anchor for different categories of test sequences. [ABSTRACT FROM AUTHOR]

First break picking is an inevitable process in land seismic data processing, which involves a huge amount of human labor to perform. Even after decades of investigation on the first break picking process, there are still enormous challenges in developing a robust automatic approach. Although many experts proposed techniques to solve the first break picking problems automatically, there are no solid solutions to avoid human labors during the picking process. In the late 20th century, the rise of the artificial intelligence and the advancement of computer hardware have overcome some challenges in first break picking but the level of their success is limited. In this article, we proposed a deep machine learning model to achieve automatic seismic first break picking. Our proposed model can find the underlying factors and determine the first break curve. In addition, the network is capable of updating itself through continuous learning. The system is able to identify labeling anomalies on-site and update the model through active learning. Unfortunately, training the machine learning model on a huge data set that contains unnecessary data points is an inefficient way for both model learning process and human labeling labors. Therefore, training the model with data selected by the experts can highly reduce the training time and the number of data that human has to label. In simulation, we show the advantage of our proposed deep semisupervised neural network, which uses both labeled and unlabeled data sets to achieve higher accuracy compared with the supervised neural networks. [ABSTRACT FROM AUTHOR]

This paper introduces a self-interference cancellation (SIC) technique for data over cable service interface specification (DOCSIS) 3.1 uplink system with full duplex. At DOCSIS 3.1 uplink environment with full duplex, cable modems (CMs) transmit their request information (upstream) to CM termination system (CMTS), and CMTS transmits its broadcasting data (downstream) through the same frequency band at the same time. Therefore, the upstream becomes a signal of interest (SoI), and consequently the high-power feedback downstream causes self-interference (SI) signal against SoI. However, under the practical DOCSIS 3.1 uplink environment, achieving time and frequency synchronization between upstream and downstream is a challenging problem and additional signal processing technique should be applied. Therefore, in order to improve the processing efficiency and to reflect practical communication environment, the time-domain SIC technique, which does not require any synchronization between upstream and downstream is proposed. Furthermore, since the proposed scheme can consider various practical signal processing aspects, such as multi-path fading channel estimation and nonlinear element estimation of SI signal, it can be efficiently applied for the practical DOCSIS 3.1 uplink system. Test results indicate that the uplink system with 1024QAM (mandatory maximum constellation) can be operated in the case that there is SI estimation interval with two OFDM symbols. [ABSTRACT FROM AUTHOR]

We present a realtime virtual grasping algorithm to model interactions with virtual objects. Our approach is designed for multi-fingered hands and makes no assumptions about the motion of the user's hand or the virtual objects. Given a model of the virtual hand, we use machine learning and particle swarm optimization to automatically pre-compute stable grasp configurations for that object. The learning pre-computation step is accelerated using GPU parallelization. At runtime, we rely on the pre-computed stable grasp configurations, and dynamics/non-penetration constraints along with motion planning techniques to compute plausible looking grasps. In practice, our realtime algorithm can perform virtual grasping operations in less than 20ms for complex virtual objects, including high genus objects with holes. We have integrated our grasping algorithm with Oculus Rift HMD and Leap Motion controller and evaluated its performance for different tasks corresponding to grabbing virtual objects and placing them at arbitrary locations. Our user evaluation suggests that our virtual grasping algorithm can increase the user's realism and participation in these tasks and offers considerable benefits over prior interaction algorithms, such as pinch grasping and raycast picking. [ABSTRACT FROM AUTHOR]

Factory automation is the next industrial revolution. 5G and IIoT are enabling smart factories to seamlessly create a network of wirelessly connected machines and people that can instantaneously collect, analyze, and distribute real-time data. A 5G-enabled communication network for IIOT will boost overall efficiency, launching a new era of market opportunities and economic growth. This article presents the 5G-enabled system architecture and ultra-reliable use cases in smart factories associated with automated warehouses. In particular, for URLLC-based cases, key techniques and their corresponding solutions, including diversity for high reliability, short packets for low latency, and on-the-fly channel estimation and decoding for fast receiver processing, are discussed. Then the channel modeling requirements concerning technologies and systems are also identified in industrial scenarios. Ray tracing channel simulation can meet such requirements well, and based on that, the channel characteristic analysis is presented at 28 and 60 GHz for licensed and unlicensed band frequencies to exploit the available degrees of freedom in the channels. [ABSTRACT FROM AUTHOR]

