bluuebunny/crossref_metadata_2025 huggingface dataset:
A neural network based lexical stress pattern classifier
Authors: Shahin Mostafa , Ahmed Beena , Ballard Kirrie
Published: October 2012

Background and Objectives:
In dysprosodic speech, the prosody does not match the expected intonation pattern and can result in robotic-like speech, with each syllable produced with equal stress. These errors are manifested through inconsistent lexical stress as measured by perceptual judgments and/or acoustic variables. Lexical stress is produced through variations in syllable duration, peak intensity and fundamental frequency.
The presented technique automatically evaluates the unequal lexical stress patterns Strong-Weak (SW) and Week-Strong (WS) in American English continuous speech production based upon a multi-layer feed forward neural network with seven acoustic features chosen to target the lexical stress variability between two consecutive syllables.

Methods:
The speech corpus used in this work is the PTDB-TUG. Five females and three males were chosen to form a training set and one female and one male for testing. The CMU pronouncing dictionary with lexical stress levels marked was used to assign stress levels to each syllable in all words in the speech corpus.
Lexical stress is phonetically realized through the manipulation of signal intensity, the fundamental frequency (F0) and its dynamics and the syllable/vowel duration. The nucleus duration, syllable duration, mean pitch, maximum pitch over nucleus, the peak-to-peak amplitude integral over syllable nucleus, energy mean and maximum energy over nucleus were calculated for each syllable in the collected speech.
As lexical stress errors are identified by evaluating the variability between consecutive syllables in a word, we computed the pairwise variability index ("PVI") for each acoustic measure. The PVI for any acoustic feature f_i is given by:
PVI_i= (f_i1-f_i2)/(( f_i1+f_i2)/2)(1), where f_i1,f_i2 are the acoustic features of the first and second syllables consecutively.
A multi-layer feed forward neural network which consisted of input, hidden and output layers was used to classify the stress patterns in the words in the database.

Results:
The presented system had an overall accuracy of 87.6%. It correctly classified 92.4% of the SW stress patterns and 76.5% of the WS stress pattern.

Conclusions:
A feed-forward neural network was used to classify between the SW and WS stress patterns in American English continuous speech with overall accuracy around 87 percent.
Energy saving mechanism in multiantenna LTE systems
Authors: Imran Reema , Nizar Zorba , Kubbar Osama , Verikoukis Christos
Published: October 2012

Long Term Evolution (LTE) supports closed-loop MIMO techniques to improve its performance; however, in order to exploit the multiuser MIMO channel capabilities, the design of an efficient MAC scheme that supports MU-MIMO is essential and is still an open issue in the literature. This work proposes a novel, energy-efficient MAC scheme for LTE, which aims to achieve simultaneous downlink transmissions to multiple users through the deployment of a low-complexity beamforming technique at the physical layer. Our proposed scheme benefits from the multiuser gain of the MIMO channel and the multiplexing gain of the Multibeam Opportunistic Beamforming (MOB) technique, not only to improve the system throughput but also to provide an energy efficient wireless network. We show that our proposed scheme can provide good energy saving performance at the eNB, where the mathematical expressions for performance evaluation in terms of throughput and saved energy are also presented.
Multiscale denoising of biological data: A comparative analysis
Authors: Nounou Mohamed , Nounou Hazem , Meskin Nader , Datta Aniruddha
Published: October 2012

With the advancements in computing and sensing technologies, large amounts of data are collected from various biological systems. These data are a rich source of information about the biological systems they represent. For example, time-series metabolic data can be used to construct dynamic genetic regulatory network models, which can be used not only to better understand the interactions among different genes inside a cell, but also to design intervention strategies that can cure or manage major diseases. Also, copy number data can be used to determine the locations and extent of aberrations in chromosome sequences which are associated with many diseases such as cancer. Unfortunately, measured biological data are usually contaminated with errors that mask the important features in the data. Therefore, noisy biological measurements need to be filtered to enhance their usefulness in practice.

Conventional linear low-pass filtering techniques are widely used because they are computationally efficient and can be implemented online. However, they are not effective because they operate on a single scale, meaning that they define a specific frequency, above which all features are considered noise. Real biological data possesses multiscale characteristics, i.e., may contain important features having high frequencies (such as sharp changes) or noise occurring at low frequencies (such as correlated or colored noise). Filtering such data requires multiscale filtering techniques that can account for the multiscale nature of the data.

