Guy Leshem provides his curriculum vitae, which details his experience as a software engineer, physicist, and statistician. He has a PhD in statistics from Hebrew University, with a thesis focusing on improving machine learning algorithms. He has over 10 years of experience as a software engineer at Intel developing networking tools. Currently he works as a statistical advisor and has taught university courses.
3d Modelling of Structures using terrestrial laser scanning techniqueIJAEMSJORNAL
In recent times, interest in the study of engineering structures has been on the rise as a result of improvement in the tools used for operations such as, As-built mapping, deformation studies to modeling for navigation etc. There is a need to be able to model structure in such way that accurate needed information about positions of structures, features, points and dimensions can be easily extracted without having to pay physical visits to site to obtain measurement of the various components of structures. In this project, the data acquisition system used is the terrestrial laser scanner, High Definition Surveying (HDS) equipment; the methodology employed is similar to Close Range Photogrammetry (CRP). CRP is a budding technique or field used for data acquisition in Geomatics. It is a subset of the general photogrammetry; it is often loosely tagged terrestrial photogrammetry. The terrestrial laser scanning technology is a data acquisition system similar to CRP in terms of deigning the positioning of instrument and targets, calibration, ground control point, speed of data acquisition, data processing (interior, relative and absolute orientation) and the accuracy obtainable. The aim of this project was to generate the three-dimensional model of structures in the Faculty of Engineering, University of Lagos using High Definition Surveying, the Leica Scan Station 2 HDS equipment was used along with Cyclone software for data acquisition and processing. The result was a 3D view (of point clouds) of the structure that was studied, from which features were measured from the model generated and compared with physical measurement on site. The technology of the laser scanner proved to be quite useful and reliable in generating three dimensional models without compromising accuracy and precision. The generation of the 3D models is the replica of reality of the structures with accurate dimensions and location.
Feasibility of Artificial Neural Network in Civil Engineeringijtsrd
An Artificial neural network ANN is an information processing hypothesis that is stimulated by the way natural nervous system, such as brain, process information. The using of artificial neural network in civil engineering is getting more and more credit all over the world in last decades. This soft computing method has been shown to be very effective in the analysis and solution of civil engineering problems. It is defined as a body which works out the more and more complex problem through sequential algorithms. It is designed on the basis of artificial intelligence which is proficient of storing more and more information's. In this work, we have investigated the various architectures of ANN and their learning process. The artificial neural network based method was widely applied to the civil engineering because of the strong non linear relationship between known and un known of the problems. They come with good modelling in areas where conventional approaches finite elements, finite differences etc. require large computing resources or time to solve problems. These includes to study the behaviour of building materials, structural identification and control problems, in geo technical engineering like earthquake induced liquefaction potential, in heat transfer problems in civil engineering to improve air quality, in transportation engineering like identification of traffic problems to improve its flexibility , in construction technology and management to estimate the cost of buildings and in building services issues like analyzing the water distribution network etc. Researches reveals that the method is realistic and it will be fascinated for more civil engineering applications. Vikash Singh | Samreen Bano | Anand Kumar Yadav | Dr. Sabih Ahmad ""Feasibility of Artificial Neural Network in Civil Engineering"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22985.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/22985/feasibility-of-artificial-neural-network-in-civil-engineering/vikash-singh
3d Modelling of Structures using terrestrial laser scanning techniqueIJAEMSJORNAL
In recent times, interest in the study of engineering structures has been on the rise as a result of improvement in the tools used for operations such as, As-built mapping, deformation studies to modeling for navigation etc. There is a need to be able to model structure in such way that accurate needed information about positions of structures, features, points and dimensions can be easily extracted without having to pay physical visits to site to obtain measurement of the various components of structures. In this project, the data acquisition system used is the terrestrial laser scanner, High Definition Surveying (HDS) equipment; the methodology employed is similar to Close Range Photogrammetry (CRP). CRP is a budding technique or field used for data acquisition in Geomatics. It is a subset of the general photogrammetry; it is often loosely tagged terrestrial photogrammetry. The terrestrial laser scanning technology is a data acquisition system similar to CRP in terms of deigning the positioning of instrument and targets, calibration, ground control point, speed of data acquisition, data processing (interior, relative and absolute orientation) and the accuracy obtainable. The aim of this project was to generate the three-dimensional model of structures in the Faculty of Engineering, University of Lagos using High Definition Surveying, the Leica Scan Station 2 HDS equipment was used along with Cyclone software for data acquisition and processing. The result was a 3D view (of point clouds) of the structure that was studied, from which features were measured from the model generated and compared with physical measurement on site. The technology of the laser scanner proved to be quite useful and reliable in generating three dimensional models without compromising accuracy and precision. The generation of the 3D models is the replica of reality of the structures with accurate dimensions and location.
