IMPROVED TIME-MULTIPLEXED FPGA ARCHITECTURE AND ALGORITHM FOR MINIMIZING COMMUNICATION COST DESIGNS

2013 ◽  
Vol 22 (05) ◽  
pp. 1350033
Author(s):  
CHI-CHOU KAO ◽  
YEN-TAI LAI
Keyword(s):  
Greedy Algorithm ◽  
Sequential Circuits ◽  
Precedence Constraints ◽  
Communication Cost ◽  
Experimental Results ◽  
Time Sharing ◽  
Fpga Architecture ◽  
The Given

The Time-Multiplexed FPGA (TMFPGA) architecture can improve dramatically logic utilization by time-sharing logic but it needs a large amount of registers among sub-circuits for partitioning the given sequential circuits. In this paper, we propose an improved TMFPGA architecture to simplify the precedence constraints so that the number of the registers among sub-circuits can be reduced for sequential circuits partitioning. To demonstrate the practicability of the architecture, we also present a greedy algorithm to minimize the maximum number of the registers. Experimental results demonstrate the effectives of the algorithm.

Experimental Investigations of the Machinability of Titanium Alloy TC4 with Different Hydrogen Contents

2011 ◽  
Vol 418-420 ◽  
pp. 1307-1311
Author(s):  
Jun Hu ◽  
Yong Jie Bao ◽  
Hang Gao ◽  
Ke Xin Wang
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Cutting Force ◽  
Hydrogen Content ◽  
Experimental Results ◽  
Cutting Conditions ◽  
Experimental Investigations ◽  
Cutting Condition ◽  
The Given

The experiments were carried out in the paper to investigate the effect of adding hydrogen in titanium alloy TC4 on its machinability. The hydrogen contents selected were 0, 0.25%, 0.49%, 0.63%, 0.89% and 1.32%, respectively. Experiments with varing hydrogen contents and cutting conditions concurrently. Experimental results showed that the cutting force of the titanium alloy can be obviously reduced and the surface roughness can be improved by adding appropriate hydrogen in the material. In the given cutting condition, the titanium alloy TC4 with 0.49% hydrogen content showed better machinability.

scholarly journals Hierarchical Classification Based on Label Distribution Learning

2019 ◽  
Vol 33 ◽  
pp. 5533-5540 ◽  
Author(s):  
Changdong Xu ◽  
Xin Geng
Keyword(s):  
Hierarchical Classification ◽  
Experimental Results ◽  
Challenging Problem ◽  
Training Set ◽  
True Label ◽  
Major Bottleneck ◽  
Novel Method ◽  
Class Labels ◽  
Label Distribution ◽  
The Given

Hierarchical classification is a challenging problem where the class labels are organized in a predefined hierarchy. One primary challenge in hierarchical classification is the small training set issue of the local module. The local classifiers in the previous hierarchical classification approaches are prone to over-fitting, which becomes a major bottleneck of hierarchical classification. Fortunately, the labels in the local module are correlated, and the siblings of the true label can provide additional supervision information for the instance. This paper proposes a novel method to deal with the small training set issue. The key idea of the method is to represent the correlation among the labels by the label distribution. It generates a label distribution that contains the supervision information of each label for the given instance, and then learns a mapping from the instance to the label distribution. Experimental results on several hierarchical classification datasets show that our method significantly outperforms other state-of-theart hierarchical classification approaches.

Mining Allocating Patterns in Investment Portfolios

2009 ◽  
pp. 110-135
Author(s):  
Yanbo J. Wang ◽  
Xinwei Zheng ◽  
Frans Coenen
Keyword(s):  
Data Mining ◽  
Portfolio Management ◽  
Association Rule ◽  
Common Type ◽  
Experimental Results ◽  
Transaction Database ◽  
Investment Portfolios ◽  
The Given

An association rule (AR) is a common type of mined knowledge in data mining that describes an implicative co-occurring relationship between two sets of binary-valued transaction-database attributes, expressed in the form of an ? rule. A variation of ARs is the (WARs), which addresses the weighting issue in ARs. In this chapter, the authors introduce the concept of “one-sum” WAR and name such WARs as allocating patterns (ALPs). An algorithm is proposed to extract hidden and interesting ALPs from data. The authors further indicate that ALPs can be applied in portfolio management. Firstly by modelling a collection of investment portfolios as a one-sum weighted transaction- database that contains hidden ALPs. Secondly the authors show that ALPs, mined from the given portfolio-data, can be applied to guide future investment activities. The experimental results show good performance that demonstrates the effectiveness of using ALPs in the proposed application.

