Configuration Table Research Articles

Approximate single precision floating point adder for low power applications

With an increasing demand for power-hungry data-intensive computing, design methodologies with low power consumption are increasingly gaining prominence in the industry. Most of the systems operate on critical and noncritical data both. An attempt to generate a precision result results in excessive power consumption and results in a slower system. For noncritical data, approximate computing circuits significantly reduce the circuit complexity and hence power consumption. In this paper, a novel approximate single precision floating point adder is proposed with an approximate mantissa adder. The mantissa adder is designed with three 8-bit full adder blocks. In this paper, a detailed mathematical background, and proposed design approach in terms of the circuit configuration and truth tables are discussed. Additionally, a concept of switching between exact computing and approximate computing is analysed considering an approximate carry look-ahead adder. The delay and power consumption for the exact operating mode and approximate operation mode considering varied window sizes is observed. Performance of the approximate computation is compared against exact computation and varied approximate computing approaches.

Multiple graphical views for automatically generating SQL for the MycoDiversity DB; making fungal biodiversity studies accessible.

Fungi is a highly diverse group of eukaryotic organisms that live under an extremely wide range of environmental conditions. Nowadays, there is a fundamental focus on observing how biodiversity varies on different spatial scales, in addition to understanding the environmental factors which drive fungal biodiversity. Metabarcoding is a high-throughput DNA sequencing technology that has positively contributed to observing fungal communities in environments. While the DNA sequencing data generated from metabarcoding studies are available in public archives, this valuable data resource is not directly usable for fungal biodiversity investigation. Additionally, due to its fragmented storage and distributed nature, it is not immediately accessible through a single user interface. We developed the MycoDiversity DataBase User Interface (https://mycodiversity.liacs.nl) to provide direct access and retrieval of fungal data that was previously inaccessible in the public domain. The user interface provides multiple graphical views of the data components used to reveal fungal biodiversity. These components include reliable geo-location terms, the reference taxonomic scientific names associated with fungal species and the standard features describing the environment where they occur. Direct observation of the public DNA sequencing data in association with fungi is accessible through SQL search queries created by interactively manipulating topological maps and dynamic hierarchical tree views. The search results are presented in configurable data table views that can be downloaded for further use. With the MycoDiversity DataBase User Interface, we make fungal biodiversity data accessible, assisting researchers and other stakeholders in using metabarcoding studies for assessing fungal biodiversity.

Multi-Table Programmable Parser with online flow-level update consistency for satellite networks

Programmable packet parsers in the space data plane, responsible for identifying protocols and extracting keywords on demand, are significant to realize protocol upgrading for satellite networks. Because flow-level update consistency is significant, onboard resources are limited, and space radiation may cause errors in packet parsers, programmable packet parsers with online flow-level update consistency, low complexity, and high reliability are urgently needed. In our previous work, a Multi-Table Programmable Parser (MTPP) [1] is designed for fast configuration and low storage cost by networking configuration and multiple tables. In this paper, we propose a Multi-Table Programmable Parser with Online Flow-Level Update Consistency (MTPP-OFLUC), consisting of five tables and eight models. To guarantee the flow-level update consistency, each table consists of a main table and a backup table. Moreover, to improve the reliability of MTPP-OFLUC, a fault tolerance mechanism is designed. Furthermore, an Online Configuration based on Super Parse Graph Segmentation (OC-SPGS) is proposed to achieve fast configuration with flow-level update consistency. The numerical analysis under typical parse graphs demonstrates that MTPP-OFLUC uses less storage than that of PISA-based parser. Due to backup tables adapted to guarantee the flow-level update consistency, the storage cost of MTPP-OFLUC is only 11.53% more than that of MTPP. Through OC-SPGS, the parsing success ratio of MTPP-OFLUC is up to 66.7% larger than that of MTPP. Finally, a prototype of MTPP-OFLUC is implemented on Xilinx Virtex-7 FPGA. The response time of MTPP-OFLUC and the network is 1.460 us and 4.196 ms, respectively. The size of configuration information is less than that of PISA-based parser. In summary, rapid response and flow-level update consistency are realized by MTPP-OFLUC. Moreover, security risks, user-friendly interfaces, potential capacity of upgrades, and supports for higher data rates still need to be considered in the future works.

