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检索条件"机构=Computer System Design and Engineering"

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Teaching digital communications in a wireless world: Who needs equations?

Teaching digital communications in a wireless world: Who nee...

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113th Annual ASEE Conference and Exposition, 2006

作者： Silage, Dennis Temple University United States Department of Electrical and Computer Engineering Temple University United States IEEE United States System Chip Design Center United States Temple University Amateur Radio Club United States Middle Atlantic Section ASEE United States ECE Division ASEE United States

Digital communication is traditionally taught by examining the temporal and spectral response and the bit error rate performance of a system in the presence of additive noise as only a set of analytical equations. This approach seems to provide little insight or motivation for the undergraduate student. Undergraduate courses in digital signal and image processing extensively utilize simulation as an adjunct to understanding, but digital communications seems to be a laggard. An undergraduate curriculum in digital communications has been developed that couples the traditional analytical approach with the simulation of the system for further design, analysis, insight and motivation. © American Society for engineering Education, 2006.

关键词： Digital communication systems

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A Reinforcement Learning Framework for Medical Image Segmentation

A Reinforcement Learning Framework for Medical Image Segment...

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International Joint Conference on Neural Networks (IJCNN)

作者： F. Sahba H.R. Tizhoosh M.M.A. Salama Pattern Analysis and Machine Intelligence Laboratory Department of System Design Engineering University of Waterloo Waterloo ONT Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo ONT Canada

This paper introduces a new method to medical image segmentation using a reinforcement learning scheme. We use this novel idea as an effective way to optimally find the appropriate local thresholding and structuring element values and segment the prostate in ultrasound images. Reinforcement learning agent uses an ultrasound image and its manually segmented version and takes some actions (i.e., different thresholding and structuring element values) to change the environment (the quality of segmented image). The agent is provided with a scalar reinforcement signal determined objectively. The agent uses these objective reward/punishment to explore/exploit the solution space. The values obtained using this way can be used as valuable knowledge to fill a Q-matrix. The reinforcement learning agent can use this knowledge for similar ultrasound images as well. The results demonstrate high potential for applying reinforcement learning in the field of medical image segmentation.

关键词： Learning Biomedical imaging Image segmentation Ultrasonic imaging Medical diagnostic imaging Pattern analysis Machine intelligence Laboratories system analysis and design design engineering

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An efficient hybrid scheduling scheme for impatience user in eMBMS over LTE

An efficient hybrid scheduling scheme for impatience user in...

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International Conference on computer Communication and Informatics (ICCCI)

作者： Rochak Sachan Navrati Saxena Abhishek Roy Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea System Design Laboratory Telecom Systems Division Samsung Electronics Suwon South Korea

ISBN: (纸本)9781467329064

The 3 rd Generation Partnership Project - Long Term Evolution (3GPP-LTE) is the best choice for multimedia data transmission in demanding situation. The 3GPP LTE provides high data transmission rate between the User Equipment (UE) and Evolved Node B (eNB) with lower latency and better throughput. However, with the advancement and increasing traffic of data load, it is necessary to have data scheduling with user requests. In real life situations, mobile users become impatient when they don't receive their desired data. Moreover, the LTE Enhanced Multimedia Broadcast Multicast Services (eMBMS) does not provide any type of scheduling to measure and manage the impatient user request in multimedia broadcasting scenario. So, we consider this situation in our proposed scheme and simulated a new type of hybrid scheduling scheme to overcome this problem. It also takes care of the server anomalies arising from multiple requests sent by the same user for the same data.

关键词： Optimal scheduling Multimedia communication Quality of service Data communication computers Servers Informatics

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Synthesizing NPR Styled Street View Animation Based on Deep Learning

Synthesizing NPR Styled Street View Animation Based on Deep ...

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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Lieu-Hen Chen Cheng-Yu Sun Yasufumi Takama Department of Computer Science and Information Engineering National Chi Nan University Puli Taiwan Graduate school of System Design Tokyo Metropolitan University Tokyo Japan

More and more serious natural disasters happen nowadays. The inheritance of memories across generations consequently faces bigger challenges, since the traditional devices of saving memories such as diary, album, video tape, and even computer may be permanently damaged or lost during the disasters. Meanwhile, both of the software and hardware technologies keep advancing day by day. Thousands of hometown images have been saved on the Cloud by many internet services, such as Google Map and Google Street View. This gave us a new way to share our precious memories with our families and friends through the internet. However, these web services cannot do everything as we expected. For example, Google Street View images cannot show people under various weather conditions like rainy day or starry night. Therefore, we developed a street navigation system based on the image resources of Google Street View in this paper. By applying different visual effects on different regions, which are image segmented, based on deep learning technologies, this system creates vivid NPR (Non-Photorealistic Rendering) animations. In this way, we can create interesting weather effects and retouch the street images with more richness and variety.

