Improved Tree Gradient Coding with Non-uniform Computation Load

2021 ◽  
Author(s):  
Ela Bhattacharya ◽  
Utsav Tiwari ◽  
Raj Shah ◽  
Anoop Thomas
Keyword(s):  
Computation Load

scholarly journals A New Efficient Filtering Model for GPS/SINS Ultratight Integration System

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chaochen Wang ◽  
Yuming Bo ◽  
Changhui Jiang
Keyword(s):  
Navigation System ◽  
The State ◽  
Integrated System ◽  
State Model ◽  
Integration System ◽  
Filter Model ◽  
Integration Mode ◽  
Measurement Vector ◽  
Computation Load ◽  
Vector Dimension

Global Positioning System (GPS) and strap-down inertial navigation system (SINS) are recognized as highly complementary and widely employed in the community. The GPS has the advantage of providing precise navigation solutions without divergence, but the GPS signals might be blocked and attenuated. The SINS is a totally self-contained navigation system which is hardly disturbed. The GPS/SINS integration system could utilize the advantages of both the GPS and SINS and provide more reliable navigation solutions. According to the data fusion strategies, the GPS/SINS integrated system could be divided into three different modes: loose, tight, and ultratight integration (LI, TI, and UTC). In the loose integration mode, position and velocity difference from the GPS and SINS are employed to compose measurement vector, in which the vector dimension has nothing to do with the amount of the available satellites. However, in the tight and ultratight modes, difference of pseudoranges and pseudorange rates from the GPS and SINS are employed to compose the measurement vector, in which the measurement vector dimension increases with the amount of available satellites. In addition, compared with the loose integration mode, clock bias and drift are included in the integration state model. The two characteristics magnify the computation load of the tight and ultratight modes. In this paper, a new efficient filter model was proposed and evaluated. Two schemes were included in this design for reducing the computation load. Firstly, a difference between pseudorange measurements was determined, by which clock bias and drift were excluded from the integration state model. This step reduced the dimension of the state vector. Secondly, the integration filter was divided into two subfilters: pseudorange subfilter and pseudorange rate subfilter. A federated filter was utilized to estimate the state errors optimally. In the second step, the two subfilters could run in parallel and the measurement vector was divided into two subvectors with lower dimension. A simulation implemented in MATLAB software was conducted to evaluate the performance of the new efficient integration method in UTC. The simulation results showed that the method could reduce the computation load with the navigation solutions almost unchanged.

Fractional-order Kalman filters for continuous-time fractional-order systems involving correlated and uncorrelated process and measurement noises

2018 ◽  
Vol 41 (7) ◽  
pp. 1933-1947 ◽  
Author(s):  
Fanghui Liu ◽  
Zhe Gao ◽  
Chao Yang ◽  
Ruicheng Ma
Keyword(s):  
Fractional Order ◽  
Continuous Time ◽  
Kalman Filters ◽  
Equation Model ◽  
Order System ◽  
Fractional Order Systems ◽  
Derivative Method ◽  
Measurement Noises ◽  
Computation Load ◽  
Average Derivative

This paper presents fractional-order Kalman filters using the fractional-order average derivative method for linear fractional-order systems involving process and measurement noises. By using the fractional-order average derivative method, a difference equation model is obtained by discretizing the investigated continuous-time fractional-order system, and the accuracy of state estimation is improved. Meanwhile, compared with the Tustin generating function, the fractional-order average derivative method proposed in this paper can reduce computation load and save calculation time. Two kinds of fractional-order Kalman filters are given, for the correlated and uncorrelated cases, in terms of the process and measurement noises for linear fractional-order systems, respectively. Finally, simulation results are illustrated to verify the effectiveness of the proposed Kalman filters using the fractional-order average derivative method.