Edge–IoT Computing and Networking Resource Allocation for Decomposable Deep Learning Inference

Deep learning (DL) applications have attracted significant attention with the rapidly growing demand for Internet of Things (IoT) systems. However, performing the inference tasks for DL applications on IoT devices is challenging due to the large computational demands of DL models. Recently, edge computing has offered us a solution by deploying resources near the end users. However, resources at the edge are still limited; thus, management issues, such as allocating the networking resources as well as the computing capabilities and configuring the devices appropriately for different applications, become essential. For knobs in such edge management, we consider multiple application tasks with different options of DL models and different hyperparameter settings, along with possible decomposition points that utilize the split DL concept to design the configuration tables. Layer-level decomposition in split DL provides greater flexibility by splitting a single DL inference model into parts on different computing devices, and each part consists of several consecutive layers. We then propose the SplitDL-Image and the SplitDL-Video algorithms based on the Vickrey–Clarke–Groves (VCG) mechanism by considering model performance and frames per second (FPS) requirements with the preferences of the heterogeneous IoT devices. The proposed method allocates networking and edge server computing resources according to the designed configuration tables by assigning the appropriate configuration to each IoT device. Simulation results based on real-world applications show that the proposed method indeed allocates more resources to IoT devices with more urgent/important tasks, preference for better accuracy, or higher local computational cost. In addition, other desired properties, such as truthful bidding, individual rationality, and weakly budget balance, are also guaranteed.

Innovation of Artificial Intelligence and Digital Media in Environmental Art Design

With the current upsurge of innovation and entrepreneurship in China, this paper objectively analyzes the importance of designers’ professional quality and scientific awareness in response to the need for the integration and development of innovative skills and artistic spirit in environmental design. This paper emphasizes that the level of intelligent operation of science and technology should be improved in design, the rigor of intelligent utilization in creative expression should be improved, and the problem of evaluation criteria of artistic design value should be solved based on professional development. This paper gives the design scheme of intelligent monitoring module based on Lora technology and designs the hardware circuit of the intelligent monitoring module with MSP430F2272 as the core. It includes a microcontroller circuit, a Lora module interface circuit, an input signal detection module, a control output module, and the like. This paper focuses on the content, representation, and implementation of the associated configuration table, and compares four analog input signals with variable thresholds. The sensor detects 800Lux, and the error is 500Lux, which is higher than errmax, so it outputs -10 brightening every time to generate analog input logic. The 4 digital inputs are combined with the analog input logic to generate output control signals by associating operations.

Principles оf Storing and Monitoring Configuration Information in the Task of On-Board Equipment Complex Redundancy Managing

The article proposes the principles of the main structural components formation for the implementation of the task of managing the on-board equipment complexs (OEC) redundant resources: configurations tables, readiness indices and functional efficiency indicators of the software and hardware equipment components, allowing to formalize the processes of their development and use in the task of managing the excess on-board equipment complex resources. The separation of redundant components into groups of resources is proposed: computing modules, operating system core components, onboard software components, peripheral switching system components, hardware peripheral components. The general organization of redundant resources monitoring is formulated, which allows using the schemes and capabilities of both traditional built-in controls (the lower level of monitoring) and more advanced algorithmic solutions based on the logical processing of control results (the middle and upper levels of moni- toring). The formation mechanisms, as well as the forms of configuration tables for hardware components and on-board applications, as well as the rules for filling them, focused on the use of configuration supervisors, are proposed. The principles of readiness indices and functional efficiency indicators forming have been developed and detailed, allowing to implement in the software environment the accounting of various factors that determine the capabilities and effectiveness of various computing tools and OEC configurations. A method for correcting the configurations functional efficiency indicator due to the mode generator, which adapts the complex reconfiguration to the conditions of its use, the tasks to be solved, and the operators commands, is proposed. An example of considering the variety of tasks and modes of the OEC of an aircraft: flight stages, emergency situations, services and support, life support modes, the operation of the flight and navigation complex, as well as the crew control commands, is given. The proposed solutions can be used in computer-aided design systems for equipment complexes, flight safety systems, control of general aircraft equipment, software-controlled radio communication equipment systems, multispectral onboard reco naissance systems, target designation and control of aviation weapons and special target loads of promising aviation complexes.

