This document provides a summary of Ben Jabeur Taoufik's background and qualifications. He received a PhD in Signal Processing from Paris Descartes University in 2009, with a thesis on channel shortening techniques in OFDM systems. Currently he works as a Research Scientist at Qatar Mobility Innovations Center, where his work involves signal and image processing applications such as passive RFID systems and analysis of physiological signals. He has published over 10 journal and conference papers in these areas.
Towards second generation expert systems in telepathology for aid in diagnosisTouradj Ebrahimi
Slides of my invited plenary talk at 10th European Congress on Telepathology and 4th International Congress on Virtual Microscopy, in Vilnius, Lithuania, 1-3 July 2010.
Telepathology involves the digital transmission of pathological images for remote diagnosis. It was coined in 1986 and addresses the need for faster sharing of medical images between locations. Key components of a telepathology system include a microscope, high-resolution camera, computer workstation, and network connection. There are static, virtual slide, and real-time telepathology categories. Requirements include suitable image formats, interactive controls, and security/confidentiality tools. Telepathology provides benefits like second opinions and expertise in remote areas, while applications include biopsies, operations, and education. India has conducted telepathology sessions between medical colleges since 2001.
The document summarizes a study evaluating different teleconferencing software options for use in telepathology. It provides background on pathology and telepathology, outlines requirements for an effective telepathology system, describes features of major videoconferencing vendors, and evaluates them based on the requirements. The document recommends Microsoft NetMeeting as a cost-effective solution that meets pathologists' needs, though notes limitations of the study and need for further evaluation.
Image Denoising is an important pre-processing task which is used before further processing of image The purpose of denoising is to remove the noise while retaining the edges and other detailed features This noise gets introduced during the process of acquisition, transmission and reception and storage and retrieval of the data Due to this there is degradation in visual quality of image The noises which are of major considerations are Additive White Gaussian Noise AWGN and Impulsive Noise Sehba Yousuf | Er. Arushi Baradwaj "Image Filtering Based on GMSK" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18403.pdf
This document is a newsletter from the Malaysian Society for Computed Tomography and Imaging Technology (MyCT) providing updates on recent events and the society's upcoming events. It discusses MyCT becoming a legally registered organization, the society's role in knowledge sharing and promoting the field. It also summarizes a regional training course hosted by IAEA on industrial computed tomography and announces the society's next board meeting and seminar to be held at Universiti Teknologi Petronas in May 2013.
Research on Ship Detection in Visible Remote Sensing Imagesijtsrd
This document summarizes research on ship detection methods in visible light remote sensing images. It discusses both traditional and deep learning-based methods. Traditional methods detect ships through feature extraction and classification, but struggle with complex backgrounds. Deep learning methods using convolutional neural networks have achieved better performance than traditional methods. Recent work has focused on introducing rotating bounding boxes to detect ships in different orientations, as well as using semantic segmentation to improve detection accuracy. Overall, deep learning represents the most promising approach, but challenges remain in adapting these methods for remote sensing images.
IRJET- A Review on Moving Object Detection in Video Forensics IRJET Journal
This document reviews research on moving object detection in video forensics. It discusses challenges in analyzing large amounts of surveillance video data and summarizes several papers that propose methods for tasks like video synopsis, abandoned object detection, person identification, copy-move forgery detection, and assessing evidence quality. The goal is to develop techniques for efficiently analyzing video evidence and detecting anomalies or tampering.
Towards second generation expert systems in telepathology for aid in diagnosisTouradj Ebrahimi
Slides of my invited plenary talk at 10th European Congress on Telepathology and 4th International Congress on Virtual Microscopy, in Vilnius, Lithuania, 1-3 July 2010.
Telepathology involves the digital transmission of pathological images for remote diagnosis. It was coined in 1986 and addresses the need for faster sharing of medical images between locations. Key components of a telepathology system include a microscope, high-resolution camera, computer workstation, and network connection. There are static, virtual slide, and real-time telepathology categories. Requirements include suitable image formats, interactive controls, and security/confidentiality tools. Telepathology provides benefits like second opinions and expertise in remote areas, while applications include biopsies, operations, and education. India has conducted telepathology sessions between medical colleges since 2001.
The document summarizes a study evaluating different teleconferencing software options for use in telepathology. It provides background on pathology and telepathology, outlines requirements for an effective telepathology system, describes features of major videoconferencing vendors, and evaluates them based on the requirements. The document recommends Microsoft NetMeeting as a cost-effective solution that meets pathologists' needs, though notes limitations of the study and need for further evaluation.
Image Denoising is an important pre-processing task which is used before further processing of image The purpose of denoising is to remove the noise while retaining the edges and other detailed features This noise gets introduced during the process of acquisition, transmission and reception and storage and retrieval of the data Due to this there is degradation in visual quality of image The noises which are of major considerations are Additive White Gaussian Noise AWGN and Impulsive Noise Sehba Yousuf | Er. Arushi Baradwaj "Image Filtering Based on GMSK" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18403.pdf
This document is a newsletter from the Malaysian Society for Computed Tomography and Imaging Technology (MyCT) providing updates on recent events and the society's upcoming events. It discusses MyCT becoming a legally registered organization, the society's role in knowledge sharing and promoting the field. It also summarizes a regional training course hosted by IAEA on industrial computed tomography and announces the society's next board meeting and seminar to be held at Universiti Teknologi Petronas in May 2013.