Feasibility of Artificial Neural Network in Civil Engineeringijtsrd
An Artificial neural network ANN is an information processing hypothesis that is stimulated by the way natural nervous system, such as brain, process information. The using of artificial neural network in civil engineering is getting more and more credit all over the world in last decades. This soft computing method has been shown to be very effective in the analysis and solution of civil engineering problems. It is defined as a body which works out the more and more complex problem through sequential algorithms. It is designed on the basis of artificial intelligence which is proficient of storing more and more information's. In this work, we have investigated the various architectures of ANN and their learning process. The artificial neural network based method was widely applied to the civil engineering because of the strong non linear relationship between known and un known of the problems. They come with good modelling in areas where conventional approaches finite elements, finite differences etc. require large computing resources or time to solve problems. These includes to study the behaviour of building materials, structural identification and control problems, in geo technical engineering like earthquake induced liquefaction potential, in heat transfer problems in civil engineering to improve air quality, in transportation engineering like identification of traffic problems to improve its flexibility , in construction technology and management to estimate the cost of buildings and in building services issues like analyzing the water distribution network etc. Researches reveals that the method is realistic and it will be fascinated for more civil engineering applications. Vikash Singh | Samreen Bano | Anand Kumar Yadav | Dr. Sabih Ahmad ""Feasibility of Artificial Neural Network in Civil Engineering"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22985.pdf
Paper URL: https://www.ijtsrd.com/engineering/civil-engineering/22985/feasibility-of-artificial-neural-network-in-civil-engineering/vikash-singh
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
Machine Learning for Chemistry: Representing and InterveningIchigaku Takigawa
Joint Symposium of Engineering & Information Science & WPI-ICReDD in Hokkaido University
Apr. 26 (Mon), 2021
https://www.icredd.hokudai.ac.jp/event/5430
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
Top cited article in 2019 - International Journal of Network Security & Its A...IJNSA Journal
The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
Top Cited Articles in 2019 - International Journal of Network Security & Its ...IJNSA Journal
Recently, the mode of living became more complicated without computer systems. The techniques of camouflage information have acquired a vital role with the requirement of intensifying trade of multimedia content. Steganography is the technique that utilizes disguise in a way that prohibits unauthorized access from suspicion of the existence of confidential information exchanged during communication channels between the connected parties. In this paper, an integrated image steganographic system is designed to conceal images, messages or together where the mainly deliberate the improvement of embedding capacity through embedding text with image simultaneously. For that purpose, used matrix partition to partition the secret image then embedded each partition separately after scrambling each pixel by replacing msb instead of lsb to provide the second level of security furthermore to steganography. The simulation results clarify the better performance of the proposed algorithms
International Journal of Embedded Systems and Applications (IJESA) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Embedded Systems and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Embedded Systems and establishing new collaborations in these areas.
Image Compression Through Combination Advantages From Existing TechniquesCSCJournals
The tremendous growth of digital data has led to a high necessity for compressing applications either to minimize memory usage or transmission speed. Despite of the fact that many techniques already exist, there is still space and need for new techniques in this area of study. With this paper we aim to introduce a new technique for data compression through pixel combinations, used for both lossless and lossy compression. This new technique is also able to be used as a standalone solution, or with some other data compression method as an add-on providing better results. It is here applied only on images but it can be easily modified to work on any other type of data. We are going to present a side-by-side comparison, in terms of compression rate, of our technique with other widely used image compression methods. We will show that the compression ratio achieved by this technique tanks among the best in the literature whilst the actual algorithm remains simple and easily extensible. Finally the case will be made for the ability of our method to intrinsically support and enhance methods used for cryptography, steganography and watermarking.
