This is a straightforward image classification study to create and compare classifiers (KNN, Neural Networks and Adaboost) that decide the correct orientation of a given image i.e. 0°,90°,180° or 270°
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This chapter deals with performance analysis of CUDA implementation of an image quality assessment
tool based on structural similarity index (SSI). Since it had been initial created at the University of Texas
in 2002, the Structural SIMilarity (SSIM) image assessment algorithm has become a valuable tool for
still image and video processing analysis. SSIM provided a big giant over MSE (Mean Square Error)
and PSNR (Peak Signal to Noise Ratio) techniques because it way more closely aligned with the results
that would have been obtained with subjective testing. For objective image analysis, this new technique
represents as significant advancement over SSIM as the advancement that SSIM provided over PSNR.
The method is computationally intensive and this poses issues in places wherever real time quality assessment
is desired. We tend to develop a CUDA implementation of this technique that offers a speedup
of approximately 30 X on Nvidia GTX275 and 80 X on C2050 over Intel single core processor.
Enhanced Human Computer Interaction using hand gesture analysis on GPUMahesh Khadatare
This poster represent very active research topic in human
computer interaction (HCI) as automatic hand gesture recognition
using nvidia GPU. In this work neural network based video gesture
are processed and recognize the finger counts. Due to real time
requirement algorithm need to optimize and computationally
efficient. We implemented the MATLAB code, it perform slow when
neural network processing started. Implementing them in a parallel
programming model such as GPU-CUDA would provide the
necessary gain in processing speed. Algorithmic result validation is
done using standard video data set and recognition rate is
calculated. A performance improvement of 15x speed is achieved
which is faster than Intel quad core processor.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
This chapter deals with performance analysis of CUDA implementation of an image quality assessment
tool based on structural similarity index (SSI). Since it had been initial created at the University of Texas
in 2002, the Structural SIMilarity (SSIM) image assessment algorithm has become a valuable tool for
still image and video processing analysis. SSIM provided a big giant over MSE (Mean Square Error)
and PSNR (Peak Signal to Noise Ratio) techniques because it way more closely aligned with the results
that would have been obtained with subjective testing. For objective image analysis, this new technique
represents as significant advancement over SSIM as the advancement that SSIM provided over PSNR.
The method is computationally intensive and this poses issues in places wherever real time quality assessment
is desired. We tend to develop a CUDA implementation of this technique that offers a speedup
of approximately 30 X on Nvidia GTX275 and 80 X on C2050 over Intel single core processor.
Enhanced Human Computer Interaction using hand gesture analysis on GPUMahesh Khadatare
This poster represent very active research topic in human
computer interaction (HCI) as automatic hand gesture recognition
using nvidia GPU. In this work neural network based video gesture
are processed and recognize the finger counts. Due to real time
requirement algorithm need to optimize and computationally
efficient. We implemented the MATLAB code, it perform slow when
neural network processing started. Implementing them in a parallel
programming model such as GPU-CUDA would provide the
necessary gain in processing speed. Algorithmic result validation is
done using standard video data set and recognition rate is
calculated. A performance improvement of 15x speed is achieved
which is faster than Intel quad core processor.
Removal of Gaussian noise on the image edges using the Prewitt operator and t...IOSR Journals
Abstract: Image edge detection algorithm is applied on images to remove Gaussian noise that is present in the
image during capturing or transmission using a method which combines Prewitt operator and threshold
function technique to do edge detection on the image. This method is better than a method which combines
Prewitt operator and mean filtering. In this paper, firstly use mean filtering to remove initially Gaussian noise,
then use Prewitt operator to do edge detection on the image, and finally applied a threshold function technique
with Prewitt operator.
Keywords: Gaussian noise, Prewitt operator, edge detection, threshold function
Human action recognition with kinect using a joint motion descriptorSoma Boubou
- We proposed a novel descriptor for motion of skeleton joints.
- Proposed descriptor proved to outperform the state-of-the-art descriptors such as HON4D and the one proposed by Chen et al 2013.
- Our proposed approached proved to be effective for periodic actions (e.g., Waving, Walking, Jogging, Side-Boxing, etc).
- Grouping was effective for actions with unique joints trajectories (e.g., Tennis serving, Side kicking , etc).
- Grouping joints into eight groups is always effective with actions of MSR3D dataset.
