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Currency Recognition System Using Image processing 10

Currency Recognition System Using Image Processing

引用

10th international conference on Contemporary Computing (IC3)

作者： Abburu, Vedasamhitha Gupta, Saumya Rimitha, S. R. Mulimani, Manjunath Koolagudi, Shashidhar G. Natl Inst Technol Karnataka Dept Comp Sci & Engn Surathkal India

ISBN: (纸本)9781538630778

In this paper, we propose a system for automated currency recognition using image processing techniques. the proposed method can be used for recognizing both the country or origin as well as the denomination or value of a given banknote. Only paper currencies have been considered. this method works by first identifying the country of origin using certain predefined areas of interest, and then extracting the denomination value using characteristics such as size, color, or text on the note, depending on how much the notes within the same country differ. We have considered 20 of the most traded currencies, as well as their denominations. Our system is able to accurately and quickly identify test notes.

关键词： Image processing Feature extraction Aspect ratio Denomination Binary image Currency recognition

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De-Duplication Of Passports Using Aadhaar 7

De-Duplication Of Passports Using Aadhaar

引用

7th international conference on Computer Communication and Informatics (ICCCI)

作者： Prathilothamai, M. Nair, Priyanka Sunil Amrita Univ Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781467388566;9781467388559

Big data is an emerging technology that is becoming an essential part of national governance. Aadhaar is the unique identification scheme of India, handled by the Unique Identification Authority of India (UIDAI), which deals with big data. Every person above the age of 5 years has to register their demographic details (Name, Date of Birth, Address and Phone number) and biometric details (10 fingerprints and both iris) and then these details are used to verify the authenticity of the person when any services are required by him. Passport is a legal document that is carried by a person when he travels between countries, but in the case of the older passports with no biometric data, a person may have more than one legal passport with different demographic details. this paper does a case study on the existing de-duplication methods for passport enrolments and other such documents. In the case of newer passports, it takes 10 days to link with Aadhaar at the time of registration, hence the aim is to reduce the processing time of the linking and verification. String matching algorithms are used to compare the demographics, and techniques such as genetic programming and hashing are used for de-duplication. this case study also helps identify big and fast data platforms to identify such e-governance plans, by evaluating the accuracy and efficiency of existing algorithms. this system aims to predict the duplication of passports by linking Aadhaar and passport details, and to reduce the processing time of the Aadhaar database by using parallel algorithms.

关键词： UIDAI de-duplication Aadhaar passports big data biometrics graphics processing unit (GPU) image processing

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Biometric Analysis For the Recognition of Spider Species According to their Webs 10

Biometric Analysis For The Recognition of Spider Species Acc...

引用

10th international conference on Contemporary Computing (IC3)

作者： Batista-Plaza, David Travieso, Carlos M. Dutta, Malay Kishore Singh, Anushikha Univ Las Palmas Gran Canaria Signals & Commun Dept Las Palmas Gran Canaria Spain Amity Univ Dept Elect & Commun Engn Noida India

ISBN: (纸本)9781538630778

this work presents a biometric approach for spider identification based on transform domain and Support Vector Machines as classifier. the dataset is composed by 185 images of spider web. the goal of this work is to use the structure of spider web for identifying the kind of spider. the experiments were done using two different of segmentation blocks and the analysis of the whole and center of the spider web. the best accuracy is reached after to run the different combinations.

关键词： image processing image segmentation pattern recognition classification system

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Performance Analysis and Benchmarking of All-Spin Spiking Neural Networks

Performance Analysis and Benchmarking of All-Spin Spiking Ne...

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international Joint conference on Neural Networks (IJCNN)

作者： Sengupta, Abhronil Ankit, Aayush Roy, Kaushik Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781509061822

Spiking Neural Network based brain-inspired computing paradigms are becoming increasingly popular tools for various cognitive tasks. the sparse event-driven processing capability enabled by such networks can be potentially appealing for implementation of low-power neural computing platforms. However, the parallel and memory-intensive computations involved in such algorithms is in complete contrast to the sequential fetch, decode, execute cycles of conventional von-Neumann processors. Recent proposals have investigated the design of spintronic "in-memory" crossbar based computing architectures driving "spin neurons" that can potentially alleviate the memory-access bottleneck of CMOS based systems and simultaneously offer the prospect of low-power inner product computations. In this article, we perform a rigorous system-level simulation study of such All-Spin Spiking Neural Networks on a benchmark suite of 6 recognition problems ranging in network complexity from 10k-7.4M synapses and 195-9.2k neurons. System level simulations indicate that the proposed spintronic architecture can potentially achieve similar to 1292x energy efficiency and similar to 235x speedup on average over the benchmark suite in comparison to an optimized CMOS implementation at 45nm technology node.