Top 5 MOST VIEWED LANGUAGE COMPUTING ARTICLE - International Journal on Natur...kevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
4 Cold Email Subject Lines That Get OpenedAlex Berman
This presentation goes over 4 different subject lines you can use to get your emails opened.
Need more leads for your SaaS startup or agency? Check out http://inspirebeats.com
Boolean Search Fundamentals For Recruiters - GuideProminence
This printable guide was produced to complement the deskside cheat sheets. The guide goes into far more detail on Boolean Logic, including several real life examples.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
Machine Learning for Chemistry: Representing and InterveningIchigaku Takigawa
Joint Symposium of Engineering & Information Science & WPI-ICReDD in Hokkaido University
Apr. 26 (Mon), 2021
https://www.icredd.hokudai.ac.jp/event/5430
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
Top cited article in 2019 - International Journal of Network Security & Its A...IJNSA Journal
The International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
Top Cited Articles in 2019 - International Journal of Network Security & Its ...IJNSA Journal
Recently, the mode of living became more complicated without computer systems. The techniques of camouflage information have acquired a vital role with the requirement of intensifying trade of multimedia content. Steganography is the technique that utilizes disguise in a way that prohibits unauthorized access from suspicion of the existence of confidential information exchanged during communication channels between the connected parties. In this paper, an integrated image steganographic system is designed to conceal images, messages or together where the mainly deliberate the improvement of embedding capacity through embedding text with image simultaneously. For that purpose, used matrix partition to partition the secret image then embedded each partition separately after scrambling each pixel by replacing msb instead of lsb to provide the second level of security furthermore to steganography. The simulation results clarify the better performance of the proposed algorithms
International Journal of Embedded Systems and Applications (IJESA) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Embedded Systems and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Embedded Systems and establishing new collaborations in these areas.
Image Compression Through Combination Advantages From Existing TechniquesCSCJournals
The tremendous growth of digital data has led to a high necessity for compressing applications either to minimize memory usage or transmission speed. Despite of the fact that many techniques already exist, there is still space and need for new techniques in this area of study. With this paper we aim to introduce a new technique for data compression through pixel combinations, used for both lossless and lossy compression. This new technique is also able to be used as a standalone solution, or with some other data compression method as an add-on providing better results. It is here applied only on images but it can be easily modified to work on any other type of data. We are going to present a side-by-side comparison, in terms of compression rate, of our technique with other widely used image compression methods. We will show that the compression ratio achieved by this technique tanks among the best in the literature whilst the actual algorithm remains simple and easily extensible. Finally the case will be made for the ability of our method to intrinsically support and enhance methods used for cryptography, steganography and watermarking.
Top 5 MOST VIEWED LANGUAGE COMPUTING ARTICLE - International Journal on Natur...kevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
4 Cold Email Subject Lines That Get OpenedAlex Berman
This presentation goes over 4 different subject lines you can use to get your emails opened.
Need more leads for your SaaS startup or agency? Check out http://inspirebeats.com
Boolean Search Fundamentals For Recruiters - GuideProminence
This printable guide was produced to complement the deskside cheat sheets. The guide goes into far more detail on Boolean Logic, including several real life examples.
Become a Ninja Recruiter: Advanced Boolean Search | Talent Connect London 2014LinkedIn Talent Solutions
Learn from Glen Cathey, author of the Boolean Black Belt blog, to extract maximum value from LinkedIn's massive professional network. Learn human capital data retrieval concepts and best practices, expose hidden talent pools within LinkedIn, and explore the five levels of LinkedIn Talent Mining.
Continue your talent acquisition transformation at Talent Connect 365: http://linkd.in/1z8YEaf
The most effective subject lines are straightforward and predispose openers to engage with the content of an email. However, creativity has its place. Creativity in less than half the length of a tweet can give your emails the edge—especially when your subject line is backed up with great email content.
Resumes Suck! 7 Ways to Find a Job in Social Media from 2016 SXSWWorkology
Slides from the 2016 SXSW Interactive Presentation with Jessica Miller-Merrell and Carlos Gil. Practical advice on how to use social media for your job search from the perspective of a social media expert, recruiter and hiring manager.