In this work, different batch as well as online multiscale filtering techniques are used to denoise biological data. These techniques include standard multiscale (SMS) filtering, online multiscale (OLMS) filtering, translation invariant (TI) filtering, and boundary corrected TI (BCTI) filtering. The performances of these techniques are demonstrated and compared to those of some conventional low-pass filters (such as the mean filter and the exponentially weighted moving average filter) using two case studies. The first case study uses simulated dynamic metabolic data, while the second case study uses real copy number data. Simulation results show that significant improvement can be achieved using multiscale filtering over conventional filtering techniques.
A hybrid word alignment approach to build bilingual lexicons for English-Arabic machine translation
Authors: Semmar Nasredine , Bouamor Dhouha , Jaoua Ali Mohamed , Elloumi Samir , Ferjani Fethi Kilani , Jaam Jihad Mohamad
Published: October 2012

In this paper we propose a hybrid approach to align single words, compound words and idiomatic expressions from English-Arabic parallel corpora. The objective is to develop, improve and maintain automatically translation lexicons. This approach combines linguistic and statistical information in order to improve word alignment results. The linguistic improvements taken into account refer to the use of an existing bilingual lexicon, named entity recognition, grammatical tag matching and detection of syntactic dependency relation between words. Statistical information refers to the number of occurrences of repeated words, their positions in the parallel corpus and their lengths in terms of number of characters. Single-word alignment uses an existing bilingual lexicon, named entities and cognate detection and grammatical tag matching. Compound word alignment consists of establishing correspondences between the compound words of the source sentence and the compound words of the target sentences. A syntactic analysis is applied to the source and target sentences in order to extract dependency relations between words and to recognize compound words. Idiomatic expression alignment starts with a monolingual term extraction for each of the source and target languages, which provides a list of sequences of repeated words and a list of potential translations. These sequences are represented with vectors which indicate their number of occurrences and the number of segments in which they appear. Then, translation relations between the source and target expressions are evaluated with a distance metric. We have evaluated the single and multiword expression aligners using two methods: A manual evaluation of the alignment quality on 1000 pairs of English-Arabic sentences and an evaluation of the impact of this alignment on the translation quality of a machine translation system. The obtained results showed that these aligners, on the one hand, generate a translation lexicon with around 85% precision, and on the other hand, report a gain in BLEU score of 0.20 for the translation quality.
Feature-based method for offline writer identification
Authors: Al-Maadeed Somaya , Hassaine Abdelaali
Published: October 2012

Writer identification consists of identifying the writer of a certain handwritten document and is of high importance in forensic document examination. Indeed, numerous cases over the years have dealt with evidence provided by handwritten documents such as wills and ransom notes.

Automatic methods for writer identification can be divided into codebook-based and feature-based approaches. In codebook-based approaches, the writer is assumed to act as a stochastic generator of graphemes. The probability distribution of grapheme usage is used to distinguish between writers.
Feature-based approaches compare the handwritings according to some geometrical, structural or textural features. Feature-based approaches prove to be efficient and are generally preferred when there is a limited amount of available handwriting. Therefore, we are more interested in this study in this category of approaches.

Writer identification is performed by comparing a query document to a set of known documents and assigning it to the closest document in term of similarity of handwriting. This is done by extracting characterizing features from the documents including: directions, curvatures, tortuosities (or smoothness), chain codes distributions and edge based directional features. These features correspond to probability distribution functions (PDF) extracted from the handwriting images to characterize writer individuality. When matching a query document against any other document, the ¬χ2 distance between their corresponding features is computed. The smaller the distance, the more likely the two documents are written by the same writer. Therefore, the identified writer is the one of the document with the smallest distance to the query document. The writer is said to be correctly identified when the identified writer corresponds to the actual writer of the query document.

The performance has been evaluated on the IAM handwriting dataset, with chain code based features generally outperforming the other features reaching 71% correct identification rate. The combination of all the features lead to 76% correct identification rate. The proposed system also won the music scores writer identification contest reaching 77% identification rate.
The proposed method automatically extracts features used by forensic experts in order to identify writers of handwritten document. The results show that the method is efficient and language-independent.
Detecting forgeries and disguised signatures in online signature verification
Authors: Hassaine Abdelaali , Al-Maadeed Somaya
Published: October 2012