Using convolutional neural network and long short time memory to automatically detect aneurysm on 2D DSA images (Preprint)

2021 ◽  
Author(s):  
JunHua Liao ◽  
LunXin Liu ◽  
HaiHan Duan ◽  
YunZhi Huang ◽  
LiangXue Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intracranial Aneurysm ◽  
Spatial Information ◽  
Diagnostic System ◽  
Two Stage ◽  
Regional Localization ◽  
Detection Stage ◽  
Aneurysm Detection ◽  
Sensitivity Specificity ◽  
Computation Load

BACKGROUND It is hard to distinguish cerebral aneurysm from overlap vessels based on the 2D DSA images, for its lack the spatial information. OBJECTIVE The aim of this study is to construct a deep learning diagnostic system to improve the ability of detecting the PCoA aneurysm on 2D-DSA images and validate the efficiency of deep learning diagnostic system in 2D-DSA aneurysm detecting. METHODS We proposed a two stage detecting system. First, we established the regional localization stage (RLS) to automatically locate specific detection region of raw 2D-DSA sequences. And then, in the intracranial aneurysm detection stage (IADS) ,we build three different frames, RetinaNet, RetinaNet+LSTM, Bi-input+RetinaNet+LSTM, to detect the aneurysms. Each of the frame had fivefold cross-validation scheme. The area under curve (AUC), the receiver operating characteristic (ROC) curve, and mean average precision (mAP) were used to validate the efficiency of different frames. The sensitivity, specificity and accuracy were used to identify the ability of different frames. RESULTS 255 patients with PCoA aneurysms and 20 patients without aneurysm were included in this study. The best results of AUC of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 0.95, 0.96, and 0.97, respectively. The sensitivity of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 81.65% (59.40% to 94.76%), 87.91% (64.24% to 98.27%), 84.50% (69.57% to 93.97%), respectively. The specificity of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 88.89% (66.73% to 98.41%), 88.12% (66.06% to 98.08%), and 88.50% (74.44% to 96.39%), respectively. The accuracy of the RetinaNet, RetinaNet+LSTM, and Bi-input+RetinaNet+LSTM were 92.71% (71.29% to 99.54%), 89.42% (68.13% to 98.49%), and 91.00% (77.63% to 97.72%), respectively. CONCLUSIONS Two stage aneurysm detecting system can reduce time cost and the computation load. According to our results, more spatial and temporal information can help improve the performance of the frames, so that Bi-input+RetinaNet+LSTM has the best performance compared to other frames. And our study can demonstrate that our system was feasible to assist doctor to detect intracranial aneurysm on 2D-DSA images.

scholarly journals Cryptosystem Identification Scheme Based on ASCII Code Statistics

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wenyu Zhang ◽  
Yaqun Zhao ◽  
Sijie Fan
Keyword(s):  
Language Processing ◽  
Block Cipher ◽  
Feature Vector ◽  
Identification Accuracy ◽  
Encrypted Data ◽  
Machine Learning Classifiers ◽  
Theoretical Significance ◽  
Ascii Code ◽  
Extraction Scheme ◽  
Computation Load

In the field of information security, block cipher is widely used in the protection of messages, and its safety naturally attracts people’s attention. The identification of the cryptosystem is the premise of encrypted data analysis. It belongs to the category of attack analysis in cryptanalysis and has important theoretical significance and application value. This paper focuses on the extraction of ciphertext features and the construction of cryptosystem identification classifiers. The main contents and innovations of this paper are as follows. Firstly, inspired by language processing, we propose the feature extraction scheme based on ASCII statistics of ciphertexts which decrease the dimension of data preprocessing. Secondly, on the basis of previous work, we increase the types of block ciphers to eight, encrypt plaintext of the same sizes as experimental objects, and recognize the cryptosystem. Thirdly, we use two machine learning classifiers to perform classification experiments including random forest and SVM. The experimental results show that our scheme can not only improve the identification accuracy of 8 typical block cipher algorithms but also shorten the experimental time and reduce the computation load by greatly minimizing the dimension of the feature vector. And the various evaluation indicators obtained by the scheme have been greatly improved compared with the existing published literature.