The design and implementation of GECAM satellite payload performance monitoring software

Background The Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is primarily designed to spot gamma-ray bursts corresponding to gravitational waves. In order to achieve stable observations from various astronomical phenomena, the payload performance need to be monitored during the in-orbit operation. Method This article describes the design and implementation of GECAM satellite payload performance monitoring (GPPM) software. The software extracts the payload status and telescope observations (light curves, energy spectrums, characteristic peak fitting of energy spectrums, etc) from the payload data. Considering the large amount of payload status parameters in the engineering data, we have designed a method of parameter processing based on the configuration tables. This method can deal with the frequent changes of the data formats and facilitate program maintenance. Payload status and performance are monitored through defined thresholds and monitoring reports. The entire software is implemented in python language and the huge amount of observation data is stored in MongoDB. Conclusion The design and implementation of GPPM software have been completed, tested with ground and in-orbit payload data. The software can monitor the performance of GECAM payload effectively. The overall design of the software and the data processing method can be applied to other satellites.

Comprehensive mechanics‐based virtual model of NHERI@UCSD shake table—Uniaxial configuration and bare table condition

AbstractA mechanics‐based virtual model of the NHERI@UCSD large high‐performance outdoor shake table (LHPOST) in its uniaxial configuration and under bare table condition is presented. The virtual model includes: (i) a reprogrammed version (i.e., virtual replica) of the actual controller, (ii) a servovalve model including servovalve spool dynamics and two independent servovalve flow nonlinearities for each of the four servovalves of the system, (iii) an actuator model for each of the two single‐ended actuators, (iv) two accumulators modeling the average supply pressure drop at the entrance of the servovalves, and (v) a one‐degree‐of‐freedom mechanical subsystem model. Based on forced vibration test results for the foundation block, it was found that the soil‐foundation compliance effects do not need to be included in the virtual model of the LHPOST. The virtual model presented is able to predict accurately the actual shake table platen response under narrow and broadband inputs with the controller gains set to the same values as on the actual shake table. Moreover, the fourth stage spool displacements and actuator driving force from actual tests are reproduced accurately by the virtual model. The mechanics‐based virtual model presented in this article can be coupled with FE models of specimens to be tested on the LHPOST and can be used to improve the signal reproduction fidelity of the LHPOST and to develop more advanced model‐based shake table controllers.

An Obfuscation Method Based on CFGLUTs for Security of FPGAs

There are many different ways of securing FPGAs to prevent successful reverse engineering. One of the common forms is obfuscation methods. In this paper, we proposed an approach based on obfuscation to prevent FPGAs from successful reverse engineering and, as a result, Hardware Trojan Horses (HTHs) insertion. Our obfuscation method is using ConFiGurable Look Up Tables (CFGLUTs). We suggest to insert CFGLUTs randomly or based on some optional parameters in the design. In this way, some parts of the design are on a secure memory, which contains the bitstream of the CFGLUTs so that the attacker does not have any access to it. We program the CFGLUTs in run-time to complete the bitstream of the FPGA and functionality of the design. If an attacker can reverse engineer the bitstream of the FPGA, he cannot detect the design because some part of it is composed of CFGLUTs, which their bitstream is on a secure memory. The first article uses CFGLUTs for securing FPGAs against HTHs insertion, which are results of reverse engineering. Our methods do not have any power and hardware overhead but 32 clock cycles time overhead.

RETRACTED: Enterprise human resource management platform based on FPGA and data mining

Optimal allocation management of corporate human resources is achieved to increase the efficiency of proposed Adaptive neural network optimal allocation model based on the FPGA design and information mining control of corporate HR, and versatile distribution of high business Human Resources (HR). The analysis and processing tool that build a database model of an optimized configuration of a company's human resources are used to build a report of an optimized configuration table model of a company's human resources. Statistical regression analysis is used by corporate talent. The ideal distribution measure is multidimensional data combination and highlight extraction. FPGA control strategies and information mining use data recovery and multidimensional information which is stacking to optimize the composition of enterprise talent using large-scale data fusion methods to make enterprise talent management decisions. The results show that by utilizing this strategy, the data combination capacity of the organization's ideal distribution of HR management is high, and the organization's capacity to ideally apportion HR is solid. It understands the designation and location of corporate HR and has superb application esteems for ideal allotment and the executives of corporate HR.

