A study on predicting students’ grades for ideological and political courses with decision tree generation rules

Cai and Selman [CS] proposed the following definition for measuring average computation time: A time function t is T on average over a distribution μ if, for all $ n \geq 1 $ , $ \sum_{|x|\geq n}T^{-1}(t(x))|x|^{-1}\mu (x) \leq W_n $ , where $ W_n = \mu (\{ x : |x| \geq n\} ) $ . This definition results in a modification of Levin's notion of average time [L]. The effect of the modification is to control the rate of convergence of the expressions that define average computation time. With this modification, they proved a hierarchy theorem for average-time complexity that is as tight as the Hartmanis—Stearns [HS] hierarchy theorem for worst-case deterministic time. They also proved that under a fairly reasonable condition on distributions, called condition W, a distributional problem is solvable in average polynomial time under the modification exactly when it is solvable in average polynomial time under Levin's definition. Various notions of reductions, as defined by Levin [L] and others, play a central role in the study of average-case complexity. However, the class of distributional problems that are solvable in average polynomial time under the modification is not closed under the standard reductions. In particular, we prove that there is a distributional problem that is not solvable in average polynomial time under the modification but is reducible, by the identity function, to a distributional problem that is, and whose distribution even satisfies condition W.

At this time, research topic about the nano scale particle dynamics simulation is an emerging research today. Some particle simulation using Euler integration method, but Euler method is the simplest integration method and used only for the smooth particle moving. For extreme abrupt motion of particle, it is necessary to study other integration methods. This paper discusses the computing comparison of Euler, Heun, fourth order Runge-Kutta and third order Adams-Bashforth-Moulton integration used in particle dynamics simulation. The program written in C++ programming languages using Microsoft Visual Studio 2010 compiler and run on Intel i7 processor. The results of computational simulations show that the Euler integration method is the fastest integration with average computation time at 0.055592 second. Whereas the slowest integration is Adams – Bashforth – Moulton with average computation time at 0.055869 second. Simulations also shows that Euler integration method is suitable for smooth motion of particle, whereas Adam-Bashforth-Moulton integration method is suitable for extreme abrupt motion of particle.

In this paper, a new approach for the realization of 1D discrete cosine transform (1D-DCT) is presented. This approach is based on a new formulation of an odd prime-length DCT which uses two half-length cyclic convolutions with the same form which can be computed in parallel. Using this approach, a new efficient systolic array with outstanding performance in structural regularity, hardware cost of the PE's, average computation time, and I/O cost has been obtained, which is well suited for VLSI realization. The average computation time has been reduced to one half and the throughput has been doubled, as compared with a previous design. It possesses also a much lower control complexity, a simpler interconnection structure, and a simpler hardware structure of the PE's, having thus a shorter cycle time. Moreover, it owns all other outstanding features of the systolic array previously proposed.

In this paper, what we believe to be, a new combined algorithm of artificial bee colony and slime mould algorithm (CABCSMA) and a differential evolution (DE) algorithm using target-to-best variation strategy are proposed to process the data based on Planck's radiation law and the mathematical model of reference temperature. The material model with 6 different emissivity trends is simulated. Simulation results show that the average relative error of CABCSMA algorithm is less than 0.68%, and the average calculation time is 0.44s. The average relative error of DE algorithm is less than 0.43%, and the average calculation time is only 0.06s. The two algorithms were applied to the temperature test of silicon carbide sample, tungsten material and rocket engine nozzle. The experimental results show that the relative error of silicon carbide experimental temperature is less than 0.41% and 0.28%, and the relative error of tungsten material experimental temperature is less than 0.31% and 0.3%. The relative errors of rocket engine nozzle temperature experiments are within 0.68% and 0.52%, respectively. The results show that these two algorithms are expected to be applied in practical measurement scenarios.

This paper proposes to further design the sub-optimal two-dimensional (2D) interleavers while taking the best interleaver from the previous work into consideration. The newly proposed interleavers are 2D Golden interleavers (Gold), uniformly distributed interleavers (Uniform) and Block and bit wise interleavers (Block & bit). The performances of these interleavers were compared and evaluated by comparing the average (Mean) and standard deviation (SD) of the bit error rate (BER) under the environments with different amounts of burst errors. Moreover, the average computation time (Time) of the interleaver and average distance between adjacent bits after deinterleaving (Dist) were also utilized as the criteria for performance evaluation. Time could also be used to analyze the computational complexity of the interleavers where Time of the proposed 2D interleaver was compared with Time of original 1D block interleaver to test the suitability for hardware accomplishment. The transmission scheme for 2D system is based on previously proposed 2D linear block code and 2D syndrome decoder. The performances of these 2D interleavers were compared with traditional one-dimensional (1D) interleavers with comparably the same complexity. The buffer sizes for 2D channel that were considered in this paper are 256*256, 128*128, 64*64, 32*32 and 16*16 which is equivalent to 65536, 16384, 4096, 1024 and 256 buffer sizes for 1D respectively as they have the same number of elements. Simulation results suggest that 2D interleavers have relatively lower computation time than their 1D equivalent interleavers and reducing the buffer size can dramatically increase the BER. The optimal buffer sizes for 2D interleavers are 256*256 and 128*128. For low error rate error, the best interleaver would be Uniform and Gold for 256*256 and 128*128 buffer size respectively. For the other environment, the finest ones are Block&bit and Uniform for 256*256 and 128*128 buffer size respectively. Comparison results between the computation time of the proposed 2D interleavers and 1D block interleaver also indicate that 2D interleavers are suitable for hardware implementation since they have less computational complexity.