How to Get People to Respond to Your Recruiting Emails & MessagesGlen Cathey
When it comes to sourcing and recruiting, it's gotten easier to find people but it's gotten more difficult to get people to respond to emails, InMails, social messages and voicemails. The poor quality and lack of sophistication of most recruiter messaging, along with rampant spamming, certainly hasn't helped. Unfortunately and yet somewhat thankfully, the bar of what people expect to receive from recruiters has been set fairly low, so the opportunity for improvement is massive. The good news is that becoming more effective at getting people to respond to recruiting outreach efforts is relatively easy because marketing & advertising has already blazed the trail - sourcers and recruiters would do well to leverage what effective sales & marketing teams has been doing for decades.
In 2014 and 2015, I spoke at Talent 42, SOSUEU, and LinkedIn Talent Connect conferences on the challenges of getting people - especially "passive," highly recruited talent - to respond to recruiter outreach efforts. The decks I used for the presentations were mostly images, so I decided to add text to the slides so that the core concepts could be understood by anyone whether they attended those conference sessions or not simply by viewing the presentation (I wish more presenters would do this!).
Presented at the Intel Global IoT DevFest (Oct 2017)
- Real-world use cases: healthcare, building management, retail, smart cities, transportation
- Time-series analysis
- AI / ML overview & applications
In the present paper, electroencephalogram (EEG)
data have been used to human identification by computing
sample entropy and graph entropy as feature extractions. Used
two classifier types, which are K-Nearest Neighbors (K-NN) and
Support Vector Machine (SVM). Python and Matlab software
were used in this study and EEG data was collected by UCI
repository . Matlab used when Thirteen channels was applied as
feature extraction . The experimental results show that, Python
software classifies the EEG-UCI data better than MATLAB
environment software where the accuracy of KNN and SVM
were 85.2% and 91.5% respectively.
Browser-Based Collaborative Modeling in Near Real-TimeNicolaescu Petru
Nicolaescu, P., Derntl, M., Klamma, R.: Browser-Based Collaborative Modeling in Near Real-Time. In Proceedings of 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2013). Austin, TX, USA: IEEE (2013)
Model-Simulation-and-Measurement-Based Systems Engineering of Power System Sy...Luigi Vanfretti
This talk starts by exploring how electrical power systems are increasingly becoming digitalized, leading to their transformation into a class of cyber-physical systems (a system of systems) where the electrical grid merges with ubiquitous information and communication technologies (ICT).
This type of complex systems present unprecedented challenges in their operation and control, and due to unknown interactions with ICT, require new concepts, methods and tools to facilitate their operational design, manufacturing (of components), and testing/verification/validation of their performance.
Inspired by the tremendous advantages of the model-based system engineering (MBSE) framework developed by the aerospace and military communities, this talk will highlight the challenges to adopt MBSE for electrical power grids. MBSE is not only a framework to deal with all the phases of putting in place complex systems-of-systems, but also provides a foundation for the democratization of technology - both software and hardware.
The talk will illustrate the foundations that have been built by the presenter's research over the last 7 years, placed within the context of MBSE, with focus on areas of power engineering. Some of these foundations and contributions include the OpenIPSL, RaPId, SD3K, BableFish and Khorjin open source software developed and distributed online by the research group, and available at: https://github.com/ALSETLab
1. Curriculum Vitae
Software engineer (Algorithm), Physicist, Statistician
14/8 Nahal Soreq street,
Modi'in, Israel
Tel: 08-9705640, 052-2813942
E-mail: gleshem@cc.huji.ac.il
Url: http://shum.huji.ac.il/~gleshem
Personal data:
Full Name: Guy Leshem
Place of Birth: Kibbutz Ein Gav, Israel.
Birthdate: Aug-24-1963
Nationality: Israeli, German.
Marital status: Married to Shirely, Father to Shani.
Pets: Snow (Dog).
Address for Correspondence:
Department of Statistics
The Hebrew University of Jerusalem
Mount Scopus, Jerusalem 91905
ISRAEL
Highlights:
• PhD thesis that dealing with a new algorithm (development) in Statistics Machine
learning (based on Adaboost and Random Forest), Machine Vision (based on
Traffic Surveillance) and Operational Research (based on "Queuing systems
consisting of N queues served by a single server"). Obtained in Nov 2002 from
Hebrew university of Jerusalem.