Forecasting-Based Adaptive Optimized Dispatch in Smart Grid Online

The power grid is a fusion of technologies in energy systems, and how to adjust and control the output power of each generator to balance the load of the grid is a crucial issue. As a platform, the smart grid is for the convenience of the implementation of adaptive control generators using advanced technologies. In this paper, we are introducing a new approach, the Central Lower Configuration Table, which optimizes dispatch of the generating capacity in a smart grid power system. The dispatch strategy of each generator in the grid is presented in the configuration table, and the scenario consists of two-level agents. A central agent optimizes dispatch calculation to get the configuration table, and a lower agent controls generators according to the tasks of the central level and the work states during generation. The central level is major optimization and adjustment. We used machine learning to predict the power load and address the best optimize cost function to deal with a different control strategy. We designed the items of the cost function, such as operations, maintenances and the effects on the environment. Then, according to the total cost, we got a new second-rank-sort table. As a result, we can resolve generator’s task based on the table, which can also be updated on-line based on the environmental situation. The signs of the driving generator’s controller include active power and system’s f. The lower control level agent carries out the generator control to track f along with the best optimized cost function. Our approach makes optimized dispatch algorithm more convenient to realize, and the numerical simulation indicates the strategy of machine learning forecast of optimized power dispatch is effective.

Noise-Robust, Reconfigurable Canny Edge Detection and its Hardware Realization

Edge detection is a common operation in image/video processing applications. Canny edge detection, which performs well in different conditions, is one of the most popular and widely used of these algorithms. Canny’s superior performance is due mainly to its provision of the ability to adjust the output quality by manipulating the edge detection parameters, Sigma and Threshold. Calculating values for these two parameters on-the-fly and based on the application’s circumstances requires additional preprocessing, which increases the algorithm’s computational complexity. To reduce the complexity, several proposed methods simply employ precalculated, fixed values for the Canny parameters (based on either the worst or typical conditions), which sacrifices the edge detection’s performance in favor of the computational complexity. In this paper, an adaptive parameter selection method is proposed that selects values for the Canny parameters from a configuration table (rather than calculating in run-time), based on the estimated noise intensity of the input image and the minimum output performance that can satisfy the application requirements. This adaptive implementation of the Canny algorithm ensures that, while the edge detection performance (noise robustness) is higher than state-of-the-art counterparts in different circumstances, the execution time of the proposed Canny remains lower than those of recent cutting-edge Canny realizations.

Predicting Loading–Unloading Pile Static Load Test Curves by Using Artificial Neural Networks

In the United Arab Emirates, Continuous Flight Auger piles are the most widely used type of deep foundation. To test the pile behavior, the static load test is routinely conducted in the field by increasing the dead load while monitoring the displacement. Although the test is reliable, it is expensive to conduct. This test is usually conducted in the UAE to verify the pile capacity and displacement as the load increase and decreases in two cycles. The artificial neural network approach was used to build a model that can predict a complete static load pile test. In this paper, it was shown that by incorporating the pile configuration, soil properties, and groundwater table in one artificial neural network model, the static load test can be predicted with confidence. Six thousand field data points were used to train and validate the model. Three complete independent field tests (not included in the training stage) were used to test the model ability to predict the behavior of the pile during loading and unloading cycles. The results show excellent agreement between the actual and predicted curves in two loading–unloading cycles. The authors believe that based on this approach and the presented results of this research, the model is able to predict the entire pile load test results from start to end. The suggested approach is an excellent tool to reduce the cost associated with such expensive tests or to predict pile’s performance ahead of the actual test.