关键词： Image color analysis Deep learning Google Image segmentation Navigation Rendering (computer graphics) Object detection

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A new neural network structure with cooperative modules

A new neural network structure with cooperative modules

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International Joint Conference on Neural Networks (IJCNN)

作者： G. Auda M. Kamel H. Raafat Computer Science Department University of Regina Regina SAS Canada PAMI Research Group System Design Engineering Department University of Waterloo Waterloo ONT Canada

A new neural network structure is proposed in order to solve complex (large) classification problems. At first, the problem is divided into a number of simpler sub-problems using an unsupervised network. Then, each sub-problem is solved using one module of the proposed network. The new addition to this traditional "divide-and-conquer" structure is a voting layer above the outputs of the modules. In this layer, the modules cooperate in deciding the final decision by training every module on some additional classes: each class represents a group of classes of another module. During testing, the average "vote" of the modules for each other is calculated and multiplied by the classes output. This scheme gave higher accuracy and faster learning speed than the backpropagation scheme. A recursive hierarchy of this network can be built-up for more enhancement to the performance.< >

关键词： Neural networks Voting Testing Backpropagation computer science design engineering systems engineering and theory Large-scale systems Councils Pipelines

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Walking Route Recommendation system for Taking a Walk as Health Promotion

Walking Route Recommendation System for Taking a Walk as Hea...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Yasufumi Takama Wataru Sasaki Takafumi Okumura Chi-Chih Yu Lieu-Hen Chen Hiroshi Ishikawa Graduate School of System Design Tokyo Metropolitan University Tokyo Japan Department of Computer Science and Information Engineering National Chi Nan University Nantou Taiwan

This paper proposes a walking route recommendation system aiming at continuously supporting a user to take a walk as means for health promotion. In recent years, taking a walk becomes popular with not only the elderly, but also those from all ages as one of the easiest ways for health promotion. The proposed system supports users to take a walk as daily exercise by recommending a walking route that can consume target calories. When it is going to be used for long period of time, it should consider the case when a user will not follow the recommended route. We think it will often happen when a user gradually gets bored with taking a walk during a long period of time. In order to solve the problem, the proposed method implicitly manages calories on monthly basis and recommends walking routes that could keep a user from getting bored. The Effectiveness of the recommendation algorithm is evaluated with agent simulation. This paper also proposes a navigation interface that presents direction to the next visiting point without using a map. It is evaluated by an experiment with test participants.

关键词： Navigation Legged locomotion Roads Resource description framework Vibrations Safety Databases

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Agile hardware development with rapid hardware definition language

Agile hardware development with rapid hardware definition la...

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IEEE International Conference on Electro-Information Technology

作者： Jacob N. Allen Hoda S. Abdel-Aty-Zohdy Robert L. Ewing Microelectronics System Design Laboratory Department of Electrical and Computer Engineering Oakland University Rochester Hills MI USA Air Force Research Laboratory OH USA

Rapid HDL is a hardware definition language developed in C# for the Microsoft .Net platform. Rapid HDL unifies digital FPGA hardware development and co-software integration. Hardware is scripted using reusable software objects, communication between hardware and software is automatic, and synthesis is automated using a free tool chain. Rapid HDL represents 18,000 lines of code and is freely available under the MIT license.

关键词： Hardware design languages Software libraries Testing Licenses Microelectronics Force sensors Field programmable gate arrays Software reusability Software tools High level languages

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Prediction Analysis of Greeting Gestures based on Data Augmentation and LSTM

Prediction Analysis of Greeting Gestures based on Data Augme...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Angga Wahyu Wibow Eri Sato-Shimokawara Kurnianingsih Department of Computer Science Graduate School of System Design Tokyo Metropolitan University Tokyo Japan Department of Electrical Engineering Politeknik Negeri Semarang Semarang Indonesia

