System Design Perspective Research Articles

Knowledge recommendation for workplace learning: a system design and evaluation perspective

PurposeAlthough digitalization in the workplace is burgeoning, tools are needed to facilitate personalized learning in informal learning settings. Existing knowledge recommendation techniques do not account for dynamic and task-oriented user preferences. The purpose of this paper is to propose a new design of a knowledge recommender system (RS) to fill this research gap and provide guidance for practitioners on how to enhance the effectiveness of workplace learning.Design/methodology/approachThis study employs the design science research approach. A novel hybrid knowledge recommendation technique is proposed. An experiment was carried out in a case company to demonstrate the effectiveness of the proposed system design. Quantitative data were collected to investigate the influence of personalized knowledge service on users’ learning attitude.FindingsThe proposed personalized knowledge RS obtained satisfactory user feedback. The results also show that providing personalized knowledge service can positively influence users’ perceived usefulness of learning.Practical implicationsThis research highlights the importance of providing digital support for workplace learners. The proposed new knowledge recommendation technique would be useful for practitioners and developers to harness information technology to facilitate workplace learning and effect organization learning strategies.Originality/valueThis study expands the scope of research on RS and workplace learning. This research also draws scholarly attention to the effective utilization of digital techniques, such as a RS, to support user decision making in the workplace.

Blockchain Technology for Applications in Internet of Things—Mapping From System Design Perspective

Internet of Things (IoT) refers to networks with billions of physical devices for collecting, sharing, and utilizing data in the virtual world. Most of IoT applications centralize security assurance in creating, authenticating, transferring, or delating system components. However, the centralization exposes its limitations to meet security needs of a rapidly growing number of things world-widely. How to scale up the applications with assured security becomes a critical challenge. Blockchain technology (BCT) is a promising solution to provide security and protect privacy in a large scale; especially, smart contracts offer opportunities to improve the reliability of IoT applications. Smart contracts establish trusts for both of data and executed processes. Recently, many literature surveys and positioning articles have been published on the integration of BCT with IoT, but they are limited to superficial discussions of technical potentials, and very few of them have a thorough exploration of the challenges in developing BCT for IoT at technical levels. This paper uses the system design approach to scrutinize the state of the art of study on BCT-based applications and clarify critical research areas of enabling BCT for security assurance: 1) the relations of BCT and IoT are modeled and discussed; 2) the needs of eliminating threats in IoT-based applications are defined as functional requirements (FRs), existing works on enabling technologies of BCT are defined as the physical solutions (PSs); and 3) the mappings between FRs and PSs are established to identify the limitations and the critical areas for the applications of BCT in large-scale distributed environment.

Design considerations for low-margin elastic optical networks in the nonlinear regime [Invited

We demonstrate from a system design perspective that nonlinearity can be exploited to minimize the impact of system margins on system performance for both point-to-point links and elastic optical networks. A nonlinear interaction causes a 2 dB reduction in launch power to be reduced to $ \lt\!{0.25}\,\,{\rm{dB}}$ signal-to-noise ratio (SNR) penalty, and likewise, a 2 dB peak–peak (pk-pk) perturbation to the output power of an optical amplifier is reduced to $ \lt\!{0.25}\,\,{\rm{dB}}$ SNR penalty (for 5, 10, and 20 spans). Extending this to a gain ripple of 1 dB pk-pk with an internode spacing of ${5} \times {80}\,\,{\rm{km}}$, ${10} \times {80}\,\,{\rm{km}}$, and ${20} \times {80}\,\,{\rm{km}}$, the penalty is 0.4 dB, 1.5 dB, and 5.1 dB, respectively, with pre-emphasis reducing this to 0.01 dB, 0.3 dB, and 1.2 dB, respectively. In elastic optical networks, we consider the nonlinear relationship among SNR, margin, and the fraction of capacity available. We consider scaling internode distances of a 9-node German scale network (DT9), such that the initial network diameter increases from 1120 km to 6720 km (six-fold scaling). We generate 1000 different topologies based on the scaled DT9 node locations to quantify the impact of margin. For the unscaled DT9 network, a 3 dB margin results in, on average, a 21% reduction in network throughput; however, when the internode spacing is increased six-fold to a continental scale network, the network throughput is reduced by 40%, on average, for the same 3 dB margin.

Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization

Laser absorption spectroscopy (LAS) is a promising diagnostic method capable of providing high-bandwidth, species-specific sensing, and highly quantitative measurements. This review aims at providing general guidelines from the perspective of LAS sensor system design for realizing quantitative species diagnostics in combustion-related environments. A brief overview of representative detection limits and bandwidths achieved in different measurement scenarios is first provided to understand measurement needs and identify design targets. Different measurement schemes including direct absorption spectroscopy (DAS), wavelength modulation spectroscopy (WMS), and their variations are discussed and compared in terms of advantages and limitations. Based on the analysis of the major sources of noise including electronic, optical, and environmental noises, strategies of noise reduction and design optimization are categorized and compared. This addresses various means of laser control parameter optimization and data processing algorithms such as baseline extraction, in situ laser characterization, and wavelet analysis. There is still a large gap between the current sensor capabilities and the demands of combustion and engine diagnostic research. This calls for a profound understanding of the underlying fundamentals of a LAS sensing system in terms of optics, spectroscopy, and signal processing.

SA-IGA: a multiagent reinforcement learning method towards socially optimal outcomes

In multiagent environments, the capability of learning is important for an agent to behave appropriately in face of unknown opponents and dynamic environment. From the system designer's perspective, it is desirable if the agents can learn to coordinate towards socially optimal outcomes, while also avoiding being exploited by selfish opponents. To this end, we propose a novel gradient ascent based algorithm (SA-IGA) which augments the basic gradient-ascent algorithm by incorporating social awareness into the policy update process. We theoretically analyze the learning dynamics of SA-IGA using dynamical system theory and SA-IGA is shown to have linear dynamics for a wide range of games including symmetric games. The learning dynamics of two representative games (the prisoner's dilemma game and the coordination game) are analyzed in details. Based on the idea of SA-IGA, we further propose a practical multiagent learning algorithm, called SA-PGA, based on Q-learning update rule. Simulation results show that SA-PGA agent can achieve higher social welfare than previous social-optimality oriented Conditional Joint Action Learner (CJAL) and also is robust against individually rational opponents by reaching Nash equilibrium solutions.

Employee’s ubiquitous learning engagement: Impact of innovativeness-oriented learning system design factors and the mediating role of imagery

This study investigates the essential factors involved in employees’ ubiquitous-learning engagement (U-LE). In this study, a research model was proposed based on learning engagement (LE) and narrative transportation theory, focusing on the impact of domain-specific innovativeness (DSI) and interface design (ID) factors on employee U-LE from a learning system design perspective. A questionnaire-type survey was conducted and the responses of 385 employees were analyzed using structural equation modeling with a partial least squares approach. The results imply the following: 1) information-possessing innovativeness (IPI) as the DSI factor influences employees’ U-LE; 2) interface convenience (IC) and interface design aesthetics (IDA), as system implementation factors, significantly affect U-LE; 3) imagery significantly influences U-LE; and 4) imagery is also proven to exert mediating effects on the relationships between U-LE and its antecedents by the bootstrapping method. Finally, both research and practice implications are discussed.

Impact of Polarization Distortions on Geometrical Structure Retrieval of Moving Man-Made Targets in ISAR Images

In this paper, the impact of several polarization distortions on geometrical structure retrieval of man-made targets in inverse synthetic aperture radar (ISAR) images is assessed. For polarimetric ISAR, the polarization distortions are usually composed of crosstalk and channel imbalance. For non-stationary targets, a residual phase error exists between two columns of the scattering matrix in the alternate transmission and simultaneous reception (ATSR) mode, especially when the radar frequency is high. These two kinds of distortions have varying influences on different targets, as well as different decomposition methods. Some theoretical derivations and numerical analyses are given to reveal the impact of these distortions on Cameron decomposition. To evaluate the impact of these distortions on geometrical structure retrieval, we use the numerical results and real data of an unmanned aerial vehicle (UAV) called Frontier to perform error analysis of both the decomposition and the retrieved size of the designated structures of the UAV. Some numerical evaluations were conducted from the perspective of system design, and these results can be useful for the practical radar system.

