Narrow Results

To solve the problem of the frequent occurrence of roadbed faults, we studied the highway roadbed intelligent monitoring system based on a combined neural network algorithm. Based on the embedded system, with a variety of sensors, we completed the construction of the roadbed monitoring system. In the selection of the data processing algorithm model, the combined neural network algorithm based on an artificial immune algorithm and probabilistic neural network (PNN) is selected. The accurate acquisition of data characteristics is realized by data preprocessing, data smoothing and data fitting. Through experimental verification, the accuracy of the research model in identifying roadbed settlements has been improved by about 5% compared to traditional models. Furthermore, the processing time of the model has been shortened by about 19.5%, proving the effectiveness of the model. In terms of fault identification, compared with other classic models, the final recognition accuracy of this model reached 96.7%, far exceeding the comparison model. This provides new ideas for the monitoring and protection of roadbed faults.

In the Internet of Things era, more intelligent systems can communicate with each other. Embedded system combined with network communication applications has become the basis for Internet of Things research. The programmable logic unit designed by ARM architecture has great advantages in running speed, power control and so on. In the paper, to solve the problems of common embedded MCU control resources occupying a large amount of memory and the slow speed of building engineering simulation model, it is necessary to use the queuing connection algorithm model to directly input the timing physical characteristics of the code stream in the embedded system to calculate and match the timing physical characteristics of the output code stream. The optimization algorithm of DTF-MARTE to detect the probability of timing deviation is used in the paper. It is to detect the problem of inaccurate timing information in the demand. We compare the expected physical characteristics of the timing sequence and obtain the timing deviation probability of the output data stream. The model developed in this paper has the characteristic of dynamic reconfiguration of the task interval. The design of monotonically decreasing data tasks can be realized, and the reconfigured task modules are used for interacting the data buffer area and dynamically reconstructing the instruction overhead and transmission. We analyze the performance comparison between the proposed model and the traditional communication connection model. It proves that the proposed model can further improve the priority queue and guide the data flow. According to that method, the problem of asynchronous spatial data interaction by controlling and combining different communication modes in a large scene can be solved. Data interaction can be triggered at a fixed time, and mutual interference of randomly triggered wireless communication and data acquisition modules can be avoided. It can solve the problem of insufficient computing power when future embedded devices need massive data encryption in the Internet of Things era, and provide a new way of thinking for fast, safe and efficient implementation.

When a fault occurs in the distribution network, the power restoration system receives the relevant data and quickly analyzes and evaluates it. After the distribution network is rebuilt to locate and isolate the fault, the network distribution constraints are operated to find the optimal solution of multiple objective functions and the best choice for power restoration. At present, most of the complete solutions adopt the traditional transformation to a single goal to solve the problem. If the implementation of the scheme is complex, the amount of data is very large, affecting the efficiency of the reconstruction model. In this paper, we use the distributed network to select the fault node of the distribution network. We consider the combination of the modified droop control coefficient with the consensus algorithm to realize the secondary optimal control of the multi-microgrid system. We also adopt the real-time dynamic data to follow the target value to ensure the power supply load’s controllable range to avoid the occurrence of regional blackouts. According to the information exchange mode between distribution network nodes and microgrids, a data analysis model of sparse communication is established to control the power output and load capacity of each node in the distribution network area. The composite Depth-First Search algorithm constructed in this paper can control the needs of multi-node energy matching service as well as the fault analysis model, thus realizing the function solution of the preferred node’s output. The simulation model’s experimental results verify that the elastic effect of the distribution network microgrid group operation mode is improved after the optimization with the security as the objective. The results also show that the theory and method in this paper are superior to the current mainstream controllable scheme because they have the advantages of short response time, strong anti-interference ability, and good noise reduction effect, supporting the emergency load’s controllable management in the distribution network.