Research on Ship Detection in Visible Remote Sensing Imagesijtsrd
This document summarizes research on ship detection methods in visible light remote sensing images. It discusses both traditional and deep learning-based methods. Traditional methods detect ships through feature extraction and classification, but struggle with complex backgrounds. Deep learning methods using convolutional neural networks have achieved better performance than traditional methods. Recent work has focused on introducing rotating bounding boxes to detect ships in different orientations, as well as using semantic segmentation to improve detection accuracy. Overall, deep learning represents the most promising approach, but challenges remain in adapting these methods for remote sensing images.
IRJET- A Review on Moving Object Detection in Video Forensics IRJET Journal
This document reviews research on moving object detection in video forensics. It discusses challenges in analyzing large amounts of surveillance video data and summarizes several papers that propose methods for tasks like video synopsis, abandoned object detection, person identification, copy-move forgery detection, and assessing evidence quality. The goal is to develop techniques for efficiently analyzing video evidence and detecting anomalies or tampering.
This document provides a resume for Trinh Le Huy including academic and professional experience. He received a PhD in Electronics from LEAT in 2015 with distinction, as well as engineering and bachelor's degrees from Ecole Polytechnique de Nice and Da Nang University of Technology. His experience includes projects at Orange Labs, LEAT, and SenSeOR involving antenna and RF circuit design. He has published papers in international journals and conferences and holds a European patent. His skills include embedded software, RF system characterization, antenna measurement, and circuit design using simulation tools like ADS.
Wide-band spectrum sensing with convolution neural network using spectral cor...IJECEIAES
Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB.
This document discusses telemedicine, including its history starting in 1959, and types such as store-and-forward and interactive. It then summarizes an Indian telemedicine project called Sanjeeva that connects rural clinics to expert centers. The technological infrastructure used includes medical imaging software, network connectivity devices, and video conferencing. It proposes a store-and-forward telemedicine model for Nepal to address health issues, describing the problems and a potential procedure. Finally, it outlines the works of Mahabir Pun who started telemedicine projects connecting remote areas in Nepal to hospitals in Kathmandu.
Taverna is a free and open-source workflow management system that allows researchers to design and execute scientific workflows. It was developed by the University of Manchester to support in silico experiments in biology. Taverna provides a graphical user interface for designing workflows using a variety of distributed data sources and web services without having to learn complex programming. It has been widely adopted by researchers in fields such as biology, healthcare, astronomy, and cheminformatics to automate analysis pipelines and share workflows.
This document discusses a fast lookup table (LUT) based direct digital synthesis (DDA) finite impulse response (FIR) filter system with adaptive coefficients for spectrum sensing in cognitive radio. It begins with an introduction to cognitive radio and spectrum sensing. It then discusses how an FIR system can help with spectrum sensing and describes the DDA algorithm. The document goes on to present the implemented 3-tap fast LUT based DDA FIR block with adaptive multiple coefficient banks and compares its performance to other FIR filter designs. It shows that the implementation reduces delay, LUT usage, and resource utilization.
This document discusses using multimedia technologies for healthcare applications. It proposes a system for remote markerless human gait tracking over wireless networks for medical diagnosis and prognosis. The system uses video content analysis techniques like background subtraction and contextual classification to extract the human gait region from video frames without needing specialized equipment. It then jointly optimizes video encoding and wireless transmission to prioritize sending the extracted human gait region while meeting quality of service requirements over bandwidth-limited wireless networks. Experimental results using H.264 video compression demonstrate the effectiveness of the proposed remote healthcare system for applications like telemedicine.
This document provides a summary of Andrew Heuze's education and career experience. He received a BSc in Biological Sciences in 2001 and has over 20 years of experience in life sciences research, primarily in assay development and high-throughput screening. His career includes roles at AstraZeneca from 1996-2012 developing cell-based and biochemical assays and running primary screens of over 1 million compounds. He currently works at Thermo Fisher Scientific running biochemical screens for pharmaceutical companies.
My updated resume for (UMTS, WCDMA, LTE-Advanced 4G and 5G, SDN and NVF) wireless engineering.
Please feel free to contact me if you have any questions.
This document is a resume for Gaurav Gandhi, a Biomedical Engineer with over 3 years of experience in research and development. He has a Master's degree in Biomedical Engineering from Drexel University and is currently working on developing medical devices at Artann Labs, including an ultrasonometer for skeletal assessment and a method for enhancing brain drug delivery using acoustic techniques. He is proficient in various engineering and programming skills and seeks opportunities in the medical device industry.