Online signatures are acquired using a digital tablet which provides all the trajectories of the signature, as well as the variation in pressure with respect to time. Therefore, online signature verification achieves higher recognition rates than offline signature verification. Nowadays, forensic document examiners distinguish between forgeries, in which an impostor tries to imitate a given signature of another person and disguised signatures, in which the authentic author deliberately tries to hide his/her identity with the purpose of denial at a later stage. The disguised signatures play an important role in real forensic cases but are not considered in recent literature.
In this study, we used online signatures acquired using a WACOM Intuos4 digitizing tablet with a sampling rate of 200Hz, a resolution of 2000 lines/cm and a precision of 0.25mm. The pressure information is available in 1024 levels. Online signatures contain a set of samples, each sample corresponds to the point coordinates on the digitizing tablet along with the corresponding pressure (Xt; Yt; Pt) where t corresponds to time. From those three basic signals, four other are extracted: distances, angles, speeds and angular speed. In order to compare the questioned signature with the reference signature, the differences between their corresponding features are computed at both the signal level and the histogram level.
This study has been evaluated on ICDAR2009 signature verification competition dataset and a new dataset of online signatures collected at Qatar University (QU-dataset). This dataset contains signatures of genuine signatures, forgeries and disguised of 194 persons. To the extent of our knowledge, this dataset is the only one that contains disguised online signatures. The best individual performing feature for the ICDAR2009 dataset is the pressure histogram difference which reaches 8% equal error rate (EER). The pressure signal difference is the best individual performing feature for the QU-dataset (29% EER). The combination of features led to 7% EER on the ICDAR2009 dataset and 22% EER on the QU-dataset.
This online signature verification system deals with both forgeries and disguised signatures. Several features have been proposed and combined using different classifiers reaching promising performance for disguised signatures detection.
Design Considerations for Content and Personality of a Multi-lingual Cross-Cultural Robot
Authors: Ziadee Micheline , El Behih Nawal , Prakash Lakshmi , Sakr Majd
Published: October 2012

Our aim is to develop a culturally aware robot capable of communicating with people from different ethnic and cultural backgrounds and performing competently in a multi-lingual, cross-cultural context. Our test bed is a female robot receptionist, named Hala, deployed at the reception area in Carnegie Mellon University in Qatar.

Hala answers questions in Arabic and English about people, locations of offices, classrooms and other rooms in the building. She also provides information about the weather, Education City, and her personal life. Our first model, Hala 1.0, was a bilingual robot extending an American model whose personality and utterances conform to the American culture. Three years of interaction logs have shown that 89% of Hala 1.0's interactions were in English. We conjecture that this is due to the robot's poor ability to equally portray both Arabic and American cultures and to its limited Arabic content.

In order for us to investigate cultural factors that bear on communication significantly, we developed Hala 2.0 which is also a bilingual robot designed to be an Arab-American robot with more Arabic features in appearance, expression and interaction. The robot's personality is constructed taking into account the socio-cultural context in which its interactions will take place. To achieve bilingualism we had to create symmetry between Arabic and English linguistic content. Since the robot's utterances were developed primarily in English we resorted to translating them into Arabic and adapting them to the constraints of our socio-cultural context. Since Arabic is a highly inflected language, we adopted the plural case in formulating the robot's replies so as to avoid gender bias.

To improve query coverage, we added word synonyms, including context-related synonyms (exp:هل تحبين عملك؟/ هل يعجبك عملك؟ ) and different formulations for the same question (exp: do you sleep? / do you go to sleep? and هل تنامين؟/ أتنامين؟). Furthermore, based on three years of recorded query logs, we expanded the range of topics that the robot is knowledgeable about by adding 3000 question/answer sentences to increase the robot's capacity for engaging users. All content and utterances were developed to align with the robot's designed personal traits.
Identifying stressful and relaxation activities using an ambulatory monitoring device
Authors: Khan Hira , Ahmed Beena , Choi Jongyoon , Gutierrez-Osuna Ricardo
Published: October 2012

Background and Objective:
The Autonomic Nervous System (ANS) regulates physiologic processes autonomously through the sympathetic (SNS) and parasympathetic systems (PNS) with both working in balance e.g. sympathetic input accelerates heart rate and prepares for emergencies while the parasympathetic slows the heart rate and relaxes the body. Stress can lead to imbalances in these two systems which can harm the human body. Persistent imbalances caused by chronic stress may trigger diseases such as hypertension, diabetes, asthma and depression and also lead to social problems. In this paper we discuss the effectiveness of a wearable physiological monitoring device in identifying the response of subjects to stressful and relaxation activities to monitor the long term impact of stress.

Methods:
To achieve this objective we developed a body sensor network to wirelessly monitor heart rate, respiratory rate and skin conductance. We collected data while subjects performed mental challenges, chosen to measure a range of stress responses interleaved with deep breathing activities, which they also assessed. We examined the data using several measures of heart rate variability--spectral power in the low frequency (HRV-LF) and high frequency range (HRV-HF), mean (AVNN) and standard deviation of successive RR intervals, the portion of RR interval that changes more than 25 msec (pNN25) and the root mean square of successive differences of RR (RMSSD). Respiratory effect was evaluated using normalized respiratory high and low frequency components and their ratio. To assess the impact on the skin conductance, the mean and standard deviation of the slow varying tonic skin conductance level (SCL) and rapidly varying phasic response-skin conductance response (SCR) were computed.

Results:
An analysis of the computed features indicated that not all features were able to accurately identify the impact of stress on the subjects. The HRV and skin conductance measures were more highly correlated to stress levels with best discrimination obtained using AVNN, RMSSD, PNN25, HRV-HF, SCL mean and SCR standard deviation.