The high-precision positioning technology of industrial Unmanned Aerial Vehicle (UAV) plays a vital role in intelligent trajectory planning in power inspection. First, the information on power facilities is collected and processed to form a planning grid to optimize the flight path and correct the trajectory of UAV. However, the preparatory work is more complex, and the algorithm model which needs more detailed and complex terrain database data is not universal enough. To improve the safety of UAV and the efficiency of path planning, we establish a three-dimensional model of the power tower, calculate the coordinate position of the inspection work according to the information of the high-voltage transmission tower, and then determine the corresponding relationship between the inspection point and the flight trajectory combined with the safe distance. Based on the set of random interference semaphores and model rasterization, the discrete error strategy and Euler method are introduced to improve the performance. In each iteration, the best solution is first updated using the execution strategy. We continue to calculate the spatial coordinates of all reference points and provide the coordinate position distribution for the flight mission. The solution of equipment inspection trajectory optimization is realized by using dynamic obstacle perception. The proposed discrete measurement error self-correction algorithm is deployed on the flight platform to guide UAV inspection tasks according to the point cloud coordinate model of transmission equipment in the power system and to correct the flight route in time. To verify the research results, the Algorithm Artemisinin optimization and Whale Optimization Algorithm test verify that the ability of the proposed algorithm to get rid of local optimization is improved by 8.94% and, 12.42% respectively compared with other optimization algorithms. The convergence accuracy is 12.4% and 7.9% higher than other schemes, and it has a better effect in solving numerical problems. The research results using the embedded system to complete the task deployment and unloading provide a reference for trajectory planning and task design in different application scenarios.

With the gradual deepening of sports research, sports training methods integrating more scientific and technological means can greatly improve athletes’ professional ability. Combined with the isokinetic strength training method, it can effectively alleviate muscle recovery and body flexibility, which requires an analysis of the level of sports body promotion, and a more efficient training program for various sports parameters. In this study, taking strength enhancement as an example, the convolutional neural network is innovatively integrated into isokinetic strength training. Using the motion data obtained by wearable sensors in the training, we trained the neural network model by real-time analysis of data samples through an embedded system. Further, we optimized the network parameters to realize the extraction and recognition of EMG depth features. Compared with the traditional isokinetic strength classification model, this model lays a foundation for further constructing personalized strength training guidance methods. After the simulation test, the difference of the model’s accuracy requirement before and after the pre-training is 4.6%, and the difference of the test set before and after the pre-intensive training is $<$1%. It is 16.2% higher than the TCAM algorithm, and the gap with STA-LSTM remains between $4.1\%$. It is verified that the attention concentration control scheme can not only reduce the error rate in visual recognition tasks, but also improve the complexity of the neural network model. The optimized lightweight data structure is especially suitable for embedded systems with limited computing power. All kinds of test projects verify its rationality and make full use of the control ability of the embedded system and the characteristics of parallel computing and programmability, which has great practical application value for the development of the Internet of Things system network. The research results have made a thorough study of the physiological guidance of existing strength training, the identification and evaluation methods of exercise-induced muscle fatigue, and the current situation of the existing isokinetic equipment.

In the process of smart grid construction, the increasing demand for electricity in power-consuming areas often leads to the operation of distribution network equipment at full load or even overload, which puts great pressure on the protection of the power system. At present, equipment load forecasting models are mainly used to predict and analyze various data-related factors of equipment, which are divided into temporal factors and nonsequential factors, to reduce the complexity of equipment management on embedded platforms. According to the original intention of ubiquitous power Internet of Things (IoT) construction, this paper uses the embedded platform of distribution network equipment to build a fog network. It also adopts a virtual defined network to manage the data communication of each node, and effectively utilizes edge computing to complete data processing tasks. A lightweight load forecasting scheme with both time series and nontime characteristics is proposed, and a neural network feature model is pre-trained. For the sensing parameters extracted from embedded systems of power equipment and load prediction, the unidirectional continuous cyclic long short-term memory (LSTM) connection is regarded as a time series neural network modeling. We then consider the determining factors of historical data on equipment operation capability to predict the effectiveness of load capacity, and extract load data feature vectors to compare potential relationships. Simulation tests have shown that the research method proposed in this paper is more effective in predicting load capacity and analyzing different temporal patterns of loads, facilitating the decomposition of data in the initial stage. Software-defined virtual networks use lightweight structures to reduce network operational pressure and minimize data space occupation. The research results can achieve the important function of early warning of abnormal operation of the power grid system, reduce the operational risks of the power grid system, and have significant implications for improving the economic and social benefits of the power system.

