Greedy Alignment

A real-time operating system (RTOs) is often used in embedded system, to structure the application code and to ensure that the deadlines are met by reacting on events by executing the functions within precise time. Most embedded systems are bound to real-time constraints with determinism and latency as a critical metrics. RTOs are generally implemented in software, increases computational overheads, jitter and memory footprint. Modern FPGA technology, enables the implementation of a full featured and flexible hardware based RTOs, which helps in reducing to greater extent these overheads even if not remove completely. Scheduling algorithms play an important role in the design of real-time systems. An Adaptive Fuzzy Inference System (FIS) based scheduler framework proposed in this article is based on the study and conclusion drawn from the research over the years in HW SW co-design domain. The proposed novel two phase FIS based adaptive hardware task scheduler minimizes the processor time for scheduling activity which uses fuzzy logic to model the uncertainty at first stage along with adaptive framework that uses feedback which allows processors share of task running on multiprocessor to be controlled dynamically at runtime. This Fuzzy logic based adaptive hardware scheduler breakthroughs the limit of the number of total task and thus improves efficiency of the entire real-time system. The increased computation overheads resulted from proposed two phase FIS scheduler can be compensated by utilising the basic characteristics of parallelism of the hardware as scheduler being migrated to FPGA.

The fundamental motivation behind this study is to identify the brain hemorrhage and to give accurate treatment so that a human life can be saved due to brain hemorrhage. Brain hemorrhage is a type of stroke which is caused by an artery in the brain bursting and causing bleeding in the surrounded tissues. Diagnosing brain hemorrhage, which is mainly through the examination of a CT scan, enables the accurate prediction of disease. This project investigates the diagnosing brain hemorrhage more accurately using an image segmentation of CT scan images using watershed method and feeding of the appropriate inputs extracted from the brain CT image to an artificial neural network for classification. The output generated as the type of brain hemorrhages, can be used to verify expert diagnosis and also as learning tool for trainee radiologists to minimize errors in current methods.

A real-time operating system (RTOs) is often used in embedded system, to structure the application code and to ensure that the deadlines are met by reacting on events by executing the functions within precise time. Most embedded systems are bound to real-time constraints with determinism and latency as a critical metrics. RTOs are generally implemented in software, increases computational overheads, jitter and memory footprint. Modern FPGA technology, enables the implementation of a full featured and flexible hardware based RTOs, which helps in reducing to greater extent these overheads even if not remove completely. Scheduling algorithms play an important role in the design of real-time systems. An Adaptive Fuzzy Inference System (FIS) based scheduler framework proposed in this article is based on the study and conclusion drawn from the research over the years in HW SW co-design domain. The proposed novel two phase FIS based adaptive hardware task scheduler minimizes the processor time for scheduling activity which uses fuzzy logic to model the uncertainty at first stage along with adaptive framework that uses feedback which allows processors share of task running on multiprocessor to be controlled dynamically at runtime. This Fuzzy logic based adaptive hardware scheduler breakthroughs the limit of the number of total task and thus improves efficiency of the entire real-time system. The increased computation overheads resulted from proposed two phase FIS scheduler can be compensated by utilising the basic characteristics of parallelism of the hardware as scheduler being migrated to FPGA.

Effective management of Slipforming operations is critical due to operation's linearity that is considered a source of planning and proper cost management complications. This paper is presented to assist practitioners and researchers for automating the process of generation both time schedules and costs estimates simultaneously for Slipform projects by developing an Integrated BIM-based and DES software and framework. The BIM Schedule and Cost integrated (BSCI) framework reinforces both BIM and DES capabilities, enhanced by BIMs extracted information the quantities are used to generate DES input data in order to calculate the execution times for construction processes and exchange data through various software. Proposed research framework consists of: 1) Slipform BIM model that exports material quantity takeoffs , schedules and required resources to an external database, 2) Discrete Event Simulation model encountering complete specification of the interdependencies between activ...

A real-time operating system (RTOs) is often used in embedded system, to structure the application code and to ensure that the deadlines are met by reacting on events by executing the functions within precise time. Most embedded systems are bound to real-time constraints with determinism and latency as a critical metrics. RTOs are generally implemented in software, increases computational overheads, jitter and memory footprint. Modern FPGA technology, enables the implementation of a full featured and flexible hardware based RTOs, which helps in reducing to greater extent these overheads even if not remove completely. Scheduling algorithms play an important role in the design of real-time systems. An Adaptive Fuzzy Inference System (FIS) based scheduler framework proposed in this article is based on the study and conclusion drawn from the research over the years in HW SW co-design domain. The proposed novel two phase FIS based adaptive hardware task scheduler minimizes the processor ...