A Software Framework for Heterogeneous Wireless Sensor Network Towards Environmental Monitoring

A wireless sensor network (WSN) is typically composed of spatially distributed miniature sensors that help collect large amounts of real-time environmental data from buildings. These environmental data (e.g., temperature, humidity, CO2 concentration) can assist a series of heating, ventilation, and air conditioning (HVAC) equipment to increase the building energy efficiency. From a system design perspective, heterogeneous wireless sensor networks need to address two challenges. First, sensor data acquisition, conversion, fusion, and packaging involve a series of software processing. Since each type of environmental sensor typically has unique processing requirements, it is difficult to develop an efficient software framework to combine the processing of multiple heterogeneous sensors. Second, during normal operation of a heterogeneous wireless sensor network, if users insert or remove some environmental sensors, the entire WSN system should operate normally. In this work, in order to solve the above two system design challenges, we have developed a low-power, low-cost, small form-factor WSN development platform, and its software framework can perform efficient data acquisition, conversion, fusion, and packaging for multiple heterogeneous sensors. Our proposed software framework enables easy and rapid WSN system deployment without affecting the overall functionality of each node. The proposed design differs from existing WSN platforms in that it emphasizes advanced high-level usability and reduces time to market without sacrificing low-level features. The proposed WSN system has been implemented and tested in an office building for indoor fire hazard detection. The experimental results show that our software framework can successfully complete data acquisition, conversion, fusion, and packaging tasks for three heterogeneous environmental sensors. In addition, we have verified that our software framework supports robust system operation when inserting or removing sensors from an existing heterogeneous WSN system.

Design and Delivery Features That May Improve the Use of Internet-Based Cognitive Behavioral Therapy for Children and Adolescents With Anxiety: A Realist Literature Synthesis With a Persuasive Systems Design Perspective.

BackgroundInternet-based cognitive behavioral therapy (iCBT) is a persuasive system as its design combines therapeutic content, technological features, and interactions between the user and the program to reduce anxiety for children and adolescents. How iCBT is designed and delivered differs across programs. Although iCBT is considered an effective approach for treating child and adolescent anxiety, rates of program use (eg, module completion) are highly variable for reasons that are not clear. As the extent to which users complete a program can impact anxiety outcomes, understanding what iCBT design and delivery features improve program use is critical for optimizing treatment effects.ObjectiveThe objectives of this study were to use a realist synthesis approach to explore the design and delivery features of iCBT for children and adolescents with anxiety as described in the literature and to examine their relationship to program use outcomes.MethodsA search of published and gray literature was conducted up to November 2017. Prespecified inclusion criteria identified research studies, study protocols, and program websites on iCBT for child and adolescent anxiety. Literature was critically appraised for relevance and methodological rigor. The persuasive systems design (PSD) model, a comprehensive framework for designing and evaluating persuasive systems, was used to guide data extraction. iCBT program features were grouped under 4 PSD categories—Primary task support, Dialogue support, System credibility support, and Social support. iCBT design (PSD Mechanisms) and delivery features (Context of use) were linked to program use (Outcomes) using meta-ethnographic methods; these relationships were described as Context-Mechanism-Outcome configurations. For our configurations, we identified key PSD features and delivery contexts that generated moderate-to-high program use based on moderate-to-high quality evidence found across multiple iCBT programs.ResultsA total of 44 documents detailing 10 iCBT programs were included. Seven iCBT programs had at least one document that scored high for relevance; most studies were of moderate-to-high methodological rigor. We developed 5 configurations that highlighted 8 PSD features (Tailoring, Personalization [Primary task supports]; Rewards, Reminders, Social role [Dialogue supports]; and Trustworthiness, Expertise, Authority [System credibility supports]) associated with moderate-to-high program use. Important features of delivery Context were adjunct support (a face-to-face, Web- or email-based communications component) and whether programs targeted the prevention or treatment of anxiety. Incorporating multiple PSD features may have additive or synergistic effects on program use.ConclusionsThe Context-Mechanism-Outcome configurations we developed suggest that, when delivered with adjunct support, certain PSD features contribute to moderate-to-high use of iCBT prevention and treatment programs for children and adolescents with anxiety. Standardization of the definition and measurement of program use, formal testing of individual and combined PSD features, and use of real-world design and testing methods are important next steps to improving how we develop and deliver increasingly useful treatments to target users.