• MS.c. laboratory work on: "Computer Add Surgery Development new algorithm
for reconstruction of 3D shapes views and statistical model".
• Ten years working as a networking engineer for Intel on the development of a
new GUI for networking configuration, Advance Network Server (ANS), SNMP
drivers on Linux OS.
• Statistical Adviser (Working at Applied Statistics Laboratory) for number of
commercial companies and academic researcher.
• Wide-ranging experience in tutoring and teaching with a good record of
motivating, challenging and supporting students.
• Creative, versatile and communicative.
• Two languages spoken fluently.
• Battalion Commander in army reserve (degree Lieutenant Colonel).
2. Professional Experience:
Work Experience:
• 10 years work experience in Intel as Software Engineer and Process
Engineer:
• Software Engineer in Intel IDC (networking Group), dealing with the
development and testing of adapters drivers for Linux / UNIX Os. The
projects that I deal as following:
1. Development of a new GUI for networking configuration of Linux
server for Dell Corporation. This GUI (PROCfg) is a reporting and
configuration tool for Intel adapters and Intel Advanced Network
Services (iANS). It works with the Intel drivers and running Linux.
2. Development of Advance Network Server (ANS) (teaming of number
of adapters) for UNIX Os (SCO). This tool support the following
teaming modes: Adapter Fault Tolerance, Switch Fault Tolerance,
Adaptive Load Balancing etc.
3. Development SNMP drivers on Linux Os for communication between
Linux server and windows client with MG-Soft. This SNMP agent
extension extends the UCD-SNMP agent for Linux the SNMP
protocol. It provides information on the Intel the adapter drivers,
teaming (Advanced Network Services (ANS) and VLANs,).
On a human and organizational level, the experience at
Intel gave me a first-hand impression of various leadership
styles and of the difficulties that can occur when managing
technical development projects.
• Process Engineer in Intel, dealing with development of a new process for
chips manufacture, control on the existing process and analysis of end of
line data (E-test, Sort), 1993 – 1999.
• Statistical Adviser
• Working at "Applied Statistics Laboratory" on numbers of project for
commercial companies and academic researcher. At this lab I’m working
on research project of MA/PhD students from the design to the final
analysis under the supervision of Prof. Ya'acov Ritov, 2004-2005, and on
projects for commercial companies under the supervision of Dr. Ronit
Nirel (2006-2007)
• Teaching Experience:
Courses taught:
Introduction to Statistics (for non-statisticians) (2006).
PROGRAMMING FOR STATISTICAL APPLICATONS (2007).
3. Other:
Teaching Assistant for Professor Leo Joskowicz, Robotics II, 1998.
Teaching Assistant for Dr. E. Zuchbistky, Matrices and Vector, 2002.
Teaching Assistant for Professor David Zucker, Regression and analysis, 2002
• Academic Experience:
1. Doctorate research, My PhD study include development a new algorithm for
improving the accuracy of algorithms for learning binary concepts. The
improvement is achieved by combining Random forests algorithm into AdaBoost
as weak learner. Random forests are a combination of tree predictors, where each
tree in the forest depends on the value of some random vector ,טso this weak
learner expect to be more “strong” and more accuracy, since 2003.
Additionally, my Ph.D. thesis also include the development of two new algorithm,
the first deal with real-time image processing of video stream for car tracking in
traffic video and the second with Operational Research model "Queueing systems
consisting of N queues served by a single server".
2. Master degree at the Computer Science institute Hebrew University. Work on
Image protocol and developing of Generic hardware, algorithms, and protocols
that will allow the accurate registration as a Lab study, 1999 – 2003. The project
that I deal with was developing innovative computer-based methods for assisting
surgeons in the planning, execution, and evaluation of surgical procedures based
on medical images.