Conclusions:
This study has shown that it is possible to extract features from physiological signals that can be transformed into meaningful measures of individual stress.
Real-time online stereo camera extrinsic re-calibration
Authors: Hansen Peter , Browning Brett , Rander Peter , Alismail Hatem
Published: October 2012

Stereo vision is a common sensing technique for mobile robots and is becoming more broadly used in automotive, industrial, entertainment, and consumer products. The quality of range data from a stereo system is highly dependent on the intrinsic and extrinsic calibration of the sensor head. Unfortunately, for deployed systems, drift in extrinsic calibration is nearly unavoidable. Thermal variation and cycling combined with shock and vibration can cause transitory or permanent changes in extrinsics that are not modeled accurately by a static calibration. As a result the quality of the sensor degrades significantly.
We have developed a new approach that provides real-time continuous calibration updates to extrinsic parameters. Our approach optimizes the extrinsic parameters to reduce epipolar errors over one or multiple frames. A Kalman Filter is used to continually refine these parameter estimates and minimize inaccuracies resulting from visual feature noise and spurious feature matches between the left and right images. The extrinsic parameter updates can be used to re-rectify the stereo imagery. Thus, it serves as a pre-processing step for any stereo process ranging, from dense reconstruction to visual odometry.
We have validated our system in a range of environments and stereo tasks and demonstrated it at the recent Computer Vision and Pattern Recognition conference. Significant improvements to stereo visual odometry and scene mapping accuracy were achieved for datasets collected using both custom built and commercial stereo heads.
MetaSimulo: An automated simulation of realistic 1H-NMR spectra
Authors: Atieh Zeinab , bensmail halima
Published: October 2012

Qatar is now accumulating important expertise in biomedical data analytics. At QCRI, we are interested in providing for biomedical researchers based on their computational needs and in developing tools for data analytics in biomedical research.

When computers extract patterns and classifiers from a body, they are used to predict new data that helps in defining a prior threat or disease. One non-invasive powerful technique for detecting and quantifying bio-markers linked to diseases (metabolites) is Nuclear Magnetic Resonance (NMR) spectroscopy.

1H NMR spectroscopy is commonly used in the metabolic profiling of biofluids. Metabolites in biofluids are in dynamic equilibrium with those in cells and tissues so their metabolic profile reflects changes in the state of an organism due to disease or environmental effects. The analysis of signals obtained from patients may be performed via methods which incorporate prior knowledge about the metabolites that contribute to the 1H NMR spectroscopic signals, recorded in a metabolite dataset.

This paper presents a novel model and computationally automated approach that allows for the simulation of datasets of NMR spectra in order to test real data analysis techniques, hypotheses and experimental designs. Unlike others, this model generates NMR spectra of biofluids unlimited by the magnetic field or pH. It is simple to implement, requires small storage, and is easy to compute and compare. Moreover, it can treat metabolites with a relatively high number of 1H due to a special technique in programing based on physical properties. This model can open the door wide to a new technique of metabolite quantification and thus a better determination of metabolite concentrations which is the key of disease identification.

The area of NMR expands rapidly and holds great promise in terms of the discovery of potential biomarkers of diseases, such as diabetes, an area of increasing concern in Qatar, and cancer, which is the third cause of death in Qatar.
Empirical evaluation of constraint diagrams notation
Authors: Fetais Noora , Cheng Peter
Published: October 2012

An empirical evaluation of constraint diagrams (CD) as a program specification language is conducted by comparing it to natural language (NL) with computer science students included two experiments, one on the interpretation of CD to evaluate the comprehension of notational system, and the other on the construction of program specifications. The first experiment took the form of a web-based competition in which 33 participants were given instructions and training either on CD or on equivalent NL specification expressions. After each example, they responded to three multiple-choice questions requiring the interpretation of expressions in their particular notation. Although the CD group spent more time on the training and had less confidence, they obtained comparable interpretation scores as the NL group and took less time to answer the questions, although they had no prior experience of CD notation. In the second experiment, which focused on the construction of CD, 20 participants were given instructions and training either on CD or on equivalent NL specification expressions. After each example, they responded to three questions requiring the construction of expressions in their particular notation. We built a mini-editor to allow the construction of the two notations, which automatically logged their interactions. Although the CD group supplied more accurate answers, they spent more time in answering the questions. The NL group supplied answers that were partially correct but with some missing information. Moreover, the CD group had spent more time in training, but their returns to the training examples were fewer than the NL group.
Minimum-selection maximum ratio transmission schemes in underlay cognitive radio systems
Authors: Bouida Zied , Ghrayeb Ali , Qaraqe Khalid
Published: October 2012