This work investigates the machining temperatures of ultra-fine-grained titanium (UFG Ti), prepared by equal channel angular extrusion, through analytical modeling. UFG Ti has great usefulness in biomedical applications because of its high mechanical strength, sufficient manufacturability, and high biocompatibility. The temperatures were predicted using a physics-based predictive model based on material constitutive relation and mechanics of the orthogonal cutting process. The minimization between the stress calculated using Johnson–Cook constitutive model and the same stress calculated using mechanics model yields the estimation of machining temperatures at two deformation zones. Good agreements are observed upon validation to the values reported in the literature. The machinability of UFG Ti is investigated by comparing its machining temperature to that of Ti–6Al–4V alloy under the same cutting conditions. Significantly lower temperatures are observed in machining UFG Ti. The computational efficiency of the presented model is investigated by comparing its average computational time (~ 0.5 s) to that of a widely used modified chip formation model (8900 s) with comparable prediction accuracy. This work extends the applicability of the presented temperature model to a broader class of materials, specifically ultra-fine-grained metals. The high computational efficiency allows the in situ temperature prediction and optimization of temperature condition with process parameters planning.

This work presents a novel hardware implementation of a levelset algorithm for carotid lumen segmentation in computed tomography. We propose to use a field programmable gate array (FPGA) to iteratively solve the underlying finite difference scheme. A FPGA processor can be programmed to have a dedicated hardware architecture including specific data path and processor core design with different types of parallelizations which is fully tailored and optimized toward its application. The method has been applied to ten carotid bifurcation of six stroke patients and the results have been compared to the results obtained from the same method implemented in C++. Visual inspections revealed similar segmentation results. The average computation time in software was 1663 ± 86 seconds, the computation time on the FPGA processor was 28 seconds yielding approximately a 60-fold speed-up which to our knowledge has been unmmatched before for this class of algorithms.

PurposeThis paper aims to present an iterative path-following method with joint limits to solve the problem of large computation cost, movement exceeding joint limits and poor path-following accuracy for the path planning of hyper-redundant snake-like manipulator.Design/methodology/approachWhen a desired path is given, new configuration of the snake-like manipulator is obtained through a geometrical approach, then the joints are repositioned through iterations until all the rotation angles satisfy the imposed joint limits. Finally, a new arrangement is obtained through the analytic solution of the inverse kinematics of hyper-redundant manipulator. Finally, simulations and experiments are carried out to analyze the performance of the proposed path-following method.FindingsSimulation results show that the average computation time is 0.1 ms per step for a hyper-redundant manipulator with 12 degrees of freedom, and the deviation in tip position can be kept below 0.02 mm. Experiments show that all the rotation angles are within joint limits.Research limitations/implicationsCurrently , the manipulator is working in open-loop, the elasticity of the driving cable will cause positioning error. In future, close-loop control based on real-time attitude detection will be used in in combination with the path-following method to achieve high-precision trajectory tracking.Originality/valueThrough a series of iterative processes, the proposed method can make the manipulator approach the desired path as much as possible within the joint constraints with high precision and less computation time.

Fast Deformable Image Registration for Real-Time Target Tracking During Radiation Therapy Using Cine MRI and Deep Learning

Optic Disc (OD) localization is an important step in retinal image analysis especially for grading Diabetic retinopathy and Glaucoma. This paper describes a method to automatically localize the OD position in retinal fundus images. The method localizes the OD by combining intensity and vascular information. Initially, the vascular structure is extracted using 2 dimensional Gabor filtering followed by a simple thresholding technique. Then the initial OD candidates are selected using intensity information. The candidates’ satisfying area and density criterion are considered for the final stage. In the final stage, the entropy of vascular directions on the final candidates was calculated, and the candidate having maximum and optimum entropy was considered as OD region. The proposed method was evaluated on the two publicly available DRIVE and STARE databases. The method was able to obtain 100% of OD localization accuracy on DRIVE database with 2.2 seconds average computation time.