Drip Irrigation in Rice

Photo courtesy of Theivasigamani Parthasarathi. The majority of rice production involves growing the crop in flooded fields, but this method is not necessary for cultivation. While flooding fields is an effective way to reduce weeds and other pests, using fresh water for rice cultivation diverts this limited resource away from other uses. Given the estimate that rice cultivation under flooded conditions uses 30 to 45% of the world's fresh water,1 finding ways to reduce water use to grow rice could have a big impact on access to this resource. Some researchers are evaluating the costs and benefits to growing rice under aerobic conditions where rice is grown in unsaturated soil. Aerobic cultivation relies upon precipitation, and irrigation where needed, and uses less water to grow the crop. Water loss can be reduced even more with the use of drip irrigation systems. Drip irrigation enables the precision application of water and fertilizer and limits evaporation. Using drip irrigation in rice is a relatively new management approach, and the best management practices for these systems are not well understood. In a recent article in Agronomy Journal (https://doi.org/10.2134/agronj2018.01.0002), researchers interested in reducing water use for rice production evaluated conventional aerobic cultivation and drip irrigation systems. The field trials were conducted in 2012 and 2013 at Tamil Nadu Agricultural University, Coimbatore, India. Theivasigamani Parthasarathi, a member of ASA, conducted this research as part of his doctoral degree. The experiment compared conventional aerobic cultivation to a series of drip irrigation treatments. The drip treatments included combinations of three lateral distances (0.6, 0.8, and 1.0 m), two dripper discharge rates (0.6, 1.0 liter per hour), and two irrigation methods (surface and 15-cm subsurface). Rice yield, plant growth, root length, and physiological and leaf gas exchange factors were measured. In general, the use of drip irrigation resulted in 50% water savings and a 29% increase in rice yield compared with aerobic cultivation. Of the 12 drip irrigation treatments, the subsurface drip laid out at a 0.8-m lateral distance with a 1.0 L hsup–1 dripper discharge irrigation system had the best performance in terms of growth, physiology, and rice yield. Drip irrigation appears to increase the length and density of roots, which subsequently increase aboveground growth. Parthasarathi notes there is a two-fold increase in water productivity of aerobic rice under drip irrigation. “This concept could be widely propagated wherever rice is being cultivated under well irrigation sources or where groundwater categories are classified as overexploited and exploited,” he says. “Drip-responsive rice genotypes could be developed, and this is one of the future lines of research in rice variety development.” This could lead to even greater gains in rice yields and further incentivize farmers to change their cultivation system. Demonstrating the potential for high performance using drip irrigation is encouraging for growers considering in investing in new technology and methods to reduce water use. While there are upfront costs to install drip irrigation systems, the technology is compatible with other crops. “The best lateral distance of 0.8 m is suitable to most vegetables and rice crops as well as more economical in system design and cost perspective,” Parthasarathi says. Therefore, growers can not only improve their rice cultivation system, but can utilize the fields for other crops, keeping them in production rotation.

Comparison of adaptive and compressed sensing beamformers

We will review the capabilities and requirements of the Minimum Variance Distortionless Response (MVDR) and sparsity-seeking beamformers, from a system design perspective. We will focus on the problem of detecting a quiet source in the presence of loud interferers. These methods are solutions to particular and different optimization problems, requiring particular combinations of inputs. Thus, for example, MVDR does not require us to model the interference, whereas the sparsity-seeking methods need both interferer and quiet source models to be put into their dictionary. MVDR is notorious for requiring more precise calibration than is needed for conventional beamformers. We will present demonstrations on simulated and experiment data, illustrating key differences between the two methods.

