AI News, BOOK REVIEW: Department of Electrical and Computer Engineering
The Best Computer Science Programs in the World Today
Computer scientists develop powerful software and algorithms that have the incredible predictive power to match products to consumers, predict political elections, and even help people find lifelong romantic partners.
Computer scientists study topics such as: computer networking, information systems, computer security, data and knowledge engineering, mainframe computing, and software development.
The rankings uncovered by AcademicInfluence.com are based on the influential faculty and alum publications, citations, and references associated with a school, degree programs, departments, or discipline.
By weighing both the QS Programmatic World Rankings and Academic Influence’s rankings, we not only created a unique ranking of the top 50 computer science programs, but we reveal why each university computer science program appears where it does.
Suranjan Panigrahi, Ph.D., MBA
May. (This special issue contained selected peer-reviewed papers that were presented at the International Biological Sensorics Conference that was held in 2007 at Minneapolis.
On-the-go sugar sensor for determining sugar content of sugar beets during harvesting- Part- 1 (hardware) US Patent
On-the-go sugar sensor for determining sugar content of sugar beets during harvesting - Part- II (Software) US Patent 6,851,662 B1 February 2005.
Evaluation of computer imaging techniques for predicting SPAD reading in potato leaves.
Evaluation of a commercial electronic nose system coupled with universal gas sensing chamber for sensing indicator compounds associated with meat safety.
Evaluation of surface enhanced Raman spectroscopy for detection of Acetone in the context of food safety and quality application.
Olfactory receptor-based polypeptide sensor for acetic acid VOC detection.
Evaluation of technique to overcome small dataset problems during neural-network based contamination classification of packaged beef using integrated olfactory sensor system.
Investigation of different gas-sensor-based artificial olfactory systems for screening Salmonella typhimurium contamination in beef.
nanocomposite sensors for butanol detection related to food contamination.
Nanoparticulate zinc oxide chemoresistive sensor for volatile acetic acid detection.
Residual soil nitrate prediction from imagery and non-imagery information using neural network technique.
Development and evaluation of piezoelectric polymer thin film sensors for low concentration detection of volatile organic compounds related to food safety applications.
Odor binding protein-based biomimetic sensor for detection of alcohols associated with Salmonella contamination in packaged beef.
Olfactory receptor-based piezo electric biosensors for detection of alcohols related to food safety applications.
Application of vapor-phase Fourier transform infrared spectroscopy (FT-IR) and statistical feature selection methods for indentifying S.
A comparative qualitative study of the profile of volatile organic compounds associated with Salmonella contamination of packaged aged and fresh beef by HS-SPME/GC-MS.
Study of headspace gases associated with Salmonella contamination of sterile beef in vials using HS-SPME/GC-MS.
Simultaneous prediction of acetic acid/ethanol concentrations in their binary mixtures using metalloporphyrin based opto-electronic nose for meat safety applications.
Development and evaluation of chemoresistive polymer sensors for low concentration detection of volatile organic compounds related to food safety applications.
Neural networks-integrated metal oxide-based artificial olfactory system for meat spoilage identification.
Neural-network-based classification of meat: Evaluation of techniques to overcome small dataset.
Independent component analysis-processed electronic nose data for predicting Salmonella typhirium population in contaminated beef.
Fungicide deposition measurement by spray volume, drop size and sprayer system in cereal grains.
Leaf nitrogen determination of corn plant using aerial images and artificial neural networks.
Evaluation of an artificial olfactory system for grain quality discrimination.
Neural network optimization of remotely sensed maize leaf nitrogen with a genetic algorithm and linear programming using five performance parameters.
Design and Development of a metal oxide based electronic nose for spoilage classification of beef.
Neural network integrated electronic nose system for spoilage identification of beef.
Identification of Salmonella inoculated beef using a portable electronic nose system.
Multispectral and color imaging techniques for nitrate and chlorophyll determination of potato leaves in a controlled environment.
Influence of oat kernel size and size distributions of test weight.
Image processing techniques and neural network models for predicting plant nitrate using aerial images.
Self-organizing map combined with a fuzzy clustering for color image segmentation of edible beans.
Techniques for yield prediction for corn aerial images-A neural network approach.
An interactive computer software for selected parametric and non-parametric (including neural networks) for chemometrics and prediction.
An interactive computer software for selected neural networks for classification and prediction.
(Photonics Spectra is an International magazine covering advancements of optics, lasers, fiber optics, electro-optics, imaging and optical computing with more than 92,000 distributions all over the world).
A Systems-based approach for designing field scale environemntal monitoring system: specific focus on heavy metal contamination in water.
Overview of parallel virtual machine (PVM) and its use for efficient image processing of beans.
A systems-based approach and analysis for water-linked health and wellness in low resource setting.
Analysis and overview of techniques to incorporate innovation in undergraduate curriculum in electrical engineering technology.
Challenges and opportunities: sensors and intelligent systems for food supply chain.
Potential and applications of advanced imaging and sensing techniques for cereal grain process.
Experiences on using portable spectrometers for real-time measurements of agricultural products.
Non-destructive sensing techniques for fatty acid profiles of confection sunflower.
Students Courtney Spivey and Cheyanne Wheat, enrolled in one of the College of Engineering and Natural Science’s fastest growing majors, are spending their summer diving into computer simulation and gaming development – with a humanities twist.
The University of Tulsa’s computer simulation and gaming degree begins with core computer science classes in the fundamentals of programming and understanding computer systems, and then gives students the freedom to choose a specialization.
In January, she began her TURC research exploring deep learning, artificial neuro networks (ANNs) and the capabilities and current limitations of artificial intelligence (AI). In addition to machine learning and AI, Spivey’s work has grown to include the study of human behavior in psychology in an attempt to find connections between the similarities of the creators and their methods for approaching deep learning.
Fellow computer simulation and gaming major Cheyanne Wheat sits at a computer across TU’s campus in Rayzor Hall working on a similar project that also involves collaboration with TU arts and sciences programming. A junior originally from the Tulsa area, she has teamed up with TU anthropology Professor Bob Pickering to create a simulated time progression of an Indian burial mound’s construction.
“I want to know how we can use games or game-like activities based on a museum collection to engage a younger audience,” Pickering explained. “Gilcrease has 10,000 years of human history objects from the Americas, but if you’re a 9-year-old, you don’t know these objects, you don’t have any connection to them and you don’t know why they’re important.” According to Pickering, the museum video game concept is an experiment on every level, but collaboration with computer simulation and gaming students on a “museum forward” idea is important for the next generation of museum professionals.
Players will explore a landscape full of nature, animals and artifacts from the Hopewell Tribe 250 BCE to 250 CE while learning about history and civilization. The objective is to tell the story behind historical objects and discuss how museum-goers of all ages can learn from a video game feature.
- On 24. september 2021
Robotics at Harvard
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NEDUET EE Final Year Project - AI NILM Demonstration
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ECE department initiatives
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Top 10 Computer Science Projects For Students 2018
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Final Year Engineering Student Project Showcase
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CSE Research Spotlight: Maria Gini
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