Review of Deep Neural Network Detectors in SM MIMO Systemijtsrd
A deep neural network detector for SM MIMO has been proposed. Its detection principle is deep learning. For this a neural network must be trained first, and then used for detection purpose. It doesn’t need any channel model and instantaneous channel state information CSI . It can provide better bit error performance compared with conventional viterbi detector VD and also it can detect any length of sequences. For a MIMO system, the channel estimation complexity can be avoided. It can detect in real time as arrives the receiver. The main benefit is it can be used where the channel model is difficult to design and also the channel is continuously varying with time. Ruksana. P | Radhika. P "Review of Deep Neural Network Detectors in SM-MIMO System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30535.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30535/review-of-deep-neural-network-detectors-in-smmimo-system/ruksana-p
Dr. Hatem Yousry El-Sayed is seeking an innovative research or teaching position where he can apply his academic background and experience. He has a PhD in electronics and communications engineering from Ain Shams University in Egypt. His research interests include digital signal processing system design, cognitive radio networks, and spectrum sensing algorithms. He has taught courses in electronic circuits, communications systems, digital signal processing, VLSI design, and PLC programming.
Attention gated encoder-decoder for ultrasonic signal denoisingIAESIJAI
Ultrasound imaging is one of the most widely used non-destructive testing methods. The transducer emits pulses that travel through the imaged samples and are reflected by echo-forming impedance. The resulting ultrasonic signals usually contain noise. Most of the traditional noise reduction algorithms require high skills and prior knowledge of noise distribution, which has a crucial impact on their performances. As a result, these methods generally yield a loss of information, significantly influencing the final data and deeply limiting both sensitivity and resolution of imaging devices in medical and industrial applications. In the present study, a denoising method based on an attention-gated convolutional autoencoder is proposed to fill this gap. To evaluate its performance, the suggested protocol is compared to widely used methods such as butterworth filtering (BF), discrete wavelet transforms (DWT), principal component analysis (PCA), and convolutional autoencoder (CAE) methods. Results proved that better denoising can be achieved especially when the original signal-to-noise ratio (SNR) is very low and the sound waves’ traces are distorted by noise. Moreover, the initial SNR was improved by up to 30 dB and the resulting Pearson correlation coefficient was maintained over 99% even for ultrasonic signals with poor initial SNR.
This document is a resume for Chenhui Hu, a PhD candidate in Electrical Engineering at Harvard University. It summarizes his education, research experience, publications, awards, and other qualifications. He has conducted research in machine learning, signal processing, and wireless networks. Some of his accomplishments include improving Alzheimer's disease classification accuracy by over 24% using graph-signal processing and deep learning, and reducing brain atrophy progression prediction error by 60% using decomposed sparse vector-autoregression models.
"Development of a EEG-Based Biometric Authentication & Security System"
presebnted the poster in my university tech fiesta- 2016
I haven't developed it.. but still working on it.
If anyone interested please knock me at
Facebook:
https://www.facebook.com/mubin.hasan.33
This doctoral thesis comprises a summary of novel results considering (1) channel selection in a cognitive radio system (CRS) using history information and (2) power allocation in a selected frequency band assuming a fading channel. Both can be seen as methods to manage interference between in-system users as well as to the users of other systems operating in the same geographical area and frequency band. Realization of CRSs that are using various methods to obtain information about environment and making intelligent decisions based on that information requires the use of adaptive transmission. Adaptive techniques proposed in this thesis enable efficient operation of CRSs in varying radio environment.
History information and learning are essential factors to consider in the CRS design. Intelligent use of history information affects throughput, collisions and delays since it helps to guide the sensing and channel selection processes. In contrast to majority of approaches presented in the literature, this thesis proposes a classification-based prediction method that is not restricted to a certain type of traffic. Instead, it is a general method that is applicable to a variety of traffic classes. The work develops an optimal prediction rule for deterministic traffic pattern and maximum likelihood prediction rule for exponentially distributed traffic patterns for finding channels offering the longest idle periods for secondary operation. Series of simulations were conducted to show the general applicability of the rule to a variety of traffic models. In addition, the thesis develops a method for traffic pattern classification in predictive channel selection. Classification-based prediction is shown to increase the throughput and reduce the number of collisions with the primary user up to 70% compared to the predictive system operating without classification.
In terms of the power allocation work, the thesis defines the transmission power limit for secondary users as a function of the detection threshold of a spectrum sensor as well as investigates theoretical water-filling and truncated inverse power control methods. The methods have been optimized using rational decision theory concepts. The main focus has been on the development and performance comparison of practical inverse power control methods for constant data rate applications. One of the key achievements of the work is the development of the filtered-x LMS (FxLMS) algorithm based power control. It can be seen as a generalized inverse control to be used in power control research, giving a unified framework to several existing algorithms as well.