First-Order Algorithm for Content-Centric Sparse Multicast Beamforming in Large-Scale C-RAN

In multimedia-rich communication scenarios, popular contents are requested by many users. This calls for the communication system design perspective transferring from user-centric to content-centric. To realize the content-centric paradigm, one of the dominant approaches is the multi-group multicast transmission. However, different content groups may cause interference with each other, and the quality of service is difficult to be guaranteed without coordination. Fortunately, a cloud radio access network (C-RAN) perfectly fills this gap as all the computations in the network are off-loaded to the computation center, making the central coordination possible. But a major challenge that C-RAN faces is that the resultant problem size could be extremely large, invalidating many existing second-order algorithms. In this paper, content-centric sparse multicast beamforming in a large-scale C-RAN is studied. In addition to the large-scale nature, this problem is further complicated by the discontinuity and non-convexity of the cost function and constraints. Despite the challenges, a first-order algorithm is proposed. Not only is the proposed algorithm guaranteed to converge to a critical point, but its complexity order is only linear with respect to the problem size. This is in sharp contrast to the cubic order of an existing solution, making the proposed algorithm indispensable for large-scale C-RAN with hundreds or thousands of users.

A numerical fire simulation approach for effectiveness analysis of fire safety measures in floating liquefied natural gas facilities

Fire remains a serious threat to a floating liquefied natural gas facility. It is of greater concern given the remote locations and limited accessibility of emergency services. This study aims to present a rigorous procedure to study potential accident scenarios in an offshore (floating processing) facility with different ignition source locations and verify the effectiveness of safety measures using computational fluid dynamics code. The uniqueness of the present study is the integration of release, dispersion and fire modeling scenarios, simplifying the fire analysis and increasing its effectiveness from the offshore process system design and analysis perspectives. The first step of the procedure is to identify the range of potential release scenarios and their strength of dispersion in confined and semi-confined spaces. Subsequently, potential fire scenarios are analyzed considering the influence of the location. Computational fluid dynamics models are used to analyze these three steps of the scenarios. Application of the procedure is demonstrated on an offshore facility by analyzing 14 credible scenarios. The ranges of safety measures of these fires are also studied to determine their effectiveness to prevent fires and mitigate their impact. This study provides a simple and efficient way to analyze the impact of key design parameters. In this study, the transition from fire to explosion is not considered and all the environmental factors are assumed to be constants in the simulation.

Functional modelling, validation and testing of analog to digital converters (ADC) from system design perspective

In this paper a methodology to develop PSpice (SPICE) model for flash type analog to digital converters and its validation is presented. With the onset of the Internet of Things and ongoing digitization of electronic devices around us, almost every device needs an interface or controlling unit as a digital computing device. As more and more products perform calculations in the digital or discrete time domain, more sophisticated data converters must translate the digital data to and from our inherent analog world. This varied nature of interfaces necessitates simulation of ADC along with a functional simulation of the system to optimize the performance of design and improve reliability. The paper focuses on the development of a functional model for ADC which can expedite system development. The design philosophy was at the block level and the development was conducted for commercially available ADC, AD7828. The complete model has been simulated using PSpice and results were compared with laboratory test measurements using AD7828.

Technologies for Efficient Amateur Drone Detection in 5G Millimeter-Wave Cellular Infrastructure

Unmanned aerial vehicles, also called drones, are recently gaining increased research attention across various fields due to their flexibility and application potential. The steady increase in the number of amateur drones demands more stringent regulations on their allowed route, mass, and load. However, these regulations may be violated accidentally or deliberately. In these cases, spying with drones, transfer of dangerous payloads, or losing reliable drone control can represent a new hazard for people, governments, and business sector. The technologies to detect, track, and disarm possible aerial threats are therefore in prompt demand. To this end, ubiquitous cellular networks, and especially 5G infrastructures based on the use of millimeter-wave radio modules, may be efficiently leveraged to offer the much needed drone detection capabilities. In this work, we propose to exploit 5G millimeter-wave deployments to detect violating amateur drones. We argue that the prospective 5G infrastructure may provide all the necessary technology elements to support efficient detection of small-sized drones. We therefore outline a novel technology and system design perspective, including such considerations as the density of base stations, their directional antennas, and the available bandwidth, among others, as well as characterize their impact with our ray-based modeling methods.

Designer bacteria as intratumoural enzyme biofactories.