Under the scenario of an underlay cognitive radio network, we introduce the concept of minimum-selection maximum ratio transmission (MS-MRT). Inspired by the mode of operation of the minimum-selection generalized selection combining (MS-GSC) technique, the main idea behind MS-MRT is to present an adaptive variation of the existing maximum ratio transmission (MRT) technique. While in the MRT scheme, all the transmit antennas are used for transmission, and only a subset of antennas verifying the interference constraint to the primary receiver in MS-MRT are adaptively selected and optimally beamformed in order to meet a given modulation requirement. The main goal of these schemes is to maximize the capacity of the secondary link while satisfying the bit error rate (BER) requirement and a peak interference constraint to the primary link. The performance of the proposed schemes is analyzed in terms of the average spectral efficiency, the average number of antennas used for transmission, the average delay, and the average BER performance. These results are then compared to the existing bandwidth efficient and switching efficient schemes (BES and SES, respectively). The obtained analytical results are then verified with selected numerical examples obtained via Monte-Carlo simulations. We demonstrate through these examples that the proposed schemes improve the spectral and the delay performance of the SES and BES schemes and fit better to delay sensitive applications. The proposed schemes also offer better processing-power consumption than the MRT schemes since a minimum number of antennas is used for communication in the MS-MRT schemes. The MS-MRT techniques represent power and spectral efficient schemes that can be extended to more practical scenarios. As an example, these schemes can be studied in the context of Long term Evolution (LTE) Networks where adaptive modulation, beamforming, and interference management are of the major enabling Techniques.
Wireless smart home monitoring and surveillance system
Authors: Abumaali Duaa Abdullah
Published: October 2012

Wireless technology such as GSM/GPRS and Wi-Fi has taken on an important role in our daily life. The noticeable improvement of these technologies has led to advancement in home automation, where people can enjoy comfort and secure living places. This project proposes a ZigBee-based cost effective solution to control and monitor home appliances remotely and to enable home security against intrusion in the absence of a homeowner using the wireless sensor network kit from national instruments. A wireless sensor network, connected to a GSM modem, can send and receive alarming and controlling SMSs and remotely control the system using an internet web page. Also a security camera is embedded within the system to send alarm SMS and snap an image in case of a visitor or intruder. The system is implemented and evaluated among different sample applications spanning home security, climate control, hazard alarming, and appliance control with remote access and control features to demonstrate the feasibility and effectiveness of the proposed system. Moreover a small-scale live demo model of the smart house is constructed to proof the concept
Malware.inc: analyzing the security of popular web/mobile frameworks
Authors: Al Haddad Talal , Reddy Manoj , Dalvi Fahim , Singh Baljit , Obeid Ossama , Al Rihawi Rami , Abou Selo Omar , Elgazar Aly , Sans Thierry
Published: October 2012

Background and Objectives:
A new generation of software emerged with mobile devices, cloud computing and the web. New usages come with new security threats, and a new generation of malware (malicious software) is emerging as well. Recent security reports show that these malware are on the increase. The goal of this project is to evaluate the risk of exposure to malware in popular app ecosystems such as Apple iOS, Google Android, Facebook, Google App Engine, Mozilla Firefox and Google Chrome.

Methods:
Eight students from Carnegie Mellon Qatar participated in this project. Each looked at a specific technology (either iOS, Android, Facebook, Google App, Mozilla Firefox or Google Chrome). The researchers learned how to develop applications and, as proof of concept, developed several malware apps that were able to steal user's personal information. One collects logins, passwords and credit card numbers from user's Gmail. One collects user's private information on Facebook and propagates through the victim's friends. One records the "clicking" passwords that users enter on online banking websites. One records keystrokes made on the computer without being detected by existing antivirus. One is an Android app that records people conversations while the phone is on standby mode.

Results:
Based on these experiments, we were able to assess the risks and analyze the security issues of these popular apps that we use everyday. These preliminary results were presented at 6th INTERPOL's Group meeting-MENA Region conference in Doha (March 22nd). We plan to publish the scientific results during the Fall of 2012. As future work, the security expertise gained during this project will allow us to design new security tools to protect users against these new kinds of malware.

Conclusion:
Qatar offers many services related to e-government, e-business, e-education and e-health through web portals and mobile applications. Deploying such a global infrastructure requires a strong security assurance. This project contributes to this vision by developing a local expertise on cyber security.
Performance analysis of cognitive radio multiple-access channels over dynamic fading environments
Authors: Ekin Sabit , Qaraqe Khalid A. , Serpedin Erchin
Published: October 2012

Due to the requirement of high data rates and broad utilization of wireless technologies (e.g., 3G, 4G and beyond), the radio frequency (RF) spectrum has become a very limited resource for modern wireless communication systems. Studies indicate that the spectrum is being under-utilized. As a promising solution, cognitive radio (CR) is an encouraging candidate to achieve more efficient RF spectrum utilization.