3. Master Work "Developing of a Room Temperature KLAN Pyroelectric Detectors
Array for Thermal Imaging at the Graduate school of applied science &
Technology", 1993 – 1996. The project that I deal with was developing new
thermal imaging technology based on room temperature KLAN Pyroelectric
detectors array. The principal technical features were (1) Long-wavelength IR
operation. (2) Room temperature KLAN Pyroelectric detectors array. (3) Applied
electronic field to boost responsivity and provide stability. (4) Use of mechanical
chopper for field-difference processing. (5) Bump bonding of detector to readout
the electric signal.
Education:
2003 - 2007 : Ph.D. study at the Statistic department in Hebrew university of Jerusalem.
The Ph.D. subject: "Improvement of Adaboost Algorithm by using Random Forests as
Weak Learner and using this algorithm as statistics machine learning for traffic flow
prediction" under the supervision of Prof. Ya'acov Ritov.
• Thesis Topic: Advance technologies in identification and prediction of urban
traffic for the prevention of traffic violation and accidents (Improvement of
Adaboost Algorithm by using Random Forests as Weak Learner and using this
algorithm as statistics machine learning for traffic flow prediction).
4. • Principal Thesis Advisors: Prof. Yaacov Ritov.
• Major Fields of Interest: Development algorithm for Statistics Machine
learning, Machine Vision Based Traffic Surveillance and Operational Research
model "Queueing systems consisting of N queues served by a single server".
1999-2003: M.Sc. study in Hebrew university of Jerusalem at the Computer Science
department, part of the time guidance by Prof. Leo Juscovitch. The M.Sc. study includes
laboratory work on: "Computer Add Surgery Development new algorithm for
reconstruction of 3D shapes views and statistical model". (Mark: 85)
1993-1996: M.Sc. study in the Hebrew university of Jerusalem at the Graduate school of
applied science & Technology, guidance By Prof. A.J.Agranat. The M.Sc. subject:
"Development of a Room Temperature KLAN Pyroelectric Detectors Array for Thermal
Imaging". (Mark: 85)
1988-1992: B.Sc. study "material engineering for micro - electronic" in Ben Gurion
university at the Material Engineering department. (Mark: 84)
Publications:
• "Traffic Flow Prediction using Adaboost Algorithm with Random Forests as a Weak
Learner" Guy Leshem, Ya'acov Ritov. XIX. International Conference on Computer,
Information, and Systems Science, and Engineering January 29-31, 2007 Bangkok,
Thailand. [pdf (498)].
• "FRACAS: A System for Computer-Aided Image-Guided Long Bone Fracture
Surgery" L. Joskowicz, C. Milgrom, A. Simkin, O. Sadowsky, Z. Yaniv, G.Leshem
Proc. of the 2nd Int. Symposium on Medical Robotics and Computer Assisted
Surgery ,Jeruslem, 1999.
• "In-vitro accuracy of contact-based registration: materials, methods, and experimental
results", Z. Yaniv, O. Sadowsky, G. Leshem, L. Joskowicz, Technical report,
December 1999. [pdf (568)].
• Computer-aided image-guided bone fracture surgery: system integration and
prototype" L. Joskowicz, C. Milgrom, A. Simkin, O. Sadowsky, Z. Yaniv and G.
Leshem. Proc. 13th Int. Symposium on Computer Assisted Radiology and Surgery,
H.U. Lemke et. al. eds, Elsevier 1999, pp 710 [pdf (0.06MB)].
Major Practice:
.++Image processing by C•
.Robot movement analysis by Matlab•
• Development of algorithms for statistics machine learning: Adaboost and Random
Forests (written in Matlab, Fortran and C++).
• Development of algorithms for statistics analysis of missing data (written in
SAS).
5. • Compilers course by Java.
Skills:
• Scientific:
1. Due to Ph.D. in statistic and two master degrees (Physics & Computer
science) I have excellent ability to deal with a new issue.
2. Excellent knowledge of in Statistics Machine learning Theory, Machine
Vision and Operational Research model.
3. Good base in development of Adapters Drivers and Computer Networks.
• Programming: Very experienced Visual Studio C++, C, SAS, SQL, Matlab,
Windows, Unix and Linux.
Some experience of JAVA. SAS, R, SQL.
• Languages: Hebrew (mother tongue), English (high level).
Army:
Parachutist special unit, I'm battalion commander in army reserve (degree Lieutenant
Colonel).