Artificial intelligence has the ability of self-learning and self-adaptation. It has become one of the important methods to study the trading behavior of the participants in the electricity market. With the rapid development of the smart grid and the rise of the Internet of Things and blockchain, the combination of the three has become one of the development directions of the power industry in the future. Considering the problem of low expansibility of distributed transaction data after the combination of blockchain and microservices, this paper proposes an electricity transaction settlement model based on blockchain under the intelligent Internet of Things. Firstly, the intelligent Internet of Things collects data through power equipment and transmits it to the blockchain to build a distributed network of Software-defined networking (SDN). Secondly, to meet the operation architecture data unification of different intelligent terminals, the micro-service architecture is adopted. That is to say, through the combination of the power trading business and marketing management system, it is decomposed into multiple large-scale power trading micro-service architecture clusters from the perspective of the power trading business. Moreover, in view of the high frequency of data calls, it is necessary to optimize the microservice architecture continuously. Finally, according to the embedded system execution mechanism and other functions, the multi-sub-chain fog layer architecture design is introduced under the micro-service architecture design. Multiple threads are started in the transaction process to maintain the corresponding sub-chain. The complete test shows that the system does not run at full load under the condition of ensuring the calculation accuracy. This has verified that the system fully meets the computing power requirements of medium and long-term electricity transaction settlement, and also achieves the desired results in accuracy and power consumption.

In modern embedded systems, the increasing number of cores requires efficient cache hierarchies to ensure data throughput, but such cache hierarchies are restricted by their tumid size and interference accesses which leads to both performance degradation and wasted energy. In this paper, we firstly propose a behavior-aware cache hierarchy (BACH) which can optimally allocate the multi-level cache resources to many cores and highly improved the efficiency of cache hierarchy, resulting in low energy consumption. The BACH takes full advantage of the explored application behaviors and runtime cache resource demands as the cache allocation bases, so that we can optimally configure the cache hierarchy to meet the runtime demand. The BACH was implemented on the GEM5 simulator. The experimental results show that energy consumption of a three-level cache hierarchy can be saved from 5.29% up to 27.94% compared with other key approaches while the performance of the multi-core system even has a slight improvement counting in hardware overhead.

The Prioritized Production System (PRIOPS) is an architecture that supports time-constrained, knowledge-based embedded system programming and learning. Inspired by the theory of automatic and controlled human information processing in cognitive psychology, PRIOPS supports a two-tiered processing approach. The automatic partition provides for compilation of productions into constant-time-constrained processes for reaction to environmental conditions. The notion of a habit in humans approximates the concept of automatic processing trading flexibility and generality for efficiency and predictability in dealing with expected environmental situations. Explicit priorities allow critical automatic activities to pre-empt and defer execution of lower priority processing. An augmented version of the Rete match algorithm implements O(1), priority-scheduled automatic matching. The controlled partition supports more complex, less predictable activities such as problem solving, planning, and learning that apply in novel situations for which automatic reactions do not exist. The PRIOPS notation allows the programmer of knowledge-based embedded systems to work at a more appropriate level of abstraction than is provided by conventional embedded system programming techniques. This paper explores programming and learning in PRIOPS in the context of a maze traversal program.

The Java programming language is being increasingly used for application development for mobile and embedded devices. Limited energy and memory resources are important constraints for such systems. Compression is an useful and widely employed mechanism to reduce the memory requirements of the system. As the leakage energy of a memory system increases with its size and because of the increasing contribution of leakage to overall system energy, compression also has a significant effect on reducing energy consumption. However, storing compressed data/instructions has a performance and energy overhead associated with decompression at runtime. The underlying compression algorithm, the corresponding implementation of the decompression and the ability to reuse decompressed information critically impact this overhead.

In this paper, we explore the influence of compression on overall memory energy using a commercial embedded Java virtual machine (JVM) and a customized compression algorithm. Our results show that compression is effective in reducing energy even when considering the runtime decompression overheads for most applications. Further, we show a mechanism that selectively compresses portions of the memory to enhance energy savings. Finally, a scheme for clustering the code and data to improve the reuse of the decompressed data is presented.