关键词： Spiking Neural Network Spintronics Domain-Wall Motion All-Spin Crossbar architectures

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Optimization of the coherence function estimation for multi-core central processing unit 10

Optimization of the coherence function estimation for multi-...

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10th international conference on Mechanical Engineering, Automation and Control Systems (MEACS)

作者： Cheremnov, A. G. Faerman, V. A. Avramchuk, V. S. Tomsk Polytech Univ Tomsk Russia

the paper considers use of parallel processing on multi-core central processing unit for optimization of the coherence function evaluation arising in digital signal processing. Coherence function along with other methods of spectral analysis is commonly used for vibration diagnosis of rotating machinery and its particular nodes. An algorithm is given for the function evaluation for signals represented with digital samples. the algorithm is analyzed for its software implementation and computational problems. Optimization measures are described, including algorithmic, architecture and compiler optimization, their results are assessed for multi-core processors from different manufacturers. thus, speeding-up of the parallel execution with respect to sequential execution was studied and results are presented for Intel Core i7-4720HQ. AMD FX-9590 processors. the results show comparatively high efficiency of the optimization measures taken. In particular, acceleration indicators and average CPU utilization have been significantly improved, showing high degree of parallelism of the constructed calculating functions. the developed software underwent state registration and will be used as a part of a software and hardware solution for rotating machinery fault diagnosis and pipeline leak location with acoustic correlation method.

关键词： Program processors

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A Novel Measurement processing Approach to the parallel Expectation Propagation Unscented Kalman Filter 20

A Novel Measurement Processing Approach to the Parallel Expe...

引用

20th international conference on Information Fusion (Fusion)

作者： De Freitas, Allan Fritsche, Carsten Mihaylova, Lyudmila Gunnarsson, Fredrik Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England Linkoping Univ Dept Elect Engn Linkoping Sweden Ericsson Res Linkoping Sweden

ISBN: (纸本)9780996452700

Advances in sensor systems have resulted in the availability of high resolution sensors, capable of generating massive amounts of data. For complex systems to run online, the primary focus is on computationally efficient filters for the estimation of latent states related to the data. In this paper a novel method for efficient state estimation with the unscented Kalman Filter is proposed. the focus is on applications consisting of a massive amount of data. From a modelling perspective, this amounts to a measurement vector with dimensionality significantly greater than the dimensionality of the state vector. the efficiency of the filter is derived from a parallel filter structure which is enabled by the expectation propagation algorithm. A novel parallel measurement processing expectation propagation unscented Kalman filter is developed. the primary advantage of the novel algorithm is in the ability to achieve computational improvements with negligible loses in filter accuracy. An example of robot localization with a high resolution laser rangefinder sensor is presented. A 47.53% decrease in computational time was exhibited for a scenario with a processing platform consisting of 4 processors, with a negligible loss in accuracy.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks Signal processing algorithms

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Design of people counting system using MATLAB 10

Design of people counting system using MATLAB

引用

10th international conference on Contemporary Computing (IC3)

作者： Saxena, Surbhi Songara, Dalpat GWECA Dept CSE Ajmer India

ISBN: (纸本)9781538630778

Automatic counting of people, entering or exiting a region of interest, is very important for both business and security applications. To properly manage security in various places, it is of great importance to introduce video surveillance. this paper introduces an automatic people counting system which can count multiple people who interact in the region of interest, by using only one camera. the algorithm uses Viola Jones method of facial recognition to detect people. Using a single overhead mounted camera, the system counts the number of people going in to an observed area. Counting is performed by analyzing the image to detect faces. We have tested the performance of the system, achieving a correct people counting rate of 85%.

关键词： people counting video processing video segmentation

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An Enhanced parallel Version of RSA Public Key Crypto Based Algorithm Using OpenMP 17

An Enhanced Parallel Version of RSA Public Key Crypto Based ...