Job-Shop Scheduling (JSS) processes have highly complex structure in terms of many criteria. Because there is no limitation in the number of the process and there are many alternative scheduling. In JSS, each order that is processed on different machines has its own process and process order. It is very important to put these processes into a sequence according to a certain order. In addition, some constraints must be considered in order to obtain the appropriate tables. In this paper, a 3-layers Feed Forward Backpropagation Neural Network (FFBNN) has been used for two different purposes, the first one task is to obtain the priority and the second one role is to determine the starting order of each operation within a job. Precedence order of operations indicates the dependency of subtasks within a job, Furthermore, the combined greedy procedure along with the back propagation algorithm will align operations of each job until best solution is obtained. In particular, greedy type algo...

Due to stringent constraints such as batterypowered, high-speed, low-voltage power supply and noiseexposed operation, safety-critical real-time embedded systems are often subject to transient faults originated from a large spectrum of noisy sources; among them, conducted and radiated Electromagnetic Interference (EMI). As the major consequence, the system's reliability degrades. In this paper, we present the most recent results involving the reliability analysis of a hardware-based intellectual property (IP) core, namely Real-Time Operating System-Guardian (RTOS-G). This is an on-chip watchdog that monitors the RTOS' activity in order to detect faults that corrupt tasks' execution flow in embedded systems running preemptive RTOS. Experimental results based on the Plasma processor IP core running different test programs that exploit several RTOS resources have been developed. During test execution, the proposed system was aged by means of total ionizing dose (TID) radiation and then, exposed to radiated EMI according to the international standard IEC 62.132-2 (TEM Cell Test Method). The obtained results demonstrate the proposed approach provides higher fault coverage and reduced fault latency when compared to the native (software) fault detection mechanisms embedded in the kernel of the RTOS.

A real-time operating system (RTOs) is often used in embedded system, to structure the application code and to ensure that the deadlines are met by reacting on events by executing the functions within precise time. Most embedded systems are bound to real-time constraints with determinism and latency as a critical metrics. RTOs are generally implemented in software, increases computational overheads, jitter and memory footprint. Modern FPGA technology, enables the implementation of a full featured and flexible hardware based RTOs, which helps in reducing to greater extent these overheads even if not remove completely. Scheduling algorithms play an important role in the design of real-time systems. An Adaptive Fuzzy Inference System (FIS) based scheduler framework proposed in this article is based on the study and conclusion drawn from the research over the years in HW SW co-design domain. The proposed novel two phase FIS based adaptive hardware task scheduler minimizes the processor time for scheduling activity which uses fuzzy logic to model the uncertainty at first stage along with adaptive framework that uses feedback which allows processors share of task running on multiprocessor to be controlled dynamically at runtime. This Fuzzy logic based adaptive hardware scheduler breakthroughs the limit of the number of total task and thus improves efficiency of the entire real-time system. The increased computation overheads resulted from proposed two phase FIS scheduler can be compensated by utilising the basic characteristics of parallelism of the hardware as scheduler being migrated to FPGA.

Many embedded applications, specifically safety-critical ones, have strict real-time constraints. In the very worst case, missing a deadline can be catastrophic. Therefore, many approaches have been developed and successfully deployed whereby time is explicitly used to schedule the application tasks. A very important design paramater is a guaranteed Worst Case Execution Time (WCET). While this approach can be justified partly for historical reasons but also for reasons of simplicity, modern many-core processors pose a significant challenge as the chips combine multiple tightly coupled processing cores, fast caches to alleviate slow memory and complex peripherals. All these elements result in a statistical execution behaviour whereby a measure like WCET is no longer practical. In this paper we advocate that this situation requires a different approach to programming, i.e. one based on events and concurrency with time no longer being a strict design parameter but rather a consequence of the program execution. It is a consequence of applying a separation of concerns to execution in space and time. Benchmarks obtained with the latest version of VirtuosoNext Designer, a fine-grain partitioning multi-core RTOS, illustrate that this is not only feasible but also with no compromise on the real-time behavior. In the latest implementation this was extended to real-time fault recovery making systems much more resilient than with the traditional approach.