We designed and tested a novel field-programmable gate array (FPGA)-based embedded system that uses automatic censored ordered statistics detector (ACOSD) algorithms to detect targets in clutter with lognormal distribution. The detection process operates through two techniques called backward and forward ACOSD (B-ACOSD and F-ACOSD, respectively), which work in parallel to increase the detection accuracy and reduce the false alarm rate. Two architectures were considered for the proposed detector. The B-ACOSD algorithm operates the censoring beginning from the last cell belonging to a window of N range cells, whereas the F-ACOSD algorithm considers the censoring based on a scan beginning with the first cell in the same sorted window of cells. The detector is implemented on a FPGA-Altera Stratix II as a system-on-chip that integrates a Nios II core processor with our proposed detector as a co-processor and additional embedded memories and interfaces using parallelism and pipelining. For a reference window of 16 cells, the processor works properly with a processing speed of up to 129.13 MHz and a processing time of only 0.23 μs, within the range of the maximum tolerated delay of 0.5 μs fixed by the pulse width [A. Farina, A. Russo and F. A. Studer, IEE Proc. F Commun. Radar Signal Process.133 (1986) 39–54] for viewing a target at high resolution.

Recently, flash memory is widely used as a non-volatile storage for embedded applications such as smart phones, MP3 players, digital cameras and so on. The software layer called flash translation layer (FTL) becomes more important since it is a key factor in the overall flash memory system performance. Many researchers have proposed FTL algorithms for small block flash memory in which the size of a physical page of flash memory is equivalent to the size of a data sector of the file system. However, major flash vendors have now produced large block flash memory in which the size of a physical page is larger than the file system's data sector size. Since large block flash memory has new features, designing FTL algorithms specialized to large block flash memory is a challenging issue. In this paper, we provide an efficient FTL named LSTAFF* for large block flash memory. LSTAFF* is designed to achieve better performance by using characteristics of large block flash memory and to provide safety by abiding by restrictions of large block flash memory. Experimental results show that LSTAFF* outperforms existing algorithms on a large block flash memory.

A Fault-Resistant scheme has been proposed to secure the Advanced Encryption Standard (AES) against Differential Fault Analysis (DFA) attack. In this paper, a hybrid countermeasure has been presented in order to protect a 32-bits AES architecture proposed for resource-constrained embedded systems. A comparative study between the most well-known fault detection schemes in terms of fault detection capabilities and implementation cost has been proposed. Based on this study, we propose a hybrid fault resistant scheme to secure the AES using the parity detection for linear operations and the time redundancy for SubBytes operation. The proposed scheme is implemented on the Virtex-5 Xilinx FPGA board in order to evaluate the efficiency of the proposed fault-resistant scheme in terms of area, time costs and fault coverage (FC). Experimental results prove that the countermeasure achieves a FC with about 98,82% of the injected faults detected during the 32-bits AES process. The area overhead of the proposed countermeasure is about 14% and the additional time delay is about 13%.

Modular multiplication (MM) is an important arithmetic operation in public key cryptography (PKC). In this paper, we present the FPGA implementation of the MM using Montgomery MM (MMM) algorithm. The execution performances of this operation depend on the radix-$r$ and the operands length. In fact, when increasing the radix-$r$, the MMM algorithm requires multiplications of digit by operand. On the other hand, when a long modulus is used, the hardware implementation of the MMM needs a large area. Our objective in this work is to realize a scalable architecture able to support any operands length. In order to achieve a best trade-off between computation throughput and hardware resources, our implementation approach is based on the execution of the basic arithmetic operations in serial way. In addition, efficient parallel and pipelined strategies are realized at low-level abstraction for the optimization of the execution time. The implementations results on Virtex-7 circuit show that a 1024-bit MMM runs in 2.09$\mu$s and consumes 581 slices.

Soft errors are the most common aspect of errors that are incurred in the memory devices during transmission. The common and fundamental reason of these soft errors is radiation that produces leakage of current and results in misleading information which is sent to various transmission stations via satellite and microwave communication. In this paper, the real-time embedded system is designed and implemented to mitigate the soft error using hybrid Hamming code. The work also develops the hardware system for soft error mitigation. The designed system is compared with the other coding schemes that are commonly available for error mitigation. The performance of real-time embedded system for soft error mitigation is carried out using signal-to-noise ratio and other performance metrics. The timing diagram analysis is the key metrics of the paper that defined the performance of the designed soft error mitigation design using the proposed technique. Furthermore, the results of the designed systems are demonstrated using bit error probability, Pb and channel symbol error probability (CSEP). The impact of the designed system will be that from now onwards using the proposed system, the soft error produced due to radiation and other reasons would not affect too much on transmission and reception of important data via satellite and microwave communication.