The previous studies motivate us to utilize the dynamic channel fading model (hyper-fading) to perform a unified analysis for CR multiple-access channel (CR-MAC) networks. Since the nature of the CR networks is multiuser communication, deliberating CR-MAC is more pertinent than point-to-point communication systems. The objective is to maximize the capacity of CR-MAC network over hyper-fading channels under both secondary user's (SU's) transmit power (TP) and interference temperature (IT) constraints.

Multiple SUs transmit to the secondary base station under the TP and IT constraints. In order to perform a general analysis, a theoretical dynamic fading model termed hyper-fading model, which is suitable to the dynamic nature of cognitive radio channel, is considered. The optimal power allocation method (water-filling) is employed to maximize the capacity of CR-MAC for hyper-fading channel with TP and IT constraints.

Throughout the results, the capacity of the hyper-fading channels are compared with that of other channel fading models such as Rayleigh, Nakagami-2, and with an AWGN channel. Furthermore, the impact of the number of SUs on capacity is investigated. Numerical results along with relevant discussions for capacity measure under AWGN, and Rayleigh, Nakagami-2 and hyper-fading channel models are provided to compare the behavior of CR-MAC in these environments.

The results reveal that in the case of very strict IT constraint the water-filling method gives good capacity improvements. This benefit is lost when the IT constraint is relaxed. Through comparison of hyper-fading with other fading environments, it is found that the hyper-fading channel fills the gap in the capacity profiles obtained from the other channel fading types. Further, a study of such a CR-MAC system that undergoes a hyper-fading model can provide unified and comprehensive insights on performance analysis of the CR networks.
New perspectives, extensions and applications of De Bruijn identity
Authors: Park Sangwoo , Serpedin Erchin , Qaraqe Khalid
Published: October 2012

Two significant identities-de Bruijn and Stein-were independently studied in information theory and statistics. De Bruijn identity shows a connection between two fundamental concepts in information theory and signal processing: differential entropy and Fisher information. On the other hand, Stein identity represents a relationship between the expectation of a function and its first-order derivative. Due to their several applications in statistics, information theory, probability theory, and economics, de Bruijn and Stein identities have attracted a lot of interest.

In this study, two different extensions of de Bruijn identity and its relationship with Stein identity will be established. In addition, a number of applications using de Bruijn identity and its extensions will be introduced.

The main theme of this study is to prove the equivalence between de Bruijn identity and Stein identity, in the sense that each identity can be derived from the other one. In a particular case, not only are de Bruijn and Stein identities equivalent, but they are also equivalent to the heat equation identity, which is another important result in statistics.

The second major goal of this study is to extend de Bruijn identity in two distinctive ways. Given an additive non-Gaussian noise channel, the first-order derivative of differential entropy of the output signal is expressed as a function of the posterior mean, and the second-order derivative of differential entropy of the output signal is represented in terms of Fisher information.

The third most important result is to introduce practical applications based on the results mentioned above. First, two fundamental bounds-the Bayesian Cramér-Rao lower bound (BCRLB) and the Cramér-Rao lower bound (CRLB)-in statistical signal processing, and a novel lower bound, tighter than BCRLB, are presented. Second, Costa's entropy power inequality is proved in two distinctive ways. Finally, min-max optimal training sequences for channel estimation and synchronization in the presence of unknown noise distribution are designed.
Performance of digitally modulated RFID energy detector for moisture sensing applications for oil and gas quality monitoring in Qatar
Authors: Nasir Adnan , Ekti Ali Riza , Qaraqe Khalid A , Serpedin Erchin
Published: October 2012

Background & Objectives:
Advances in the radio frequency identification (RFID) technology have made it ubiquitously present. Due to the recently emerging applications, RFIDs have been used in a plethora of different scenarios. Oil and gas industry is no exception; developments in the RFID technology enabled it to be used in oil and gas quality and pipeline infrastructure monitoring through low frequency RFID tags. The presence of moisture in liquefied petroleum gas (LPG) / liquefied natural gas (LNG) pipes can create two main problems. It can degrade the heating ability of the fuel and it can also react with the refrigerant of the liquefied gas to create hydrates, which further lower the quality of the gas. A wireless monitoring system using low frequency RFIDs as moisture sensors can prevent such hazards by detecting the received energy of the transmitted signal to determine the presence of moisture.

Method:
A simple energy detector concept was utilized to exploit the well-known behavior of the RFID's signal reception and energy absorption with varying environments. A decision for the presence of moisture was made using the threshold values of the energy detector.

Results:
The experimental results show that the energy detector approach detects the presence of moisture in the oil and gas system. As another remark, when we decrease the distance between the receiver and transmitter antennas of RFID sensors, one can easily notice the increase on the energy detection performance. Different power levels, modulation schemes and frequency ranges have been analyzed to better understand the energy detection output of RFID system. An optimized solution can be looked at for using the best frequency and input power to maximize the distance between the two antennas.