This project finds real objects dimensions from their images using OpenCV Java using two methods - Reference Method and Stereo Method.
Code for this project is available on my GitHub:
https://github.com/shyamabhuvanendran/VirtualRuler_CV
OVERVIEW AND APPLICATION OF ENABLING TECHNOLOGIES ORIENTED ON ENERGY ROUTING ...ijaia
Energy routers are recent topics of interest for scientific community working on alternative energy.
Enabling technologies supporting installation and monitoring energy efficiency in building are discussed in
this paper, by focusing the attention on innovative aspects and on approaches to predict risks and failures
conditions of energy router devices. Infrared (IR) Thermography and Augmented Reality (AR) are
indicated in this work as potential technologies for the installation testing and tools for predictive
maintenance of energy networks, while thermal simulation, image post-processing and data mining
improve the analysis of the prediction process. Image post- processing has been applied on thermal images
and for WiFi AR. Concerning data mining we applied k-Means and Artificial Neural Network –ANNobtaining
outputs based on measured data. The paper proposes some tools procedure and methods
supporting the Building Information Modeling- BIM- in smart grid applications. Finally we provide some
ISO standards matching with the enabling technologies by completing the overview of scenario
Massive Sensors Array for Precision Sensingoblu.io
More than a billion smartphones being sold annually and growing with CAGR of 16%, the smartphone industry has become a driving force in the development of ultralow-cost inertial sensors. Unfortunately, these ultra low-cost sensors do not yet meet the needs of more demanding applications like inertial navigation and biomedical motion tracking systems. However, by adapting a wisdom of the crowd’s thinking and design arrays consisting of hundreds of sensing elements, one can capitalize on the decreasing cost, size, and power-consumption of the sensors to construct virtual high-performance low-cost inertial sensors. Team at KTH, Sweden and WUSTL, USA share findings and challenges.
Nuclear Material Verification Based on MCNP and ISOCSTM Techniques for Safegu...IOSRJAP
Recently, Mathematical techniques such as Monte Carlo and ISOCSTM software are being increasingly employed in the absolute efficiency calibration of gamma ray detector. Monte Carlo simulations and Canberra ISOCSTM software bring the possibility to establish absolute efficiency curve for desired energy range based on numerical simulation, with use of known or guessed geometry and chemical composition, of measured item. Broad-energy germanium (BEGe) detector was employed to perform the NDA measurements to five standard reference nuclear material (NBS, SNM-969). MC calculations were performed to calculate some factors (attenuation, geometry and efficiency) which affect the uranium isotope mass estimation. 235U and 238U masses are calculated based on MCNPX modeling calibration and also upon spectra analysis using ISOCSTM Calibration Software. The obtained results from the two different efficiency calibration methods were compared with each other and with the declared value for each sample. The obtained results are in agreements with the declared values within the estimated relative accuracy (ranges between -2.81 to 1.83%). The obtained results indicate that the techniques could be applied for the purposes of NM verification and characterization where closely matching NM standards are not available.
Multi-core GPU – Fast parallel SAR image generationMahesh Khadatare
This poster presents a design for parallel processing of synthetic
aperture radar (SAR) data using multi-core Graphics Processing
Units (GP-GPUs). In this design a two-dimensional image is
reconstructed from received echo pulses and their corresponding
response values. Then the comparative performance of proposed
parallel algorithm implementation is tabulated using different set of
nvidia GPUs i.e. GT, Tesla and Fermi. Analyzing experimentation
results on image of various size 1,024 x 1,024 pixels to 4,096 x
4,096 pixels led us to the conclusion that that nvidia Fermi S2050 have
maximum performance throughput .
The frame camera is used by users of digital single-lens reflex cameras (DSLRs) as a shorthand for an image sensor format which is the same size as 35mm format (36 mm × 24 mm) film.
Panoramic imagery is created either by digitally stitching together multiple images from the same position (left/right, up/down) or by rotating a camera with conventional optics, and an area or line sensor.
A (very brief) Introduction to Image Processing and 3D Printing with ImageJPaul Mignone, Ph.D
Using ImageJ to extract 3D models from material image data-sets for 3D printing purposes. Participants will have an opportunity to 3D print a small model using the Objet Connex500 3D printer.