Zane Ricks is a mechanical and biomedical engineer with experience in technical support, tutoring, research, and clinical trials. He has over 10 years of experience in various roles including technical support, tutoring engineering and math students, directing pre-clinical research on neural stimulation, and coordinating human clinical trials. Ricks has a Master's degree in Biomedical Engineering from Vanderbilt University and a Bachelor's degree in Mechanical Engineering from the University of Nevada, Reno. He has strong skills in communication, analytics, computer programs, and technical areas relevant to engineering.
Philip "Sam" Davis is pursuing a PhD in electrical engineering from New Mexico State University with a focus on signal processing, embedded programming, and machine learning. He has extensive academic and research experience in these areas, including coursework, research projects analyzing EEG signals to predict human perceptions, and internships. His skills include signal processing, machine learning, embedded systems, and software/hardware design. He is currently finishing his dissertation and has published several papers in the field.
This document provides an overview of a project report on the design and implementation of a multi-channel pseudolite GPS baseband module. The report was submitted in partial fulfillment of an M.Tech degree in information technology with a specialization in satellite communication and space systems. The work was carried out at the Central Electronics Engineering Research Institute under the supervision of Dr. Kota Solomon Raju from January to June 2011.
Ambuj Srivastava is an M.Tech student at IIT Guwahati studying Biotechnology. He has a B.Sc. in Chemistry, Botany and Zoology from Chhatrapati Shahu Ji Maharaj University and an M.Sc. in Bioinformatics also from Chhatrapati Shahu Ji Maharaj University. His M.Tech project focuses on analyzing and developing protein-protein docking algorithms under the supervision of Dr. Shankar Prasad Kanaujia. He has work experience developing software systems for library book management, hotel management, and bioinformatics tools for restriction site prediction.
The Telecommunication Networks and Integrated Services (TNS) Laboratory is located within the Department of Digital Systems at the University of Piraeus in Greece. The TNS Laboratory conducts research and development in areas related to telecommunication networks and services. It is staffed by 3 faculty members, 4 senior research engineers, 5 PhD students, and 25 research/software engineers. Past research activities included projects related to broadband networks, wireless access infrastructures, core networks, and services. Ongoing research focuses on projects involving cognitive networks, self-management in future networks, and experimentation platforms related to cognitive radio and network management architectures.
This document provides a resume for Trinh Le Huy including academic and professional experience. He received a PhD in Electronics from LEAT in 2015 with distinction, as well as engineering and bachelor's degrees from Ecole Polytechnique de Nice and Da Nang University of Technology. His experience includes projects at Orange Labs, LEAT, and SenSeOR involving antenna and RF circuit design. He has published papers in international journals and conferences and holds a European patent. His skills include embedded software, RF system characterization, antenna measurement, and circuit design using simulation tools like ADS.
Wide-band spectrum sensing with convolution neural network using spectral cor...IJECEIAES
Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB.
This document discusses telemedicine, including its history starting in 1959, and types such as store-and-forward and interactive. It then summarizes an Indian telemedicine project called Sanjeeva that connects rural clinics to expert centers. The technological infrastructure used includes medical imaging software, network connectivity devices, and video conferencing. It proposes a store-and-forward telemedicine model for Nepal to address health issues, describing the problems and a potential procedure. Finally, it outlines the works of Mahabir Pun who started telemedicine projects connecting remote areas in Nepal to hospitals in Kathmandu.
Taverna is a free and open-source workflow management system that allows researchers to design and execute scientific workflows. It was developed by the University of Manchester to support in silico experiments in biology. Taverna provides a graphical user interface for designing workflows using a variety of distributed data sources and web services without having to learn complex programming. It has been widely adopted by researchers in fields such as biology, healthcare, astronomy, and cheminformatics to automate analysis pipelines and share workflows.
This document discusses a fast lookup table (LUT) based direct digital synthesis (DDA) finite impulse response (FIR) filter system with adaptive coefficients for spectrum sensing in cognitive radio. It begins with an introduction to cognitive radio and spectrum sensing. It then discusses how an FIR system can help with spectrum sensing and describes the DDA algorithm. The document goes on to present the implemented 3-tap fast LUT based DDA FIR block with adaptive multiple coefficient banks and compares its performance to other FIR filter designs. It shows that the implementation reduces delay, LUT usage, and resource utilization.
This document discusses using multimedia technologies for healthcare applications. It proposes a system for remote markerless human gait tracking over wireless networks for medical diagnosis and prognosis. The system uses video content analysis techniques like background subtraction and contextual classification to extract the human gait region from video frames without needing specialized equipment. It then jointly optimizes video encoding and wireless transmission to prioritize sending the extracted human gait region while meeting quality of service requirements over bandwidth-limited wireless networks. Experimental results using H.264 video compression demonstrate the effectiveness of the proposed remote healthcare system for applications like telemedicine.
This document provides a summary of Andrew Heuze's education and career experience. He received a BSc in Biological Sciences in 2001 and has over 20 years of experience in life sciences research, primarily in assay development and high-throughput screening. His career includes roles at AstraZeneca from 1996-2012 developing cell-based and biochemical assays and running primary screens of over 1 million compounds. He currently works at Thermo Fisher Scientific running biochemical screens for pharmaceutical companies.