Recently, scheduling problems proceed in domains as diverse as manufacturing, computer processing, transportation, health care, space exploration, and education. A scheduling technique role is to organize the allocation of resource(s) over time to perform allocation of tasks. This paper presents the design and analysis of a generalized Job-Shop (JS) problem with priority assignment of jobs. Two Feed-Forward Neural Networks (FFNN) have been developed to solve the scheduling problem. The starting time for various operations will be determined using FFNN. Then, these operations will be scheduled by greedy algorithm. The greedy alignment procedure aligns operations of various jobs on respective machines with feasible/optimal Finishing Time (FT). Each operation within a job is predefined priority by another FFNN, which is trained to recognize jobs from a job queue to estimate each job’s priority. Simulation results prove the validity of the proposed approach.

Classification is one of the data mining problems receiving great attention recently in the database community. This paper presents an approach to discover symbolic classification rules using neural networks. Neural networks have not been thought suited for data mining because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by humans. With the proposed approach, concise symbolic rules with high accuracy can be extracted from a neural network. The network is first trained to achieve the required accuracy rate. Redundant connections of the network are then removed by a network pruning algorithm. The activation values of the hidden units in the network are analyzed, and classification rules are generated using the result of this analysis. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of standard data mining test problems.

Recently, scheduling problems proceed in domains as diverse as manufacturing, computer processing, transportation, health care, space exploration, and education. A scheduling technique role is to organize the allocation of resource(s) over time to perform allocation of tasks. This paper presents the design and analysis of a generalized Job-Shop (JS) problem with priority assignment of jobs. Two Feed-Forward Neural Networks (FFNN) have been developed to solve the scheduling problem. The starting time for various operations will be determined using FFNN. Then, these operations will be scheduled by greedy algorithm. The greedy alignment procedure aligns operations of various jobs on respective machines with feasible/optimal Finishing Time (FT). Each operation within a job is predefined priority by another FFNN, which is trained to recognize jobs from a job queue to estimate each job’s priority. Simulation results prove the validity of the proposed approach.

Job-Shop Scheduling (JSS) processes have highly complex structure in terms of many criteria. Because there is no
limitation in the number of the process and there are many alternative scheduling. In JSS, each order that is processed
on different machines has its own process and process order. It is very important to put these processes into a sequence
according to a certain order. In addition, some constraints must be considered in order to obtain the appropriate tables.
In this paper, a 3-layers Feed Forward Backpropagation Neural Network (FFBNN) has been used for two different
purposes, the first one task is to obtain the priority and the second one role is to determine the starting order of each
operation within a job. Precedence order of operations indicates the dependency of subtasks within a job, Furthermore,
the combined greedy procedure along with the back propagation algorithm will align operations of each job until best
solution is obtained. In particular, greedy type algorithm will not always find the optimal solution. However, adding
a predefined alignment dataset along with the greedy procedure result in optimal solutions.

This paper presents a feed-forward neural network (NN), together with an alignment algorithm to solve a generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed NN is embedded with predefined criteria which is relevant to determine starting time of various operations within a job and is trained until its mean squared error is reduced to a value less than 10%. The output of the NN is used by the alignment algorithm to detect the precedence order of each operation within a job and schedule each operation on respective machine without violating the detected precedence order (precedence constraint) and resource constraint. The key findings reveal that the NN plays a decisive role to estimate the starting time of each operation within a job before the operations are scheduled on a respective machine. Simulations of the proposed scheduler have shown that the NN with the alignment algorithm approach is efficient with respect to the quality of expected solutions and the solving speed.

This paper describes the design and implementation of a neural network-based job priority assigner system for a job scheduling environment. Scheduling deals with the allocation of resources over time to perform a collection of tasks. Scheduling problems arise in domains as diverse as manufacturing, computer processing, transportation, health care, space exploration, and education. In the case of a neural network (NN) based scheduler, once the job attributes are properly trained for a specified schedule, it will never miss that related scheduling pattern for that particular job. An NN based scheduling procedure can successfully overcome the local minima of its error surface. This paper reports on research which established that a back propagation neural network-based priority procedure would recognize jobs from a job queue by estimating each job's priority. Once the priorities are assigned, it is not possible to alter the priorities under any circumstances.