Tele-medical systems have proven to be very useful to improve patient outcomes. However, they suffered from drawbacks such as insufficient functionality, prohibitive cost, the lack of connectivity and many other factors. To solve these problems, medical experts were consulted and an embedded tele-medical system was developed that allows a doctor to analyze and predict the ailment of a patient and direct the paramedic on the scene to perform potentially life-saving corrective actions or even promote recovery. The system would capture, process and interpret ECG data from the patient using the low cost hardware which was designed.

This paper introduces a low-cost (i.e. economically competitive, yet functionally robust) embedded system known as the InterDAQ (i.e. Internet Data AcQuisition). The current energy savings technology relies on conventional data logging systems, in which two major barriers exist. Foremost is the fact that retrieving the energy data is not convenient, and the cost of the data logging equipment is high. The interdisciplinary solution presented here to accomplish these goals is to include a miniature web server in a remote-logging module, which we designed as part of our device, thus allowing data to be accessed more frequently, via the Internet.

To illustrate a state-of-the-art application in which remote, low-cost monitoring is needed, it is worth mentioning that distributed power generation is gaining national and international attention.^1–3 The main energy source of distributed power is derived from a fuel cell, a micro-turbine, or a photo-voltaic cell. Distributed power systems offer a potential increase in efficiency by localizing power generation and eliminating the need for transmission.¹ Distributed power also offers increased reliability, uninterruptible service, and energy cost savings.²If an energy savings program is to be implemented, then a low-cost energy monitoring strategy is paramount. Our Internet appliance provides such a solution, and this paper summarizes our implementation details and provides a computer screen-capture of the data posted on the Web.

Landmark-based car navigation is a widely used technique for automotive and robot navigation. Wireless landmarks have some key features such as robustness and simple detection that make them suitable for automotive navigation. In this paper, a light-weight embedded algorithm for high speed car navigation in the roads with branches is presented which can be efficiently used in real-time automotive systems. We implemented the proposed algorithm on a real-time MIPS-based embedded system and analyzed its accuracy and efficiency in some real road maps, especially for high speed movements in real roads. Experimental results show that the proposed algorithm can be used for high speeds (up to about 360 km/h) with a very small error rate. Additionally, the experimental results show that the power consumption of the proposed system is suitable for built-in car applications.

In the actual microbial fermentation process, excessive or insufficient substrate can produce inhibitory effects on cells growth. The artificial substrate feeding rules by past experiences have great blindness to keep substrate concentration in a given appropriate range. This paper considers that alkali feed depends on pH value of the solution and glycerol feed depends on glycerol concentration of the solution in the uncoupled microbial fed-batch fermentation process, and establishes a state-dependent switched system in which the flow rates of glycerol and alkali, the number of mode switches, the mode sequence and the switching times are prior unknown. To maximize the yield of target product 1,3-Propanediol (1,3-PD), we formulate a switching optimal control problem with the flow rates of glycerol and alkali, the number of mode switches, the mode sequence and the switching times as decision variables, which is a mixed-integer dynamic programming problem. For solving the mixed-integer dynamic programming problem, the control parametrization technique, the time scaling transformation and the embedded system technology are used to obtain an approximate parameter optimization problem. By using a parallel optimization algorithm, we obtain the optimal control strategies. Under the obtained optimal control strategies, the 1,3-PD yield at the terminal time is increased significantly compared with the previous results.

Since both the parents need to work and look after their babies/infants simultaneously, the families bear more workload. Thus, this paper presents an innovative infant monitoring system consisting of an embedded system platform with a Linux kernel embedded operation system using the TCP/IP protocol, a CMOS image sensor, and peripheral control systems. The key feature of the system is that it can be used to monitor the living environment and the activities of the babies and/or infants through a web browser at any time from any place in the world. In order to increase the accuracy of the temperature sensor, the measured values from the digital temperature sensor were calibrated using regression analysis methods, resulting in an accuracy of ± 0.1°C, a correlation coefficient of 0.996, and a standard deviation of 0.124. The experimental results show that the proposed concept and the resulting system are feasible.