My updated resume for (UMTS, WCDMA, LTE-Advanced 4G and 5G, SDN and NVF) wireless engineering.
Please feel free to contact me if you have any questions.
This document is a resume for Gaurav Gandhi, a Biomedical Engineer with over 3 years of experience in research and development. He has a Master's degree in Biomedical Engineering from Drexel University and is currently working on developing medical devices at Artann Labs, including an ultrasonometer for skeletal assessment and a method for enhancing brain drug delivery using acoustic techniques. He is proficient in various engineering and programming skills and seeks opportunities in the medical device industry.
Review of Deep Neural Network Detectors in SM MIMO Systemijtsrd
A deep neural network detector for SM MIMO has been proposed. Its detection principle is deep learning. For this a neural network must be trained first, and then used for detection purpose. It doesn’t need any channel model and instantaneous channel state information CSI . It can provide better bit error performance compared with conventional viterbi detector VD and also it can detect any length of sequences. For a MIMO system, the channel estimation complexity can be avoided. It can detect in real time as arrives the receiver. The main benefit is it can be used where the channel model is difficult to design and also the channel is continuously varying with time. Ruksana. P | Radhika. P "Review of Deep Neural Network Detectors in SM-MIMO System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30535.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30535/review-of-deep-neural-network-detectors-in-smmimo-system/ruksana-p
Dr. Hatem Yousry El-Sayed is seeking an innovative research or teaching position where he can apply his academic background and experience. He has a PhD in electronics and communications engineering from Ain Shams University in Egypt. His research interests include digital signal processing system design, cognitive radio networks, and spectrum sensing algorithms. He has taught courses in electronic circuits, communications systems, digital signal processing, VLSI design, and PLC programming.
Attention gated encoder-decoder for ultrasonic signal denoisingIAESIJAI
Ultrasound imaging is one of the most widely used non-destructive testing methods. The transducer emits pulses that travel through the imaged samples and are reflected by echo-forming impedance. The resulting ultrasonic signals usually contain noise. Most of the traditional noise reduction algorithms require high skills and prior knowledge of noise distribution, which has a crucial impact on their performances. As a result, these methods generally yield a loss of information, significantly influencing the final data and deeply limiting both sensitivity and resolution of imaging devices in medical and industrial applications. In the present study, a denoising method based on an attention-gated convolutional autoencoder is proposed to fill this gap. To evaluate its performance, the suggested protocol is compared to widely used methods such as butterworth filtering (BF), discrete wavelet transforms (DWT), principal component analysis (PCA), and convolutional autoencoder (CAE) methods. Results proved that better denoising can be achieved especially when the original signal-to-noise ratio (SNR) is very low and the sound waves’ traces are distorted by noise. Moreover, the initial SNR was improved by up to 30 dB and the resulting Pearson correlation coefficient was maintained over 99% even for ultrasonic signals with poor initial SNR.
This document is a resume for Chenhui Hu, a PhD candidate in Electrical Engineering at Harvard University. It summarizes his education, research experience, publications, awards, and other qualifications. He has conducted research in machine learning, signal processing, and wireless networks. Some of his accomplishments include improving Alzheimer's disease classification accuracy by over 24% using graph-signal processing and deep learning, and reducing brain atrophy progression prediction error by 60% using decomposed sparse vector-autoregression models.
"Development of a EEG-Based Biometric Authentication & Security System"
presebnted the poster in my university tech fiesta- 2016
I haven't developed it.. but still working on it.
If anyone interested please knock me at
Facebook:
https://www.facebook.com/mubin.hasan.33
This doctoral thesis comprises a summary of novel results considering (1) channel selection in a cognitive radio system (CRS) using history information and (2) power allocation in a selected frequency band assuming a fading channel. Both can be seen as methods to manage interference between in-system users as well as to the users of other systems operating in the same geographical area and frequency band. Realization of CRSs that are using various methods to obtain information about environment and making intelligent decisions based on that information requires the use of adaptive transmission. Adaptive techniques proposed in this thesis enable efficient operation of CRSs in varying radio environment.
History information and learning are essential factors to consider in the CRS design. Intelligent use of history information affects throughput, collisions and delays since it helps to guide the sensing and channel selection processes. In contrast to majority of approaches presented in the literature, this thesis proposes a classification-based prediction method that is not restricted to a certain type of traffic. Instead, it is a general method that is applicable to a variety of traffic classes. The work develops an optimal prediction rule for deterministic traffic pattern and maximum likelihood prediction rule for exponentially distributed traffic patterns for finding channels offering the longest idle periods for secondary operation. Series of simulations were conducted to show the general applicability of the rule to a variety of traffic models. In addition, the thesis develops a method for traffic pattern classification in predictive channel selection. Classification-based prediction is shown to increase the throughput and reduce the number of collisions with the primary user up to 70% compared to the predictive system operating without classification.