Current scientific imaging (and image processing) technologies allow researchers and professionals to visualize the morphology of complex composite materials at very fine length scales. Combined with the advances and accessibility of additive manufacturing technologies, researches are able to produce tangible assets of these composite morphologies for further research and analysis. The following workshop will briefly introduce image processing theory, as well as an open source tool chain to produce 3D models of a material data-set for 3D printing purposes.
Inertial Sensor Array Calibration Made Easy !oblu.io
Ultra-low-cost single-chip inertial measurement units (IMUs) combined into IMU arrays are opening up new possibilities for inertial sensing. However, to make these systems practical, calibration and misalignment compensation of low-cost IMU arrays are necessary and a simple calibration procedure that aligns the sensitivity axes of the sensors in the array is needed. Team at KTH suggests a novel mechanical-rotation-rig-free calibration procedure based on blind system identification and a platonic solid (Icosahedron) printable by a contemporary 3D-printer. Matlab-scripts for the parameter estimation and production files for the calibration device are made available.
An infrared thermometer is a thermometer which infers temperature from a portion of the thermal radiation sometimes called blackbody radiation emitted by the object being measured.
They are sometimes called laser thermometers as a laser is used to help aim the thermometer,
or non-contact thermometers or temperature guns, to describe the device's ability to measure temperature from a distance.
visit us at https://goo.gl/vwr40b to know more.
ADAPTIVE FILTER FOR DENOISING 3D DATA CAPTURED BY DEPTH SENSORSSoma Boubou
Current consumer depth sensors produce depth maps that are often noisy and lack sufficient detail. Enhancing the quality of the 3D depth data obtained from compact depth Kinect-like sensors is an increasingly popular research area. Although depth data is known to carry a signal-dependent noise, the state-of-the-art denoising methods tend to employ denoising techniques which are independent of the depth signal itself. In this paper, we present a novel adaptive denoising filter to enhance object recognition from 3D depth data. We evaluate the performance of our proposed denoising filter against other state-of-the-art filters based on the enhancement of object recognition accuracy achieved after denoising the raw data with each filter. In order to perform object recognition from depth data, we make use of Differential Histogram of Normal Vectors (DHONV) features along with a linear SVM. Experiments show that our proposed filter outperformed the state-of-the-art denoising methods.
Removal of Gaussian noise on the image edges using the Prewitt operator and t...IOSR Journals
Abstract: Image edge detection algorithm is applied on images to remove Gaussian noise that is present in the
image during capturing or transmission using a method which combines Prewitt operator and threshold
function technique to do edge detection on the image. This method is better than a method which combines
Prewitt operator and mean filtering. In this paper, firstly use mean filtering to remove initially Gaussian noise,
then use Prewitt operator to do edge detection on the image, and finally applied a threshold function technique
with Prewitt operator.
Keywords: Gaussian noise, Prewitt operator, edge detection, threshold function
Human action recognition with kinect using a joint motion descriptorSoma Boubou
- We proposed a novel descriptor for motion of skeleton joints.
- Proposed descriptor proved to outperform the state-of-the-art descriptors such as HON4D and the one proposed by Chen et al 2013.
- Our proposed approached proved to be effective for periodic actions (e.g., Waving, Walking, Jogging, Side-Boxing, etc).
- Grouping was effective for actions with unique joints trajectories (e.g., Tennis serving, Side kicking , etc).
- Grouping joints into eight groups is always effective with actions of MSR3D dataset.
This project finds real objects dimensions from their images using OpenCV Java using two methods - Reference Method and Stereo Method.
Code for this project is available on my GitHub:
https://github.com/shyamabhuvanendran/VirtualRuler_CV
OVERVIEW AND APPLICATION OF ENABLING TECHNOLOGIES ORIENTED ON ENERGY ROUTING ...ijaia
Energy routers are recent topics of interest for scientific community working on alternative energy.