Video sharing sites have become keepers of de-facto digital libraries of sign language content, being used to store videos including the experiences, knowledge, and opinions of many in the deaf or hard of hearing community. Due to limitations of term-based search over metadata, these videos can be difficult to find, reducing their value to the community. Another result is that community members frequently engage in a push-style delivery of content (e.g., emailing or posting links to videos for others in the sign language community) rather than having access be based on the information needs of community members. In prior work, we have shown the potential to detect sign language content using features derived from the video content rather than relying on metadata. Our prior technique was developed with a focus on accuracy of results and are quite computationally expensive, making it unrealistic to apply them on a corpus the size of YouTube or other large video sharing sites. Here, we describe and examine the performance of optimizations that reduce the cost of face detection and the length of video segments processed. We show that optimizations can reduce the computation time required by 96%, while losing only 1% in F1 score. Further, a keyframe-based approach is examined that removes the need to process continuous video. This approach achieves comparable recall but lower precision than the above techniques. Merging the advantages of the optimizations, we also present a staged classifier, where the keyframe approach is used to reduce the number of non-sign language videos fully processed. An analysis of the staged classifier shows a further reduction in average computation time per video while achieving similar quality of results.

Surgical planning is crucial to Stereoelectroencephalography (SEEG), a minimally invasive procedure that requires clinicians to operate with no direct view of the brain. Decisions making involves different clinical specialties and requires analysis of multiple multimodal datasets. We present a DepthMap tool designed to localize, measure, and visualize surgical risk, and an AlternativeFinder tool, designed to search for alternative trajectories maintaining adherence to the initial trajectory with three different re-planning strategies: similar entry, similar target, or parallel trajectory. The two tools transform the 3D problem into the 2D domain using projective geometry and distance mapping. Both use the graphics processing unit (GPU) to create a 2D depth image used by DepthMap for measurement and visualization, and by AlternativeFinder to find alternative trajectories. Tools were tested with 12 SEEG cases using digital subtraction angiography. DepthMap was used to measure vessel distance. AlternativeFinder was then used to search for alternatives. Computation time and displacements of the entry and target points for each trajectory and adherence strategy were recorded. The DepthMap tool found vessels in 118 initial trajectories (out of 145). Ninety alternative trajectories were found to meet the required avascular constraints (average 820K alternatives evaluated per initial trajectory). The average computation time was 449 ms per initial trajectory (77 ms when alternatives were found). The tools presented helped clinicians examine and re-plan SEEG trajectories to avoid vascular risks using three adherence strategies. Quantitative measurement of the adherence shows the potential of this tool for clinical use.

This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs).
 Keywords: Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot.

The advances in biomedical imaging equipment have produced a massive amount of medical images that are generated by the different modalities. Consequently, a huge volume of data has been produced and caused a complex and time-consuming retrieving process of the relevant cases. To resolve this issue, the Content-Based Biomedical Image Retrieval (CBMIR) system is applied to retrieve the related images from the earlier patients’ databases. However, the previous handcrafted features methods that applied the CBMIR model have shown poor performance in many multimodal databases. In this paper, we focus on designing CBMIR technique using Deep Learning (DL) models. We present a new Multimodal Biomedical Image Retrieval and Classification (M-BMIRC) technique for retrieving and classifying the biomedical images from huge databases. The proposed M-BMIRC model involves three dissimilar processes as following: feature extraction, similarity measurement, and classification. It uses an ensemble of handcrafted features from Zernike Moments (ZM) and deep features from Deep Convolutional Neural Networks (DCNN) for feature extraction process. Additionally, the Hausdorff Distance based similarity measure is employed to identify the resemblance between the queried image and the images that exist in the database. Moreover, the classification process gets executed on the retrieval images using the Probabilistic Neural Network (PNN) model, which allocates the class labels of the tested images. Finally, the experimental studies are conducted using two benchmark medical datasets and the results ensure the superior performance of the proposed model in terms of different measures include Average Precision Rate (APR), Average Recall Rate (ARR), F-score, accuracy, and Computation Time (CT).

Automatic Optic Disc (OD) localization is an important problem in ophthalmic image processing. Knowing its location helps doctors with the early detection of preventable eye diseases. Inspired by a fast and accurate OD localization algorithm utilizing the vessel projection technique that is usually inefficient when the OD in the image is unusually pale, we employed the decision tree with 5 features to improve the accuracy of the existing algorithm. Also to overcome the problem of poor accuracy when the image is tilted, we repeatedly run this improved algorithm on a series of images tilted at different degree from the original image to obtain the voted location of the OD.