In terms of the power allocation work, the thesis defines the transmission power limit for secondary users as a function of the detection threshold of a spectrum sensor as well as investigates theoretical water-filling and truncated inverse power control methods. The methods have been optimized using rational decision theory concepts. The main focus has been on the development and performance comparison of practical inverse power control methods for constant data rate applications. One of the key achievements of the work is the development of the filtered-x LMS (FxLMS) algorithm based power control. It can be seen as a generalized inverse control to be used in power control research, giving a unified framework to several existing algorithms as well.
Zane Ricks is a mechanical and biomedical engineer with experience in technical support, tutoring, research, and clinical trials. He has over 10 years of experience in various roles including technical support, tutoring engineering and math students, directing pre-clinical research on neural stimulation, and coordinating human clinical trials. Ricks has a Master's degree in Biomedical Engineering from Vanderbilt University and a Bachelor's degree in Mechanical Engineering from the University of Nevada, Reno. He has strong skills in communication, analytics, computer programs, and technical areas relevant to engineering.
Philip "Sam" Davis is pursuing a PhD in electrical engineering from New Mexico State University with a focus on signal processing, embedded programming, and machine learning. He has extensive academic and research experience in these areas, including coursework, research projects analyzing EEG signals to predict human perceptions, and internships. His skills include signal processing, machine learning, embedded systems, and software/hardware design. He is currently finishing his dissertation and has published several papers in the field.
This document provides an overview of a project report on the design and implementation of a multi-channel pseudolite GPS baseband module. The report was submitted in partial fulfillment of an M.Tech degree in information technology with a specialization in satellite communication and space systems. The work was carried out at the Central Electronics Engineering Research Institute under the supervision of Dr. Kota Solomon Raju from January to June 2011.
Ambuj Srivastava is an M.Tech student at IIT Guwahati studying Biotechnology. He has a B.Sc. in Chemistry, Botany and Zoology from Chhatrapati Shahu Ji Maharaj University and an M.Sc. in Bioinformatics also from Chhatrapati Shahu Ji Maharaj University. His M.Tech project focuses on analyzing and developing protein-protein docking algorithms under the supervision of Dr. Shankar Prasad Kanaujia. He has work experience developing software systems for library book management, hotel management, and bioinformatics tools for restriction site prediction.
The Telecommunication Networks and Integrated Services (TNS) Laboratory is located within the Department of Digital Systems at the University of Piraeus in Greece. The TNS Laboratory conducts research and development in areas related to telecommunication networks and services. It is staffed by 3 faculty members, 4 senior research engineers, 5 PhD students, and 25 research/software engineers. Past research activities included projects related to broadband networks, wireless access infrastructures, core networks, and services. Ongoing research focuses on projects involving cognitive networks, self-management in future networks, and experimentation platforms related to cognitive radio and network management architectures.
1. BEN JABEUR Taouk, PhD
Civil status, born on February 11th, 1978, Married, Tunisian Citizenship
RESEARCH SCIENTIST
Qatar Mobility Innovations Center (QMIC), Qatar Science Technology Park, P.O. Box: 210531, Doha, Qatar
Phone: (+974) 4459 2718 | Mobile: (+974) 3303 2036 | taoufikb@qmic.com
Fields of Interest
Signal and image processing, applied mathematics, Optimization, Detection, Pattern
recognition. De-noising algorithms, Wavelet and time-frequency analysis, Short Time-
Fourier transform (STFT). OFDM systems, IR-UWB, equalization, Channel shortening,
Wireless Sensor Networks (WSN), Passive RFID system. MIMO systems, Filtering,
Blind source separation, Adaptive algorithms. Non-stationary physiological signals: Fetal
Movement signals, EEG signals, HRV signals, etc.