Enabling technologies supporting installation and monitoring energy efficiency in building are discussed in
this paper, by focusing the attention on innovative aspects and on approaches to predict risks and failures
conditions of energy router devices. Infrared (IR) Thermography and Augmented Reality (AR) are
indicated in this work as potential technologies for the installation testing and tools for predictive
maintenance of energy networks, while thermal simulation, image post-processing and data mining
improve the analysis of the prediction process. Image post- processing has been applied on thermal images
and for WiFi AR. Concerning data mining we applied k-Means and Artificial Neural Network –ANNobtaining
outputs based on measured data. The paper proposes some tools procedure and methods
supporting the Building Information Modeling- BIM- in smart grid applications. Finally we provide some
ISO standards matching with the enabling technologies by completing the overview of scenario
Massive Sensors Array for Precision Sensingoblu.io
More than a billion smartphones being sold annually and growing with CAGR of 16%, the smartphone industry has become a driving force in the development of ultralow-cost inertial sensors. Unfortunately, these ultra low-cost sensors do not yet meet the needs of more demanding applications like inertial navigation and biomedical motion tracking systems. However, by adapting a wisdom of the crowd’s thinking and design arrays consisting of hundreds of sensing elements, one can capitalize on the decreasing cost, size, and power-consumption of the sensors to construct virtual high-performance low-cost inertial sensors. Team at KTH, Sweden and WUSTL, USA share findings and challenges.
Nuclear Material Verification Based on MCNP and ISOCSTM Techniques for Safegu...IOSRJAP
Recently, Mathematical techniques such as Monte Carlo and ISOCSTM software are being increasingly employed in the absolute efficiency calibration of gamma ray detector. Monte Carlo simulations and Canberra ISOCSTM software bring the possibility to establish absolute efficiency curve for desired energy range based on numerical simulation, with use of known or guessed geometry and chemical composition, of measured item. Broad-energy germanium (BEGe) detector was employed to perform the NDA measurements to five standard reference nuclear material (NBS, SNM-969). MC calculations were performed to calculate some factors (attenuation, geometry and efficiency) which affect the uranium isotope mass estimation. 235U and 238U masses are calculated based on MCNPX modeling calibration and also upon spectra analysis using ISOCSTM Calibration Software. The obtained results from the two different efficiency calibration methods were compared with each other and with the declared value for each sample. The obtained results are in agreements with the declared values within the estimated relative accuracy (ranges between -2.81 to 1.83%). The obtained results indicate that the techniques could be applied for the purposes of NM verification and characterization where closely matching NM standards are not available.
Multi-core GPU – Fast parallel SAR image generationMahesh Khadatare
This poster presents a design for parallel processing of synthetic
aperture radar (SAR) data using multi-core Graphics Processing
Units (GP-GPUs). In this design a two-dimensional image is
reconstructed from received echo pulses and their corresponding
response values. Then the comparative performance of proposed
parallel algorithm implementation is tabulated using different set of
nvidia GPUs i.e. GT, Tesla and Fermi. Analyzing experimentation
results on image of various size 1,024 x 1,024 pixels to 4,096 x
4,096 pixels led us to the conclusion that that nvidia Fermi S2050 have
maximum performance throughput .
The frame camera is used by users of digital single-lens reflex cameras (DSLRs) as a shorthand for an image sensor format which is the same size as 35mm format (36 mm × 24 mm) film.
Panoramic imagery is created either by digitally stitching together multiple images from the same position (left/right, up/down) or by rotating a camera with conventional optics, and an area or line sensor.
A (very brief) Introduction to Image Processing and 3D Printing with ImageJPaul Mignone, Ph.D
Using ImageJ to extract 3D models from material image data-sets for 3D printing purposes. Participants will have an opportunity to 3D print a small model using the Objet Connex500 3D printer.
Current scientific imaging (and image processing) technologies allow researchers and professionals to visualize the morphology of complex composite materials at very fine length scales. Combined with the advances and accessibility of additive manufacturing technologies, researches are able to produce tangible assets of these composite morphologies for further research and analysis. The following workshop will briefly introduce image processing theory, as well as an open source tool chain to produce 3D models of a material data-set for 3D printing purposes.
Inertial Sensor Array Calibration Made Easy !oblu.io
Ultra-low-cost single-chip inertial measurement units (IMUs) combined into IMU arrays are opening up new possibilities for inertial sensing. However, to make these systems practical, calibration and misalignment compensation of low-cost IMU arrays are necessary and a simple calibration procedure that aligns the sensitivity axes of the sensors in the array is needed. Team at KTH suggests a novel mechanical-rotation-rig-free calibration procedure based on blind system identification and a platonic solid (Icosahedron) printable by a contemporary 3D-printer. Matlab-scripts for the parameter estimation and production files for the calibration device are made available.
An infrared thermometer is a thermometer which infers temperature from a portion of the thermal radiation sometimes called blackbody radiation emitted by the object being measured.