A Prolific Scheme for Load Balancing Relying on Task Completion Time

2018 ◽  
Vol 8 (3) ◽  
pp. 1741
Author(s):  
V. Anand ◽  
K. Anuradha
Keyword(s):  
Load Balancing ◽  
Uniform Distribution ◽  
Resource Utilization ◽  
Completion Time ◽  
Computation Time ◽  
Task Completion ◽  
The Internet ◽  
Task Completion Time ◽  
Network Load ◽  
Computation Load

In networks with lot of computation, load balancing gains increasing significance. To offer various resources, services and applications, the ultimate aim is to facilitate the sharing of services and resources on the network over the Internet. A key issue to be focused and addressed in networks with large amount of computation is load balancing. Load is the number of tasks‘t’ performed by a computation system. The load can be categorized as network load and CPU load. For an efficient load balancing strategy, the process of assigning the load between the nodes should enhance the resource utilization and minimize the computation time. This can be accomplished by a uniform distribution of load of to all the nodes. A Load balancing method should guarantee that, each node in a network performs almost equal amount of work pertinent to their capacity and availability of resources. Relying on task subtraction, this work has presented a pioneering algorithm termed as E-TS (Efficient-Task Subtraction). This algorithm has selected appropriate nodes for each task. The proposed algorithm has improved the utilization of computing resources and has preserved the neutrality in assigning the load to the nodes in the network.

Hybrid Powertrain Optimization With Real-Time Traffic Information

2013 ◽  
Author(s):  
Mohd Azrin Mohd Zulkefli ◽  
Jianfeng Zheng ◽  
Zongxuan Sun ◽  
Henry Liu
Keyword(s):  
Real Time ◽  
Computation Time ◽  
Lookup Table ◽  
Traffic Information ◽  
Hybrid Powertrain ◽  
Real Time Traffic ◽  
Necessary Conditions For Optimality ◽  
Online Implementation ◽  
Fuel Rate ◽  
Computation Load

Combining hybrid powertrain optimization with traffic information has been researched before, but tradeoffs between optimality, driving-cycle sensitivity and speed of calculation have not been cohesively addressed. Optimizing hybrid powertrain with traffic can be done through iterative methods such as Dynamic Programming (DP), Stochastic-DP and Model Predictive Control, but high computation load limits their online implementation. Equivalent Consumption Minimization Strategy (ECMS) and Adaptive-ECMS were proposed to minimize computation time, but unable to ensure real-time charge-sustaining-operation (CS) in transient traffic environment. Others show relationship between Pontryagin’s Minimum Principles (PMP) and ECMS, but iteratively solve the CS-operation problem offline. This paper proposes combining PMP’s necessary conditions for optimality, with sum-of State-Of-Charge-derivative for CS-operation. A lookup table is generated offline to interpolate linear mass-fuel-rate vs net-power-to-battery slopes to calculate the equivalence ratio for real-time implementation with predicted traffic data. Maximum fuel economy improvements of 7.2% over Rule-Based is achieved within a simulated traffic network.

scholarly journals An Improved Ambiguity-Free Method for Precise GNSS Positioning with Utilizing Single Frequency Receivers

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 856 ◽  
Author(s):  
Wenhao Yang ◽  
Yue Liu ◽  
Fanming Liu
Keyword(s):  
Global Optimum ◽  
Ambiguity Function ◽  
Single Frequency ◽  
Density Estimator ◽  
New Approach ◽  
Gnss Positioning ◽  
Cycle Slips ◽  
Computation Efficiency ◽  
Ambiguity Fixing ◽  
Computation Load

The solution of carrier phase ambiguity is essential for precise global navigation satellite system (GNSS) positioning. Methods of searching in the coordinate domain show their advantage over the methods based on ambiguity fixing, for example, immune to cycle slips, far fewer epochs taken for obtaining the precise solution. However, there are still some drawbacks via using the Ambiguity Function Method (AFM), such as low computation efficiency and the existence of a false global optimum. The false global optimum is a situation where the Least Square (LS) criterion achieves minimum in another place than the point of the actual position, which restricts the application of this method to single-frequency receivers. The numerical search approach derived in this paper is based on the Modified Ambiguity Function Approach (MAFA). It focuses on eliminating the false optimum solution and reducing the computation load by utilizing single-frequency receivers without solving the ambiguity fixing problem. An improved segmented simulated annealing method is used to decrease the computation load while the Kernel Density Estimator (KDE) method is used to filter out the false optimum candidates. Static experiments were carried out to evaluate the performance of the new approach. It is shown that a precise result can be obtained by handling two epochs of data with z coordinate fixed to the referenced value. Meanwhile, the new approach can achieve a millimeter level of position accuracy after dealing with nineteen epochs of observations data when searching in x , y , z domain. The new approach shows its robustness even if the search region is broad, and the prior position is several meters away from the referenced value.