Education
20052009 Ph.D. in Signal Processing and computer Sciences granted with honours.
Thesis title : Channel shortening techniques in OFDM system
Institution: LIPADE, Paris Descartes University, France
Advisors: Pr. Madeleine BONNET and Dr Karim ABED-MERAIM
20042005 M.Sc. Research Master Degree on Mathematical and Computer Sciences. Specialization
in Signal Processing. Paris Descartes University, France
20032004 M.Sc. Professional Master Degree on Statistical and Computer Sciences. Bretagne-Sud
University, France
20022003 M.Sc. Professional Master Degree on Applied Mathematics. Bretagne-Sud University,
France
1998-2001 Bachelor Degree on Applied Mathematics and Computer Sciences. Paris Descartes
University, France
Languages
Arabic uent (mother tongue)
French uent
English good level
Computers
Operating systems Linux and Windows
Programming languages Matlab, C/C++, Scilab, Pascal, Splus, SAS, SQL
Software Latex, Megawave2, CATIAV5, DELMIA, Hygens
2. Work Experience
Research Scientist
06/2014present Research Scientist at QMIC, Qatar
Analysis of the eect of the multi-path channel on the continuous waves in the passive
RFID system
On The Use Of Pre-equalization To Enhance The Passive UHF RFID Communication
Under Multipath Channel Fading
PostDoctoral Fellow
201105/2014 PostDoctoral Fellow at Qatar University, Qatar
Time-Frequency analysis, Wavelet de-noising Algorithm and Matching Pursuit
Fetal Movement signals, EEG signals and HRV signals
Filtering, TFD designing and Detection algorithms
20102011 PostDoctoral Fellow at Reims University, France
Wireless Sensor Networks, IR-UWB
MB-OFDM, non-coherent and coherent codes, Detection algorithms
Teaching:
20082010 Associate Lecturer at Paris Descartes University, France
Signal Processing
Mathematics for engineering
Fundamental Mathematics
Algorithms and Programming
Microsoft Oce: Word, Excel PowerPoint
20062008 Lecturer (Part time), Paris Descartes University
Algorithms and Programming
Microsoft Oce: Word, Excel PowerPoint
Master's Internship:
2005 (5 months) Research Master (Telecom-ParisTech, Paris, France)
Channel shortening in OFDM system. Supervised by: Pr. Karim ABED-MERAIM
2004 (6 months) Professional Master on Statistical and computer sciences (Institut Français de
Recherche pour l'Exploitation de la Mer (IFREMER) et ACTIMAR, BREST)
Modeling the state of wind from west Africa. Supervised by: Pr. Marc PREVOSTO
2003 (6 months) Professional Master Degree on Applied Mathematics (Geoservices at Blanc-
Mesnil)
Development of an observation prototype of drilled cuttings
International organization
Member of the Institute of Electrical and Electronic Engineers (IEEE).
References
Dr. Abdullah Kadri, Dr. Abdullah Kadri, Senior RD Expert, Qatar Mobility
Innovations Center (QMIC), Qatar, abdullahk@qmic.com, phone: (+974) 4459 2711
Pr. Madeleine BONNET, Pr. Madeleine BONNET, UFR de Mathématiques et
Informatique, Paris Descartes University 45, rue des Saints Pères 75270 PARIS cedex 06
bonnet@math-info.univ-paris5.fr, phone: (+33) 1 4455 3524
Pr. Karim Abed-Meraim, Electrical Engineering, Polytech Orléans, France,
karim.abed-meraim@univ-orleans.fr, phone: (+33) 2 3849 4537
Dr. Alban Goupil, CReSTIC, UFR Sciences Exactes et Naturelles, Moulin de la Housse,
BP 1039, 51687 Reims CEDEX, alban.goupil@univ-reims.fr, phone: (+33) 3 26 91 84 22,
Fax: (+33) 3 26 91 31 06
3. Projet du recherche
Member Member NPRP project: 4-726-2-272, (2011-2014), Qatar University, Department of
Electrical Qatar Mobility Innovations Center (QMIC), Qatar
Member Member NPRP project: 09 - 626 - 2 - 243, (2011-2014), Qatar University, Department of
Electrical Engineering, Doha, Qatar The University of Queensland, UQ Centre for Clinical
Research, Herston QLD 4029, Australia
Automatic Identication of Fetal Movement using Non-Stationary Signal Processing
Member CMCU Project (2007-2008), Telecom-ParisTech (France) Sup'Com (Tunisia)
Improving the QoS in 4G communication systems
Activités professionnelles
Member of the Institute of Electrical and Electronic Engineers (IEEE).
Journal Reviews: EURASIP Journal on Advances in Signal Processing, Digital Signal Processing, Elsevier
Conf. Reviews: ISSPA 2010, ISSPA 2012, WOSSPA 2011, WOSSPA 2013
Member Conference Organization Committees: Program Committee and Special DSP Forum Co-
Chairs, ISSPA'2012
Research visits
5 months Ecole d'Ingénieur de Tunis, Sup'Com, Tunisia
4 months University of Sharjah, UAE
Publications
PhD Thesis
T. Ben Jabeur Channel shortening techniques in OFDM system, 2009, Institution:
LIPADE, Paris Descartes University, France.
Journal papers
1 B. Boashash, N. Ali Khan and T.Ben-Jabeur , Designing High-Resolution Time-
Frequency Distributions and Linking Image Pattern Recognition Algorithms for Diagnosis
and Abnormality Detection: A Tutorial Review, Accepted under revision, 2014, Digital
Signal Processing
2 B. Boashash, M.S. Khlif, T.Ben-Jabeur , C.E. East, P.B. Colditz, Passive detection of
accelerometer-recorded fetal movements using a timefrequency signal processing approach,
Digital Signal Processing, Volume 25, February 2014, Pages 134-155, ISSN 1051-2004,
http://dx.doi.org/10.1016/j.dsp.2013.10.002.
3 T. Ben-Jabeur , K. Abed-Meraim, H. Boujemaa, Channel shortening techniques for
dierential encoded OFDM, Physical Communication, Volume 5, Issue 1, March 2012, Pages
47-60, ISSN 1874-4907, http://dx.doi.org/10.1016/j.phycom.2011.04.002.