Conclusion:
In this study, one can see that the RFID system can be used to send information regarding the presence of moisture for quality monitoring of liquefied gas pipes in Qatar. By using this information, we can detect and make decisions on the basis of the energy detection output from the RFID antenna system. This study can be used in a wireless cyber-physical moisture detection system targeted at Qatar's needs.
Outage and SER performance of an opportunistic multi-user underlay cognitive network
Authors: Khan Fahd Ahmed , Tourki Kamel , Alouini Mohamed-Slim , Qaraqe Khalid
Published: October 2012

Having multiple users gives rise to multi-user diversity which can be exploited to give good quality-of-service to each user in the network and also increase the overall capacity of the network. In a spectrum-sharing setting, the multi-user diversity can be exploited; however, this is different from the traditional multi-user case because of the interference power constraints imposed on the secondary users.

In this work, we consider a multi-user underlay cognitive network, where multiple cognitive users concurrently share the spectrum with a primary network, and a single secondary user is selected for transmission. The channel is assumed to have independent but not identical Nakagami-m fading. Considering an interference power constraint and a maximum transmit power constraint on the secondary user, a power allocation policy is derived based on the peak interference power constraint. For this policy the secondary user transmitter (SU-Tx) requires the instantaneous channel state information (CSI) of the link between the SU-Tx and the primary user receiver (PU-Rx). The advantage of this scheme
is that the interference constraint is never violated and there is no loss of performance of the primary network.

The user is selected for transmission based on a greedy scheduling scheme where the user with the highest instantaneous signal-to-noise ratio is chosen for transmission. For this user scheduling scheme, we analyze the uplink performance of the multi-user underlay secondary network in terms of outage probability and symbol error rate (SER). Exact closed-form expressions for the outage performance, moment-generating-function and SER
performance of a multi-user cognitive network are derived. These expressions are obtained for an independent but non-identical distributed (i.n.i.d) Nakagami-m fading channel which is a more generic fading model and can cover a variety of fading environments including Rayleigh fading.

Numerical results based on Monte-Carlo simulations are presented to verify the derived results. It is shown that the SER reduces as the peak interference power constraint is relaxed. Furthermore, as the number of users increases the SER reduces. If the interference power constraint is relaxed the power allocated becomes constant depending on the peak transmit power and thus the SER also becomes constant.
Experimental analysis of energy detection for digitally modulated signals: Indoor measurements
Authors: Ekti Ali Riza , Serpedin Erchin , Qaraqe Khalid A
Published: October 2012

Background & Objectives:
Spectrum sensing is a feature of cognitive radio (CR) systems which is proposed to improve spectral utilization of wireless signals. One of the sensing methods is the non-coherent energy detector, and even though it is computationally more effective than coherent methods, it has critical drawbacks, e.g. requirement of certain signal-to-noise ratio and number of samples. Moreover, type of digital modulation employed also affects the performance of the energy detectors.
Therefore, the performance of the energy detectors is investigated for phase shift keying (PSK) and quadrature amplitude modulated (QAM) signals. Such an analysis is essential, because once the performance of the different modulated signals are understood; next generation wireless networks (NGWNs) and CRs can be designed in such a way that the arduous and expensive planning stage is omitted. This way, a higher data rate can be achieved by using the proper modulation type and/or order for indoor CRs and NGWNs.

Methods:
Instead of false alarm and missed detection analysis, probability mass function vs. energy detection statistics are introduced to better understand the effect of modulation type, order and wireless channel. A measurement setup is developed to consider line-of-sight and non-line-of-sight conditions. Signals are constructed, transmitted and recorded based on the signal model provided. All experiments took place in the Wireless Research Laboratory of the department of ECEN at Texas A&M University at Qatar.

Results:
The results show that performance of the energy detector changes drastically with the digital modulation scheme employed at the transmitter side. Another interesting point is the impact of the used energy detector samples (N). As expected, with the increase in N, the impact of central limit theorem (CLT) can be felt in a clearer way as well.

Conclusions:
By using the experimental results, the design and deployment of future cellular mobile radio systems such as femtocells in Qatar could be optimized. This is crucial, since most communications such as voice/internet/text traffic occur inside buildings especially for rapidly changing network topographies as is the case with the city of Doha. This way, data rate and speed can be optimized for the indoor environments.
Incremental pseudo-conceptual organization of information relative to a domain
Authors: Ismail Sahar , Jaoua Ali
Published: October 2012