scholarly journals Computational Techniques for Model Predictive Control of Large-Scale Systems with Continuous-Valued and Discrete-Valued Inputs

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Koichi Kobayashi ◽  
Kunihiko Hiraishi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Large Scale ◽  
Computational Techniques ◽  
Continuous Variables ◽  
Current Time ◽  
Large Scale Systems ◽  
Virtual Control ◽  
Online Computation ◽  
Computation Load

We propose computational techniques for model predictive control of large-scale systems with both continuous-valued control inputs and discrete-valued control inputs, which are a class of hybrid systems. In the proposed method, we introduce the notion of virtual control inputs, which are obtained by relaxing discrete-valued control inputs to continuous variables. In online computation, first, we find continuous-valued control inputs and virtual control inputs minimizing a cost function. Next, using the obtained virtual control inputs, only discrete-valued control inputs at the current time are computed in each subsystem. In addition, we also discuss the effect of quantization errors. Finally, the effectiveness of the proposed method is shown by a numerical example. The proposed method enables us to reduce and decentralize the computation load.

scholarly journals Algorithm Improvement of the Low-End GNSS/INS Systems for Land Vehicles Navigation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Quan Zhang ◽  
Xiaoji Niu ◽  
Hongping Zhang ◽  
Chuang Shi
Keyword(s):  
Quantitative Analysis ◽  
Real Time ◽  
Comprehensive Analysis ◽  
Special Significance ◽  
Land Vehicles ◽  
Time Performance ◽  
Simplified Algorithm ◽  
Navigation Accuracy ◽  
Mems Imu ◽  
Computation Load

Recent advances in MEMS IMUs give the potential to develop affordable low-end GNSS/INS systems for land vehicles navigation (LVN). To improve the performance of low-end GNSS/INS systems, we made detailed quantitative analysis to the computation terms of the INS navigation equation in regard to accuracy impacts and computation loads and then proposed a simplified INS algorithm and adjusted the corresponding Kalman filter of GPS/INS integration. Comprehensive analysis was made to get the quantitative impacts of each simplified term. Results of road test have shown that the degradation of the navigation accuracy caused by the algorithm simplification was much less than that caused by the sensors errors of the MEMS IMU. Meanwhile, the computation load could be reduced by 70% with the simplified algorithm, and the reduction can go further to reach nearly 95% by downsampling IMU data rate simultaneously. Therefore, it is feasible to simplify the INS algorithm without losing accuracy and get benefits of reducing the computation load, which can further enhance the real-time performance of the navigation. The work has special significance for the applications that have limited processor resource and request strict real-time response, such as a deeply coupled GPS/INS receiver.

Motion Planning of Tomato Harvesting Manipulators with 7 DOF

2010 ◽  
Vol 97-101 ◽  
pp. 2840-2844 ◽  
Author(s):  
Xi Feng Liang ◽  
Yong Wei Wang
Keyword(s):  
Motion Planning ◽  
Degrees Of Freedom ◽  
Numerical Solutions ◽  
Optimization Technique ◽  
Tracking Error ◽  
Absolute Error ◽  
Initial Position ◽  
End Effector ◽  
Mathematical Relationships ◽  
Computation Load

This work presents a motion planning approach for tomato harvesting manipulators with seven degrees of freedom (7 DOF) based on an optimization technique and alternative method. It is to find optimal joint perturbations during the path planning so that a manipulator reaches a goal from an initial position with high accuracy. The optimization model consists of the objective function defined by the tracking error and the representation of a set of mathematical relationships that describe the kinematic restrictions of the manipulator. In this method, only a forward kinematics is used and the complex mathematics in numerical solutions of an inverse kinematics is avoided to reduce the computation load. Simulation results show that the tomato harvesting manipulator can move the end-effector to the target from an initial position along a specified geometric trajectory in its workspace. Simultaneously, the joint displacements vary smoothly within their limits during tracking. The position absolute error, moving velocity and precision of the end-effector are 0.53mm, 0.18m/s and 3.75% respectively, which fulfill the requirements of tomato picking with well working efficiency.

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