4 T. Ben-Jabeur, and A. Kadri, High-energy Concentration Quadratic TFD for detection
of the normality and abnormality of the non-stationary physiological signals, In progress,
2014, to submit to IEEE signal processing transaction.
4. Conference papers
1 T. Ben-Jabeur and A. Kadri, Enhancing Passive UHF RFID Communication in
Multipath Fading Channels using TimeVarying Continuous Wave Signals, Submitted to
IEEE WCNC conference, 2015
2 T. Ben-Jabeur and A. Kadri, On The Use Of Pre-equalization To Enhance The Passive
Uhf Rd Communication Under Multipath Channel Fading, Qatar Foundation Annual
Research Forum Proceedings: Vol. 2014, DOI: 10.5339/qfarc.2014.ITPP0445, (Poster)
3 T. Ben-Jabeur and B. Boashash, Design Of A New Wavelet-Based Pre-processing Stage
For Improved Time-Frequency Detection Of Fetal Movements, Qatar Foundation Annual
Research Forum Proceedings: Vol. 2013, BIOP 068. DOI: 10.5339/qfarf.2013.BIOP-068,
(Poster)
4 B. Boashash and T. Ben-Jabeur, Design of a high-resolution separable-kernel quadratic
TFD for improving newborn health outcomes using fetal movement detection, Information
Science, Signal Processing and their Applications (ISSPA), 2012 11th International
Conference on.
5 M.S.H. Khlif, B. Boashash, S. Layeghy T. Ben-Jabeur, P.B. Colditz, , C. East, A
passive DSP approach to fetal movement detection for monitoring fetal health, Information
Science, Signal Processing and their Applications (ISSPA), 2012 11th International
Conference on , vol., no., pp.71,76, 2-5 July 2012, doi: 10.1109/ISSPA.2012.6310647
6 T. Ben-Jabeur , B. Boashash, Design of Quadratic Time Frequency Distribution and
Application to the Analysis and Detection of Fetal Movements, Qatar Foundation Annual
Research Forum Proceedings, 2012
7 M. S. Khlif, B. Boashash, S. Layeghy, T. Ben-Jabeur , M. Mesbah, , East, C.,
Colditz, P., Time-frequency characterization of tri-axial accelerometer data for fetal
movement detection, Signal Processing and Information Technology (ISSPIT), 2011 IEEE
International Symposium on , vol., no., pp.466,471, 14-17 Dec. 2011
8 T. Ben-Jabeur, B Boashash,Design of Quadratic Time Frequency Distribution and
Application to the Analysis and Detection of Fetal Movements, Qatar Foundation Annual
Research Forum Proceedings, 2011
9 T. Ben-Jabeur, A. Goupil, Gelle, G., Pre-equalization for IR-UWB system, Advanced
Technologies for Communications (ATC), 2011 International Conference on ,Vietnam, vol.,
no., pp.244,247, 2-4 Aug. 2011, doi: 10.1109/ATC.2011.6027476
10 T. Ben-Jabeur, K. Abed-Meraim, H. Boujemaa, Blind channel shortening for OFDM
system using Alamouti code, IEEE Internationa Workshop on Signal Processing Advances
in Wireless Communications, (SPAWC), 2010
11 T. Ben-Jabeur, K. Abed-Meraim, H. Boujemaa, Combined channel shortening and source
separation in MIMO-OFDM systems, Information Science and Signal Processing, ISSPA,
Kuala-Lumpur - Malaysia, 10-13 May, 2010
12 T. Ben-Jabeur, K. Abed-Meraim, H. Boujemaa, Blind channel shortening in ZP-OFDM
systems with controlled TIR quality, European Signal Processing Conference, (EUSIPCO),
Glasgow, Scotland, August 24-28, 2009
13 T. Ben-Jabeur, K. Abed-Meraim, H. Boujemaa, Blind channel shortening in MIMO-OFDM
systems using single-block dierential modulation International Wireless Communi-
cations and Mobile Computing Conference (IWCMC MIMO Systems Symposium), Leipzig,
Germany, June 21-24, 2009
5. 14 T. Ben-Jabeur, K. Abed-Meraim, H. Boujemaa, A new blind channel shortening for
dierential encoded OFDM system, IEEE International Workshop on Signal Processing
Advances in Wireless Communications, (SPAWC), Recife, Brazil, July 06-09, 2008
15 T. Ben-Jabeur, K. Abed-Meraim, H. Boujemaa, Blind channel shortening in OFDM
system using nulltones and cyclic prex, IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP), Las Vegas, USA, March 30 - April 4 2008
16 T. Ben-Jabeur, K. Abed-Meraim, Raccourcissement du canal dans un système OFDM
(Poster), International Summer School On Signal Processing and its Applications,
(ISSSPA) Boumerdès, Algeria, June 30 - July 04, 2007
17 T. Ben-Jabeur, K. Abed-Meraim and M. Bonnet, Channel shortening in OFDM System
with controlled TIR quality, Information Science and Signal Processing, ISSPA, Sharjah,
United Arab Emirates, February 2007