A Fuzzy Adaptive K-SVD Dictionary Algorithm for Face Recogntion

2013 ◽  
Vol 347-350 ◽  
pp. 3797-3803 ◽  
Author(s):  
Xiao Ning Song ◽  
Zi Liu
Keyword(s):  
Iterative Method ◽  
Fuzzy Set ◽  
Sparse Coding ◽  
Sparse Representations ◽  
Experimental Results ◽  
Sparse Signal ◽  
Signal Representations ◽  
The Given ◽  
Class Assignment ◽  
Fuzzy Adaptive

Sparse representations using overcomplete dictionaries has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. The K-SVD algorithm is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. However, the existing K-SVD algorithm is employed to dwell on the concept of a binary class assignment meaning that the multi-classes samples are assigned to the given classes definitely. The work proposed in this paper provides a novel fuzzy adaptive way to adapting dictionaries in order to achieve the fuzzy sparse signal representations, the update of the dictionary columns is combined with an update of the sparse representations by incorporated a new mechanism of fuzzy set, which is called fuzzy K-SVD. Experimental results conducted on the ORL and Yale face databases demonstrate the effectiveness of the proposed method.

scholarly journals Query-Driven Multi-Instance Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 4158-4165
Author(s):  
Yen-Chi Hsu ◽  
Cheng-Yao Hong ◽  
Ming-Sui Lee ◽  
Tyng-Luh Liu
Keyword(s):  
Experimental Results ◽  
Weighted Sum ◽  
Class Label ◽  
Action Classification ◽  
Video Clips ◽  
A New Technique ◽  
Class Labels ◽  
The Given ◽  
Network Component ◽  
Generalized Compatibility

We introduce a query-driven approach (qMIL) to multi-instance learning where the queries aim to uncover the class labels embodied in a given bag of instances. Specifically, it solves a multi-instance multi-label learning (MIML) problem with a more challenging setting than the conventional one. Each MIML bag in our formulation is annotated only with a binary label indicating whether the bag contains the instance of a certain class and the query is specified by the word2vec of a class label/name. To learn a deep-net model for qMIL, we construct a network component that achieves a generalized compatibility measure for query-visual co-embedding and yields proper instance attentions to the given query. The bag representation is then formed as the attention-weighted sum of the instances' weights, and passed to the classification layer at the end of the network. In addition, the qMIL formulation is flexible for extending the network to classify unseen class labels, leading to a new technique to solve the zero-shot MIML task through an iterative querying process. Experimental results on action classification over video clips and three MIML datasets from MNIST, CIFAR10 and Scene are provided to demonstrate the effectiveness of our method.

scholarly journals Arithmetic expressions optimisation using dual polarity property

2003 ◽  
Vol 1 (1) ◽  
pp. 71-80 ◽  
Author(s):  
Dragan Jankovic ◽  
Radomir Stankovic ◽  
Claudio Moraga
Keyword(s):  
Order Term ◽  
High Order ◽  
High Order Term ◽  
Experimental Results ◽  
Simple Relationship ◽  
Dual Polarity ◽  
The Given ◽  
Arithmetic Expressions

A method for optimisation of fixed polarity arithmetic expressions (FPAEs) based on dual polarity is proposed. The method exploits a simple relationship between two FPAEs for dual polarities. It starts from the zero polarity FPAE of the given function and calculates all FPAEs using the dual polarity route. Using one-bit check carries out conversion from one FPAE to another. Each term in an FPAE is processed by the proposed processing rule. Terms, which differ in a single position, can be substituted by a high order term (cube). Experimental results show efficiency of proposed method.

Numerical and Experimental Investigation of a Single-Axis Parallel Active Vibration Mount for Helicopter Seats Using Vibration Standards

2020 ◽  
Vol 142 (7) ◽  
Author(s):  
Amin Fereidooni ◽  
Silas Graham ◽  
Eric Chen ◽  
Viresh Wickramasinghe
Keyword(s):  
Vibration Isolation ◽  
Experimental Results ◽  
Least Mean Square ◽  
Axis Parallel ◽  
Active Vibration ◽  
Iso 2631 ◽  
The Given ◽  
Nonlinear Numerical Model ◽  
Good Agreement ◽  
Helicopter Vibration