Information resources over the World Wide Web are increasingly growing in size and creating immense demand for relevant and context-sensitive information. Thus, information retrieval and knowledge management systems need to exhibit high capacity for the mass of information available and most importantly should be able to handle the constant and rapid changes in information in order to produce results in a reasonable time. In this presentation we would like to highlight the research work done for building a new system for incremental information management relative to a domain of knowledge. The new system (named IPS) utilizes new conceptual methods developed using new formal concept analysis constructs called pseudo concepts for managing incremental information organization and structuring in a dynamic environment. The research work in hand focuses on managing changes in an information store relevant to a specific domain of knowledge attempted through addition and deletion of information. The incremental methods developed in this work should support scalability in change-prone information stores and be capable of producing updates to end users in an efficient time. We will also discuss practical aspects related to the macro and micro information organization built using the new system. These include handling incremental corpus organization for a specific domain, performing context-sensitive text summarization of news articles and news articles features extraction. In addition, initial evaluation results will be also discussed showing the improvement in execution time and time complexity while maintaining reasonable comparable quality of incremental structures produced.
Breakthrough coastal research of Qatar as input to geological and reservoir modeling
Authors: Alnouri Sabla Y , Linke Patrick
Published: October 2012

Maximizing recovery in oil and gas fields relies on geological models that realistically portray the spatial complexity, composition, and properties of reservoir units. Present day arid climate coastal systems, like the coastline of Qatar provide analogues for depositional and diagenetic processes that control reservoir quality in ancient reservoirs. Many major reservoirs in Qatar are formed under conditions that are remarkably similar to those shaping the coastlines of today.

Among the major controls on coastal sedimentation patterns are: 1) wind, wave and tidal energy, 2) coastline orientation, 3) relative sea level, 4) depositional relief and 5) sediment sources.

Strong NW prevailing winds (shamal winds) drive shallow marine circulation patterns, creating four very distinct coastal profiles: windward, leeward, oblique, and protected. In addition, winds supply quartz sand to the leeward coast, as the dune fields of Khor Al-Adaid are blown into the sea. Elsewhere, carbonate sands are formed by wave breakdown of skeletal material in the shallow marine environment. These sands are washed ashore to form beaches. The grain size, composition, and dimensions of coastal sands vary due to wave energy.

Coastal deposits are equally affected by high frequency oscillations in sea level. Approximately 8,000 years ago, the sea level was about 3 meters higher than it is currently and the Qatari coastline was up to 15 km inland. Most coastal deposits and sabkhas are relicts of this ancient highstand in sea level. Punctuated sea level drops to present day levels have led to the formation of seaward-stepping spit systems.

Understanding these coastal and near coastal areas, the processes that form them, and developing geologic models based on this understanding, is a focus of the Qatar Center for Coastal Research (QCCR) within ExxonMobil Research Qatar.

The observed spatial complexity and heterogeneity of modern coastal systems are important aspects to be considered for conditioning three-dimensional geological models. The studied modern outcrops along the Qatar coastline are particularly useful as analogs for conditioning subsurface data sets in geologic (static) and reservoir (dynamic) models.
Multiscale investigations leading to the design of a novel Fischer-Tropsch reactor for gas-to-liquid processes
Authors: Elbashir Nimir , Bani Nassr Layal , Elmalik Elfatih , Blank Jan , Hussain Rehan
Published: October 2012

Gas-to-liquid (GTL) projects form an important part of Qatar's energy industry due to the country's extensive natural gas reserves. At present, commercial GTL plants in Qatar account for 36% of the total worldwide GTL production, but suffer from high operational costs due to limitations in the existing Fischer-Tropsch synthesis (FTS) reactor technologies, which are at the heart of the GTL process. Of the two FTS reactor types currently in use commercially, fixed-bed reactors (i.e. gas-phase FTS) offer poor temperature control while slurry-bed reactors (i.e. liquid-phase FTS) suffer from difficult catalyst separation and other challenges. The utilization of supercritical fluids (SCF) as solvents in FTS (SCF-FTS) provide several advantages over the existing commercial technologies. SCF-FTS can improve the heat transfer properties relative to fixed-bed reactors, while also offering high diffusivity of the reactants relative to slurry-bed reactors.

The results presented here summarize multidisciplinary research activities, led by our research team at Texas A&M University at Qatar, in collaboration with top scientists from institutions around the word and supported by an industrial advisory board. The work was funded by different agencies and combined several projects, which have been undertaken over the past four years. This work was unique in that it focused on understanding both the micro- and macro-scale behaviours of the FTS chemistry and reactor. The micro-scale studies enabled better understanding of the reaction mechanism and kinetics, FTS thermodynamics and phase behavior (via experimental and modeling studies), and intra-particle catalyst effectiveness factor. The macro-scale investigations covered: 1) identifying the overall (heat/mass/hydrodynamic) profile inside the reactor, 2) selecting an appropriate supercritical solvent, and 3) building a lab scale reactor unit. The outcome of these studies is that we were able to identify the most applicable solvent(s) while providing a detailed techno-economic and safety evaluation of this process. Furthermore, the overall structure of the separation process for solvent recovery and recycle has been completed based on energy optimization. Currently, we are at the stage of developing an upgraded design for this technology based on the data to be generated from our demo-scale FTS reactor unit.