引用

10th international conference on Security of Information and Networks (SIN)

作者： Saxena, Rahul Jain, Monika Singh, Dushyant Kushwah, Ashutosh Manipal Univ Jaipur Jaipur Rajasthan India

ISBN: (纸本)9781450353038

Due to increased data movement and information exchange over internet and web, preserving data confidentiality and security has emerged as a prime concern for the end users. From bank transactions to document verification portals, from government official websites to social media, all these platforms share data remotely over web which contains certain confidential information and thus uses security mechanisms based on cryptographic algorithm to preserve the confidentiality of data preventing any information leakage or breach. RSA being the most popular public key cryptography based algorithm uses data encryption and decryption technique. It uses the mathematical concept of factorization of prime integers. the efficiency of the algorithm in terms of enhancing the security aspect to guess the factors of prime numbers increase when the numbers have high powers. Computing prime factors of these large numbers is compute intensive task where the serial programming makes RSA algorithm to slow down. the paper here presents an OpenMP based algorithmic modification to the code of RSA algorithm to improve the execution time using the parallel processing power of modern day multi-core architecture based machines. Experimental results under the lights of graphical representation shows a considerable speed up gained over the traditional implementation. Further the paper also compares the efficiency with other parallel implementations of RSA algorithm where the reported algorithm is found to perform almost equivalent to the GPGPU based implementation without the need of extra hardware in the form of graphics card and with reduced power consumption of the machine. the paper sums up with a conclusion that GPU based acceleration can be clubbed with proposed method to generate factors severely large prime numbers making it highly difficult to guess the factors.

关键词： Cryptography RSA public key encryption decryption multi-core OpenMP

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ENVISION: A 0.26-to-10TOPS/W Subword-parallel Dynamic-Voltage-Accuracy-Frequency-Scalable Convolutional Neural Network Processor in 28nm FDSOI 64

ENVISION: A 0.26-to-10TOPS/W Subword-Parallel Dynamic-Voltag...

引用

64th IEEE international Solid-State Circuits conference (ISSCC)

作者： Moons, Bert Uytterhoeven, Roel Dehaene, Wim Verhelst, Marian Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9781509037582

ConvNets, or Convolutional Neural Networks (CNN), are state-of-the-art classification algorithms, achieving near-human performance in visual recognition [1]. New trends such as augmented reality demand always-on visual processing in wearable devices. Yet, advanced ConvNets achieving high recognition rates are too expensive in terms of energy as they require substantial data movement and billions of convolution computations. Today, state-of-the-art mobile GPU's and ConvNet accelerator ASICs [2][3] only demonstrate energy-efficiencies of 10's to several 100's GOPS/W, which is one order of magnitude below requirements for always-on applications. this paper introduces the concept of hierarchical recognition processing, combined with the Envision platform: an energy-scalable ConvNet processor achieving efficiencies up to 10TOPS/W, while maintaining recognition rate and throughput. Envision hereby enables always-on visual recognition in wearable devices. © 2017 IEEE.

关键词： Convolution

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the Risk Assessment of Breast Tumors Based on Text Data of Ultrasound Reports 15

The Risk Assessment of Breast Tumors Based on Text Data of U...

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15th IEEE international Symposium on parallel and Distributed processing with Applications (ISPA) / 16th IEEE international conference on Ubiquitous Computing and Communications (IUCC)

作者： Pan, Qiao Xiang, Lan Yu, Chunru Zhang, Yuanyuan Chen, Dehua Gao, Gaonian Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China Triman Informat & Technol Co Ltd Shanghai Peoples R China

ISBN: (纸本)9781538637906

the breast tumor has become a common and prevalent disease. If it can be found timely, and treated early, the life risk of patients will be reduced, and the survival rate will be improved. the accurate assessment of the breast tumor, benign or malignant, is the primary key to cure the disease in practical clinical diagnosis. the paper proposes a risk assessment method of breast tumors based on text data of ultrasound reports, using practical ultrasound reports of patients with breast tumors from a 3A grade hospital in Shanghai, and focuses on an innovative point of depending on reinforcement learning to extract index values from text data of ultrasound reports. First, this method chooses normative text data which index values are complete as external files, iterates to compute the reward function of the reinforcement learning algorithm with index parameters, and associate external files to dynamically update the corresponding index values to ultimately form the structured data. And then, this method inputs the structured data into three models based on machine learning algorithms-Support Vector Machine, K-Nearest Neighbor, and Random Forest-to assess the risk, benign or malignant, of the breast tumor. the experiments show the accuracy of every classification algorithm increases by nearly 10% via the method based on reinforcement learning, comparing with the common structured approach relying on dependency parsing, which reaches up to 84% in practical text data of ultrasound reports. Because of this method, manual workload can drop a lot, doctors can improve the accuracy of diagnosis and decrease misdiagnosis and missed diagnosis, and patients can reduce unnecessary surgical pain.

关键词： breast tumors risk assessment reinforcement learning text structure Support Vector Machine K-Nearest Neighbor Random Forest

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