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

The field of Human Activity Recognition (HAR) is growing significantly in several areas but little research focuses on cultural behavior. How machine learning can explain human activity as a promotional tool in understanding cultural differences in a region is very challenging. Studies using Recurrent Neural Network (RNN), especially Long Short-Term Memory (LSTM), require large data when used to research human activity recognition. Using small data is a challenge when using LSTM. This study aims to determine which data augmentation is most appropriate when used using LSTM to predict Japanese greeting gestures, namely eshaku, keirei, saikeirei, waving hands, and Indonesian greeting gestures, namely the movement of bringing both hands together in front of the chest with a slight bow of the head. This study proposes to compare several data augmentations, namely jittering, scaling, permutation, cropping, reverse, time shifting, frequency domain, and synthetic data generation. We evaluate model performance using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R 2 ) metrics. The experimental results show that Time Shifting has the best MSE, RMSE, and MAE values compared to others and Frequency Domain has the best R 2 values compared to others. This study concludes that Time Shifting and Frequency Domain are the best data augmentation methods when using LSTM in predicting greeting gestures using small data. These findings provide valuable insights into the effectiveness of various data augmentation methods in motion prediction tasks with limited data sets, as well as the importance of selecting appropriate data augmentation methods in human motion analysis. Future work could use data from two or more people using model explanations generated by machine learning and developing a new method for cultural behavior.

关键词： Hands Recurrent neural networks Frequency-domain analysis Machine learning Data augmentation Human activity recognition Cultural differences Motion analysis Long short term memory Synthetic data

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Interpretable Disease Prediction from Clinical Text by Leveraging Pattern Disentanglement

Interpretable Disease Prediction from Clinical Text by Lever...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Malikeh Ehghaghi Pei-Yuan Zhou Wendy Yusi Cheng Sahar Rajabi Chih-Hao Kuo En-Shiun Annie Lee Computer Science Department University of Toronto Toronto ON Canada System Design Engineering University of Waterloo Waterloo ON Canada Electrical and Computer Engineering Department University of Tehran Tehran Iran Computer Science Department Ontario Tech University University of Toronto Toronto ON Canada

For artificial intelligence (AI) systems to be adopted in high stake human-oriented applications, they must be able to make complex decisions in an understandable and interpretable manner. While AI systems today have grown leaps and bounds in predictive power using larger datasets with more complex architectures, existing models remain ineffective at generating interpretable insights in the clinical setting. In this paper, we address the challenge of discovering interpretable insights from the clinical text for disease prediction. For this purpose, we apply the clinical notes from the electronic health records (EHR) available in the Medical Information Mart of Intensive Care III (MIMIC-III) dataset, which are labeled with the international classification of diseases (ICD9) codes. Our proposed algorithm combines interpretable text-based features with a novel pattern discovery and disentanglement algorithm. Specifically, our approach encompasses the following: (1) uncovering strong association patterns between clinical notes and diseases, (2) surpassing baseline clustering algorithms in effectively distinguishing between disease clusters, and (3) demonstrating comparable performance to baseline supervised methods in predicting diseases. Our results validate the model’s capability to strike a balance between interpretability and outcome prediction accuracy. By unveiling insightful patterns between clinical notes and diseases, our approach upholds a reasonable level of diagnostic *** relevance—This paper proposes a novel all-in-one clinical Natural Language Processing (NLP) knowledge base, which can be applied in healthcare systems to discover interpretable insights from the clinical text for predicting medical conditions.

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Cognitive processing using spiking neural networks

Cognitive processing using spiking neural networks

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IEEE National Conference on Aerospace and Electronics (NAECON)

ISBN: (纸本)9781424444946

Powerful parallel cognitive processors can be developed by studying biologically plausible models of cognitive systems in animals and extrapolating key principles to be adapted for implementation in digital computer architectures. The network described here uses basic statistical methods such as proportion sampling on a massively parallel scale to create a general purpose pattern classifier. From these principles, we can achieve auto association and self organization that provides fundamental cognitive processing. Signal preprocessing is essential to transform the signal into a scale and rotation invariant binary pattern. The network avoids the curse of dimensionality by filtering out irrelevant inputs, allowing us to combine large sensor input vectors from multiple sources. Recent hardware designs define the network structure and state in memory, and then use accelerator processor cores to modify these memory structures in parallel.

关键词： Neural networks Neurons Biological system modeling Biology computing Biological information theory Brain modeling computer architecture Microelectronics Power engineering and energy Biosensors

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