They are sometimes called laser thermometers as a laser is used to help aim the thermometer,
or non-contact thermometers or temperature guns, to describe the device's ability to measure temperature from a distance.
visit us at https://goo.gl/vwr40b to know more.
ADAPTIVE FILTER FOR DENOISING 3D DATA CAPTURED BY DEPTH SENSORSSoma Boubou
Current consumer depth sensors produce depth maps that are often noisy and lack sufficient detail. Enhancing the quality of the 3D depth data obtained from compact depth Kinect-like sensors is an increasingly popular research area. Although depth data is known to carry a signal-dependent noise, the state-of-the-art denoising methods tend to employ denoising techniques which are independent of the depth signal itself. In this paper, we present a novel adaptive denoising filter to enhance object recognition from 3D depth data. We evaluate the performance of our proposed denoising filter against other state-of-the-art filters based on the enhancement of object recognition accuracy achieved after denoising the raw data with each filter. In order to perform object recognition from depth data, we make use of Differential Histogram of Normal Vectors (DHONV) features along with a linear SVM. Experiments show that our proposed filter outperformed the state-of-the-art denoising methods.
Visual Quality for both Images and Display of Systems by Visual Enhancement u...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
http://www.Media4MathPlus.com
In this issue of Math in the News we look at 3D printing technology. In particular, we look at Nike's recent unveiling of a new running shoe developed using 3D printing. Mathematically, we look at recursive functions, since the 3D printing technology was used for rapid, iterative prototyping of the new shoe. We look at an example of a recursive function, using the Babylonian method to calculate the square root of a number.
Scan Segmentation Approach to Magnify Detection Sensitivity for Tiny Hardware...奈良先端大 情報科学研究科
Outsourcing of IC fabrication components has initiated the
potential threat of design tempering using hardware Trojans and also has drawn the attention of government agencies and the semiconductor industry. The added functionality, known as hardware Trojan, poses major detection and isolation challenges. This paper presents a hardware Trojan detection technique that magnifies the detection sensitivity for small Trojan in power-based side-channel analysis. A scan segmentation approach with a modified LOC test pattern application method is proposed so as to maximize dynamic power consumption of any target segment. The proposed architecture allows activating any target segment of scan chain and keeping others freeze which reduces total circuit switching activity. This helps magnify the Trojan’s contribution to selected segment by increasing dynamic power
consumption. Experimental results for ISCAS89 benchmark circuit demonstrate its effectiveness in side-channel analysis.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Similar to Image orientation classification analysis (20)
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
1. An Analysis of Classification Techniques for Image
Orientation Classification
Problem Statement:
This is a straightforward image classification study to create and compare classifiers (KNN, Neural
Networks and Adaboost) that decide the correct orientation of a given image i.e. 0°, 90°, 180° 𝑜𝑜𝑜𝑜 270°
(as shown in figure 1).
𝟎𝟎° 𝟗𝟗𝟗𝟗° 𝟐𝟐𝟐𝟐𝟐𝟐° 𝟏𝟏𝟏𝟏𝟎𝟎°
Dataset:
The dataset is of images borrowed from Flickr photo sharing, taken and uploaded by real users from
across the world. Raw images are treated as a numerical feature by taking each n x m x 3 color image
and appending all of the rows together to produce a single vector of size 1x3mn, on which standard
machine learning techniques can be applied. Each image is rescaled to a very tiny "micro-thumbnail"
of 8x8 pixels, resulting in an 8x8x3 = 192 dimensional feature vector. The text files have one row per
image. The training dataset consists of about 10,000 images, while the test set contains about 1,000.
Classifiers:
1) Neural Network
A three layer (one hidden layer) feed forward neural network featuring Stochastic Gradient
Descent is implemented.
Data pre-processing: The data is normalized by dividing each data point by 255 followed by
subtraction with the mean and finally dividing by the standard deviation. It was clearly
observed that without this preprocessing step, the model was not able to perform (was
giving random accuracy) under any circumstances.
Parameters experimented with:
Step sizes: 0.1, 0.01, 0.0001
Activation functions: Sigmoid, tanh
No. of Hidden layer neurons: 10, 200, 600
Epochs: 1, 20, 50
Observations:
It was observed that the number of epochs did not enhance the accuracy by a noticeable
margin. Hence, the following results are shown for one epoch.