Concept of a multi sensor and freely configurable patient table for CT applications

Abstract Conventional computed tomography (CT) systems are encapsulated in hardware and software. Integration of further imaging modalities and sensors which can acquire prior knowledge for dose saving image acquisition and reconstruction techniques are barely possible. Within the scope of our research project, an open interface and freely configurable CT system is now being developed. The integration of further modalities and sensors into this system is a main target. A subproject deals with a multi sensor patient table, which provides additional information through integrated sensors. In particular, force sensors are installed inside the patient table to determine the patient’s mass. This value can be used to specify the required tube voltage, so that a more precise setting can be made in comparison to today‘s clinical practice. Studys show that a more precise kVp estimation can significantly reduce patient dose. Sensors for the monitoring of respiration and pulse are also integrated into the setup of the patient table. On the one hand, these are designed to encourage the patients to minimize disturbing movements and on the other hand to generate trigger signals for the examination. In addition to the sensor concept of the table, a position control system for vertical and horizontal movement of the table is integrated. The position of the table can be adjusted using different input devices so that a fast and intuitive handling of the table movement can be achieved for standard diagnostic and CT guided interventional procedures. The communication between all sensors, actors and the CT ist realized via the Robot Operating System (ROS) framework.

Towards building a neural network model for predicting pile static load test curves

In the United Arab Emirates, Continuous Flight Auger piles are the most widely used type of deep foundation. To test the pile behaviour, the Static Load Test is routinely conducted in the field by increasing the dead load while monitoring the displacement. Although the test is reliable, it is expensive to conduct. This test is usually conducted in the UAE to verify the pile capacity and displacement as the load increase and decreases in two cycles. In this paper we will utilize the Artificial Neural Network approach to build a model that can predict a complete Static Load Pile test. We will show that by integrating the pile configuration, soil properties, and ground water table in one artificial neural network model, the Static Load Test can be predicted with confidence. We believe that based on this approach, the model is able to predict the entire pile load test from start to end. The suggested approach is an excellent tool to reduce the cost associated with such expensive tests or to predict pile’s performance ahead of the actual test.

Towards building a neural network model for predicting pile static load test curves

In the United Arab Emirates, Continuous Flight Auger piles are the most widely used type of deep foundation. To test the pile behaviour, the Static Load Test is routinely conducted in the field by increasing the dead load while monitoring the displacement. Although the test is reliable, it is expensive to conduct. This test is usually conducted in the UAE to verify the pile capacity and displacement as the load increase and decreases in two cycles. In this paper we will utilize the Artificial Neural Network approach to build a model that can predict a complete Static Load Pile test. We will show that by integrating the pile configuration, soil properties, and ground water table in one artificial neural network model, the Static Load Test can be predicted with confidence. We believe that based on this approach, the model is able to predict the entire pile load test from start to end. The suggested approach is an excellent tool to reduce the cost associated with such expensive tests or to predict pile’s performance ahead of the actual test.

Automated construction of software configuration tables for real-time systems in avionics

A problem of automated construction of configuration tables for real-time operating systems is considered. Software configuration information is categorized as follows: information depending on the target function of the system, information depending on the operating system (OS), and information depending on system hardware. For a specialized system meant for automated design of OS configurations, information links are established between its components and other functional elements of the programming software as part of the programmer's workplace. An automated software configuration environment developed by the authors of this paper is described. An example of working windows of a program for automated design of software configurations for avionics under control of the ARINC 653 compliant real-time operating system is presented.

The Research and Application on Design Method for “Virtual Terminal” of Smart Substation

According to the new changes brought out by the smart substation, including disappearance of the traditional concept of terminal and replacement of the original point to point cable connection by optical fiber network, a new design method of GOOSE “virtual terminal” is put forward. The method contains GOOSE “virtual terminal”, virtual terminal logic connection line, GOOSE configuration table and virtual terminal information flow diagram, and solves some problems of digital protection, measurement and control device, such as without contact, no terminal, no wiring etc. of GOOSE information. The design method was applied in the engineering practice of the south of Changshu 500kV smart substation in Suzhou, and the effect of designing was very good.

A National System for Incident Reporting in Radiation Therapy: Development of a Taxonomy and Severity Classification