The visualization toolkit (VTK) is a popular cross-platform, open source toolkit for scientific and medical data visualization, processing, and analysis. It supports a wide variety of data formats, algorithms, and rendering techniques for both polygonal and volumetric data. In particular, VTK's volume rendering module has long provided a comprehensive set of features such as plane clipping, color and opacity transfer functions, lighting, and other controls needed for visualization. However, due to VTK's legacy OpenGL backend and its reliance on a deprecated API, the system did not take advantage of the latest improvements in graphics hardware or the flexibility of a programmable pipeline. Additionally, this dependence on an antiquated pipeline posed restrictions when running on emerging computing platforms, thereby limiting its overall applicability. In response to these shortcomings, the VTK community developed a new and improved volume rendering module, which not only provides a modern graphics processing unit-based implementation, but also augments its capabilities with new features such as fast volume clipping, gradient-magnitude-based opacity modulation, render to texture, and hardware-based volume picking. [ABSTRACT FROM AUTHOR]

For data-intensive applications, energy expended in on-chip computation constitutes only a small fraction of the total energy consumption. The primary contribution comes from transporting data between off-chip memory and on-chip computing elements—a limitation referred to as the Von-Neumann bottleneck. In such a scenario, improving the compute energy through parallel processing or on-chip hardware acceleration brings minor improvements to the total energy requirement of the system. We note that an effective solution to mitigate the Von-Neumann bottleneck is to develop a framework that enables computing in off-chip nonvolatile memory arrays, where the data reside permanently. In this paper, we present a malleable hardware (MAHA) reconfigurable framework that modifies nonvolatile CMOS-compatible flash memory array for on-demand reconfigurable computing. MAHA is a spatio-temporal mixed-granular hardware reconfigurable framework, which utilizes the memory for storage as well as lookup table-based computation (hence malleable) and uses a low-overhead hierarchical interconnect fabric for communication between processing elements. A detailed design of the malleable hardware together with a comprehensive application mapping flow is presented. Design overheads carefully estimated at the 45-nm technology node indicate that for a set of common kernels, MAHA achieves a 91X improvement in energy efficiency over a software-only solution with negligible impact on memory performance in normal mode. The proposed design changes incur only 6% memory area overhead. [ABSTRACT FROM AUTHOR]

A supply chain (SC) is a network of independent manufacturing and logistics companies that perform the critical functions in the order fulfillment process. This paper proposes an effective and modular model to describe material, financial and information flow of SCs at the operational level based on first-order hybrid Petri nets (PNs), i.e., PNs that make use of first-order fluid approximation. The proposed formalism enables the SC designer to choose suitable production rates of facilities in order to optimize the chosen objective function. The optimal mode of operation is performed based on the state knowledge of the obtained linear discrete-time, time-varying state variable model in order to react to unpredictable events such as the blocking of a supply or an accident in a transportation facility. A case study is modeled in the proposed framework and is simulated under three different closed-loop control strategies. [ABSTRACT FROM AUTHOR]

Considers task-based authorization control (TBAC) models in workflow and E-commerce. Discussion on the authorization life cycle; Order processing in TBAC; Visual languages in TBAC; Overview of visual TBAC in E-commerce applications. INSET: Related Work in Visualizing Authorization.

Discusses the establishment of the Post Press System (PPS) at Deluxe Corp. designed to automate the process of producing bank checks in response to surging customer demand. Traditional process; PPS architecture; Design approach; Plant server; Line supervisor; Control subsystems.

The article looks into the perceptions of healthcare professionals on medical software and informatics systems. Significantly, it is viewed that a computerized physician order entry system (CPOE) might actually increase the number of medication errors as compared to a manual, handwritten system. The author highlights the system design of the CPOE system. There are several improvements made in the CPOE system design. Furthermore, contemporary requirements engineering techniques include many of the investigatory techniques employed by physician-researchers who study medical systems. Finally, software engineers have long known that extensive retrofitting causes software to age very rapidly.

The article presents order form for recipients for ordering electronic print of papers published in the journal.