Abstract This paper presents the experimental and numerical investigation of a single-axis replicate of a patented multi-axis active vibration isolation seat mount. Following the design of the multi-axis system, this single axis vibration isolation mount uses a flexible elastomer support placed in parallel with an electromagnetic actuator. This mount is designed to reduce the N/rev harmonic vibration of a helicopter using a filtered-X least mean square (FXLMS)-based controller. To improve the efficiency of the FXLMS controller for this application, the ISO-2631-1 Wk filter is added. Employing this modified controller, the experimental setup is tested using a payload mass representative of a 95th percentile pilot. The experimental results confirm the effectiveness of the proposed design in canceling the unwanted helicopter vibration, where the active mount effectively reduces the vibration representative of a Bell-412 helicopter by 69.37% (−10.28 dB, g-rms). In order to develop a better understanding of the problem, the system is also modeled from first principles in simulink. The comparison between the nonlinear numerical model and the experimental results demonstrates a good agreement between the two approaches. Moreover, it is shown that the addition of the ISO-2631-1 Wk filter improves the transient performance of the FXLMS controller for the given helicopter vibration profile.

scholarly journals DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling

2019 ◽  
Vol 33 ◽  
pp. 4031-4038
Author(s):  
Sunghwan Joo ◽  
Sungmin Cha ◽  
Taesup Moon
Keyword(s):  
Neural Network ◽  
Multiplicative Noise ◽  
Additive Noise ◽  
State Of The Art ◽  
Unbiased Estimate ◽  
Experimental Results ◽  
Fine Tuning ◽  
Noisy Image ◽  
Supervised Training ◽  
The Given

We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which is a recently proposed neural adaptive image denoiser. While the original NAIDE was designed for the additive noise case, we show that the same framework, i.e., adaptively learning a network for pixel-wise affine denoisers by minimizing an unbiased estimate of MSE, can be applied to the multiplicative noise case as well. Moreover, we derive a double-sided masked CNN architecture which can control the variance of the activation values in each layer and converge fast to high denoising performance during supervised training. In the experimental results, we show our DoPAMINE possesses high adaptivity via fine-tuning the network parameters based on the given noisy image and achieves significantly better despeckling results compared to SAR-DRN, a state-of-the-art CNN-based algorithm.

A RUN-BASED ONE-AND-A-HALF-SCAN CONNECTED-COMPONENT LABELING ALGORITHM

2010 ◽  
Vol 24 (04) ◽  
pp. 557-579 ◽  
Author(s):  
LIFENG HE ◽  
YUYAN CHAO ◽  
KENJI SUZUKI
Keyword(s):  
Binary Image ◽  
Experimental Results ◽  
Connected Components ◽  
Connected Component ◽  
Run Length ◽  
Highly Efficient ◽  
Connected Component Labeling ◽  
Labeling Algorithm ◽  
The Given

This paper presents a run- and label-equivalence-based one-and-a-half-scan algorithm for labeling connected components in a binary image. Major differences between our algorithm and conventional label-equivalence-based algorithms are: (1) all conventional label-equivalence-based algorithms scan all pixels in the given image at least twice, whereas our algorithm scans background pixels once and object pixels twice; (2) all conventional label-equivalence-based algorithms assign a provisional label to each object pixel in the first scan and relabel the pixel in the later scan(s), whereas our algorithm assigns a provisional label to each run in the first scan, and after resolving label equivalences between runs, by using the recorded run data, it assigns each object pixel a final label directly. That is, in our algorithm, relabeling of object pixels is not necessary any more. Experimental results demonstrated that our algorithm is highly efficient on images with many long runs and/or a small number of object pixels. Moreover, our algorithm is directly applicable to run-length-encoded images, and we can obtain contours of connected components efficiently.

scholarly journals Genetic Spot Optimization for Peak Power Estimation in Large VLSI Circuits

VLSI Design ◽  
2002 ◽  
Vol 15 (1) ◽  
pp. 407-416 ◽  
Author(s):  
Michael S. Hsiao
Keyword(s):  
Peak Power ◽  
Power Estimation ◽  
Sequential Circuits ◽  
Vlsi Circuits ◽  
Experimental Results ◽  
Switching Function ◽  
Circuit Delay ◽  
Optimization Heuristics ◽  
Multiple Switching ◽  
Output Capacitance

Estimating peak power involves optimization of the circuit's switching function. The switching of a given gate is not only dependent on the output capacitance of the node, but also heavily dependent on the gate delays in the circuit, since multiple switching events can result from uneven circuit delay paths in the circuit. Genetic spot expansion and optimization are proposed in this paper to estimate tight peak power bounds for large sequential circuits. The optimization spot shifts and expands dynamically based on the maximum power potential (MPP) of the nodes under optimization. Four genetic spot optimization heuristics are studied for sequential circuits. Experimental results showed an average of 70.7% tighter peak power bounds for large sequential benchmark circuits was achieved in short execution times.

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