Bacterial-directed enzyme prodrug therapy (BDEPT) is an emerging form of treatment for cancer. It is a biphasic variant of gene therapy in which a bacterium, armed with an enzyme that can convert an inert prodrug into a cytotoxic compound, induces tumour cell death following tumour-specific prodrug activation. BDEPT combines the innate ability of bacteria to selectively proliferate in tumours, with the capacity of prodrugs to undergo contained, compartmentalised conversion into active metabolites in vivo. Although BDEPT has undergone clinical testing, it has received limited clinical exposure, and has yet to achieve regulatory approval. In this article, we review BDEPT from the system designer's perspective, and provide detailed commentary on how the designer should strategize its development de novo. We report on contemporary advancements in this field which aim to enhance BDEPT in terms of safety and efficacy. Finally, we discuss clinical and regulatory barriers facing BDEPT, and propose promising approaches through which these hurdles may best be tackled.

Interactive Demonstration of an AC-130 Aircraft Virtual Reality Part Task Trainer

This session will demonstrate a Virtual Reality Part Task Trainer (vrPTT) that was developed under a Department of Defense funded research and development program. The vrPTT consists of Virtual Reality (VR) head mounted display (HMD) goggles and bare-hand motion sensor technology integrated via a high speed gaming computer that hosts an automated, intelligent tutor. Upon donning the VR goggles, the user is immersed into a cockpit of an AC-130 aircraft. The AC-130 system instruments are operationally accurate with selection and feedback indications true to aircraft operational parameters. The demonstration will allow participants to manipulate buttons and switches on a simulated checklist completion task within the immersive vrPTT environment. The Session attendees will gain an understanding of VR’s potential for DoD aircrew training from a human factors psychology and instructional systems design perspective by interacting with a developed capability.

High Performance Silicon Carbide Power Block for Industry Applications

Silicone carbide (SiC) power devices have been optimized in performance over the past decade. However, wide industry adoption of SiC technology still faces challenges from system design perspective. This paper demonstrates an integrated air-cooled three-phase SiC power block for industry applications. The key design aspects, such as high performance gate driver, low parasitic layout, optimized thermal management, as well as flexible control platform are addressed. Experimental results are provided to demonstrate the superior performance of the design.

Analysis of intra-pulse frequency-modulated, low probability of interception, radar signals

In this paper, we investigate the problem of analysis of low probability of interception (LPI) radar signals with intra-pulse frequency modulation (FM) under low signal-to-noise ratio conditions from the perspective of an airborne electronic warfare (EW) digital receiver. EW receivers are designed to intercept and analyse threat radar signals of different classes, received over large dynamic range and operating independently over large geographical spread to advice host aircraft to undertake specified actions. For an EW receiver, primary challenges in interception and analysis of LPI radar signals are low received power, intra-pulse modulations, multi-octave frequency range, wide signal bandwidth, long pulse width, vast and multi-parametric search space, etc. In the present work, a method based on match filterbank localization and Taylor’s series approximation for analysing the entire family of intra-pulse FM radar signals is proposed. The method involves progressive, joint time–frequency (TF) localization of the signal of interest (SOI), under piecewise linearity and continuity assumptions on instantaneous frequency, to effectively capture local TF signatures. Detection is by information-theoretic criterion based hypotheses testing, while estimation and classification are based on polynomial approximation. Fine signal analysis is followed by synthetic reconstruction of the received signal slope. Detection, estimation and classification performances for the prominent FM radar signal classes are quantified based on simulation study statistics. Stagewise implementation of analysis and FM slope reconstruction, in realistic radar threat scenarios, is demonstrated for the potential SOIs. Subject discussion is organized from the perspective of practical EW system design and presented within the realm of signal processing architecture of concurrent EW digital receivers.

On Market-Driven Hybrid-P2P Video Streaming

Consistent (pause-free) quality of service is required in peer-to-peer (P2P) video streaming systems. In this paper, we aim to eliminate the problem of playback pauses in such systems via the use of positive incentives for peers to contribute high upload rates. We model our problem as a market, where the market stakeholders consist of multiple content providers, advertisement providers , and network peers ; the positive incentives for peers in the market are reduced advertisement (ad) viewing durations. From a system design perspective, one of our primary goals is to compute the market equilibria that include appropriate ad viewing durations, offering sufficient incentives for network peers to continue contributing. Our simulation-based studies demonstrate that we mitigate the “playback pause” problem for peers by up to 80% as compared to existing approaches, generate sufficient utility for advertisers to be part of the market, and enable content providers to achieve their desired utility by providing sufficient incentives for all peers to stay in the system without violating ad provider agreements.