Purpose/Objective(s): Computerized Record and Verify System (RVS) has been used to detect and prevent mistakes in the delivery of external beam radiation therapy for more than 20 years. Conventional dose tracking mechanism in RVS is monitor unit (MU) based, assuming patients are treated exactly as planned. Without incorporating parameters such as machine behavior, patient setup, and patient anatomy, the cumulative doses do not accurately represent the actual doses delivered to patients, which is the goal of a RVS. We developed an automatic system to compute and accumulate daily doses through re-computation using daily imaging, and verify dose volume information in the context of RVS. Materials/Methods: Our system consists of several components, including a DICOM data retriever, dosimetric computation engine, and review platform. The workflow begins after each treatment; the DICOM daemon automatically retrieves daily volumetric images from patient records. The delivered dose is computed using a convolution-superposition based dose calculator implemented in-house on graphic cards. The deformation field between the daily and planning CT is computed using Morphons algorithm to warp the planning structures and delivered doses. The dose volume histograms of the daily and cumulative doses are generated for dose tracking and reporting. These data are queryable and a triggering system using a configurable tolerance table can be set up to prompt a clinical review. Possible root causes are analyzed such as machine output drifting, patient set up error. A user-friendly 4D image viewer is integrated for efficient review of images and doses. Results: Various aspects of the system were validated. Our dose calculator passed 97% gamma test evaluated in 3D with 2% dose difference and 2mm distance-to-agreement compared with tomotherapy calculated dose. With proper calibration of the imaging systems on tomotherapy machines, recomputed daily doses agreed with measurements in phantom within 2% for all jaw sizes. Visual inspection shows acceptable and consistent deformable registration results, except for cases with large or unrealistic deformations. This system is used clinically and more than 35,000 fractions were computed. More than 50 incidences, which would otherwise not be caught with conventional MU-based dose tracking system, were detected. The automatic triggering system is able to detect significant patient setup errors and anatomical changes. Conclusions: We developed a system to automatically accumulate and verify the actual distribution of delivered dose over the course of radiation therapy. This system helps detect and prevent further deviations in treatments, quantifies the dosimetric impact of the deviations, provides necessary information for adaptive radiation therapy, and hence is invaluable in improving the quality of patient care. Author Disclosure: X. Mo: None. Y. Chen: None. M. Chen: None. D. Parnell: None. S. Key: None. W. Lu: None. G. Olivera: None. D. Galmarini: None.

A Delivered Dose–Based Record and Verify System in the Era of IGRT

Purpose/Objective(s): Computerized Record and Verify System (RVS) has been used to detect and prevent mistakes in the delivery of external beam radiation therapy for more than 20 years. Conventional dose tracking mechanism in RVS is monitor unit (MU) based, assuming patients are treated exactly as planned. Without incorporating parameters such as machine behavior, patient setup, and patient anatomy, the cumulative doses do not accurately represent the actual doses delivered to patients, which is the goal of a RVS. We developed an automatic system to compute and accumulate daily doses through re-computation using daily imaging, and verify dose volume information in the context of RVS. Materials/Methods: Our system consists of several components, including a DICOM data retriever, dosimetric computation engine, and review platform. The workflow begins after each treatment; the DICOM daemon automatically retrieves daily volumetric images from patient records. The delivered dose is computed using a convolution-superposition based dose calculator implemented in-house on graphic cards. The deformation field between the daily and planning CT is computed using Morphons algorithm to warp the planning structures and delivered doses. The dose volume histograms of the daily and cumulative doses are generated for dose tracking and reporting. These data are queryable and a triggering system using a configurable tolerance table can be set up to prompt a clinical review. Possible root causes are analyzed such as machine output drifting, patient set up error. A user-friendly 4D image viewer is integrated for efficient review of images and doses. Results: Various aspects of the system were validated. Our dose calculator passed 97% gamma test evaluated in 3D with 2% dose difference and 2mm distance-to-agreement compared with tomotherapy calculated dose. With proper calibration of the imaging systems on tomotherapy machines, recomputed daily doses agreed with measurements in phantom within 2% for all jaw sizes. Visual inspection shows acceptable and consistent deformable registration results, except for cases with large or unrealistic deformations. This system is used clinically and more than 35,000 fractions were computed. More than 50 incidences, which would otherwise not be caught with conventional MU-based dose tracking system, were detected. The automatic triggering system is able to detect significant patient setup errors and anatomical changes. Conclusions: We developed a system to automatically accumulate and verify the actual distribution of delivered dose over the course of radiation therapy. This system helps detect and prevent further deviations in treatments, quantifies the dosimetric impact of the deviations, provides necessary information for adaptive radiation therapy, and hence is invaluable in improving the quality of patient care. Author Disclosure: X. Mo: None. Y. Chen: None. M. Chen: None. D. Parnell: None. S. Key: None. W. Lu: None. G. Olivera: None. D. Galmarini: None.