2. Activation
Function
Step
size
Number of
Hidden Layer
Neurons
Running
Time (sec)
Accuracy (%)
Sigmoid
0.1
10 14.72200012 67.126193
200 51.89700007 65.00530223
600 186.4519999 30.54082715
0.01
10 14.66200018 64.05090138
200 53.99899983 71.26193001
600 225.901 68.83775186
0.0001
10 14.50999999 44.22057264
200 54.28299999 58.53658537
600 185.635 61.82396607
Accuracies obtained with Sigmoid activation function
Activation
Function
Step
size
Number of
Hidden Layer
Neurons
Running
Time (sec)
Accuracy
(%)
tanh
0.1
10 13.59500003 56.81018028
200 42.16799998 38.38812301
600 154.8970001 50.26511135
0.01
10 13.94499993 63.30858961
200 42.95900011 64.58112407
600 167.5339999 62.88441145
0.0001
10 13.44700003 32.34358431
200 43.27499986 43.90243902
600 158.342 35.41887593
Accuracies obtained with tanh activation function
● Sigmoid activation function works better than tanh
● The error increases for very low or very high number of hidden layer neurons
● 0.01 was found to be the most effective in terms of accuracy
● The running time increases with the increase in the number of hidden layer neurons
● The accuracy for each run varies by approximately ± 5%.
3. The code was also run with random splits of the train set of varying percentages. Some
interesting observations were made as follows:
Percent of train
data set
Number of train
rows processes
Running Time
(sec)
Accuracy
(%)
0.5 184 9.460000038 43.69034995
2 739 9.905999899 60.65747614
10 3697 13.41300011 65.42948038
20 7395 18.37800002 68.82290562
30 11092 22.63999987 69.67126193
50 18488 30.99000001 69.88335101
80 29580 44.70600009 69.95917285
100 36976 63.12800002 72.11028632
It can be seen that with just 20% of randomly selected training data, almost 69% accuracy is
achieved which is very near to the accuracy obtained training on the entire train set.
Moreover, there is a huge difference between the time taken to train with 20% data and 100% data.
Conclusion:
As it can be observed from the above tables that for a three layer feed forward neural network
implementing Stochastic Gradient Descent, the following configuration is recommended:
Activation function: Sigmoid
Step size: 0.01
No. of Hidden layer neurons: 200
Accuracy obtained: 71.26193001%
Sample images classified correctly:
4. Sample images classified incorrectly:
2) KNN
Data pre-processing:
Normalization did not improve accuracy and took lot of time to run, so no preprocessing done.
Observations:
Below table has runtime and accuracy for different values of K.
K Accuracy Runtime (mins)
1 67.23 20.54
50 71.26 25.41
100 70.30 40.51
150 70.30 41.10
200 7.26 40.40
Below table has runtime, accuracy for varying amount of probability of choosing each record
in test.
Probability K Accuracy Runtime (mins)
0.25 200 68.92 10.34
0.5 200 70.83 20.55
1 200 70.26 40.40
Sample Images classified correctly:
5. Sample Images classified incorrectly:
Observations: k > 25 works giving less error. We would recommend working with K = 200;
keeping in mind that time to run KNN with K as 200 takes time similar to k as 1.
3) Adaboost
For each decision stump, best pair of features are chosen from 100 pairs to reduce computation
time. One vs all classifiers are implemented for multi class prediction among classes of 0, 90,
180, 270. In each decision stump, best attribute is determined based on the accuracy, number
of images are correctly classified.
Number of stumps Accuracy (%) Running Time (sec)
1 61 43.55
2 65.1
3 65.3 142.5
5 70 267.96
7 72.8 383.77
9 73.49 477.92
11 74.12 647.81
17 74.12 969.85
It is observed that, in each run with different number of stumps, most of the 270 orientation
images are failed to be predicted correctly.
In most circumstances, the 270 orientation is predicted as 180.
6. Percent
of train
data set
Number of train
rows processes
Running Time
(sec)
Accuracy (%)
50 18488 325 73.9
80 29580 630 73.7
100 36976 647.81 74.12
Sample Images classified correctly:
Sample Images classified incorrectly:
Best classifier:
Among all of the above classifiers, Adaboost with decision stumps performed better on the given
training and test data sets. Accuracy of 74.12 % is achieved for the 11 stumps.