Professor and Students Publish Paper

Dr Nabih Jaber and his students Tahmina Gouhar, and Pallavi Kuntumalla have published a paper, entitled “Speech enhancement using new iterative minimum statistics approach” in the 2017 IEEE 30th CCECE conference.
In hands-free mobile communication, speech quality is often degraded due to presence of surrounding noise. This paper introduces an improved version of Minimum Mean Square Error (MMSE) noise estimator. Noise spectrum estimation is a crucial element used in speech recognition systems. Our proposed noise estimation method is based on a popular searching algorithm used in software engineering called Binary Search (BS), which we integrate with First-In First-Out (FIFO) MMSE noise reduction algorithm. In the literature, there is no research addressing the integration of BS algorithm with an active noise cancellation system. The noise spectral minima are computed using BS algorithm which makes it fast and efficient. The proposed algorithm is tested using real time data collected from vehicles running at different speeds. Simulation results are provided, and it is shown that the proposed algorithm outperforms other MMSE algorithms.

ECE Professor and Student Publish Optics Paper

DSC-6947ADr Jinjun Xia and student Ashley Julin have published a paper entitled “Polarization enhanced laser speckle contrast imaging for vascular dynamic study” in the SPIE Digital Library as part of the proceedings of the Dynamics and Fluctuations in Biomedical Photonics XIV conference. SPIE is the international society for the study of optics and photonics. The paper has Digital Object Identifier


ECE Professor Publishes Paper

Title: A quantitative analysis of hands-free speech enhancement using real automobile data
Publisher: IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Abstract: This paper provides a detailed comparison study between three different vehicles’ Bluetooth built-in noise cancellation filter with two widely used techniques in speech enhancement, Spectral Subtraction (SS) and Wiener filtering (WF). The main purpose is to determine if any of these two filters provide superior audio quality over the built-in filter. In literature, several authors have compared the performance of SS to that of WF using, primarily, simulated data, whereas this paper uses real-time data samples collected from cars subjected to a noisy environment with varying sound levels in search of an optimal solution. The cars were driven at different speeds with the windows and fan set to different configurations. In the process of comparing the three noise-canceling algorithms, the collected data were filtered using each filter, and the resulting tracks were analyzed both subjectively and objectively. Overall, SS outperformed WF by canceling more noise and/or conserving speech related frequency peaks. In all cases, but one, both Wiener and SS filters outperformed the built-in filter. The audio tests analyzed subjectively agree with the plot results.

Professor Coauthors IEEE Conference Paper

JABER_NabihDr. Nabih Jaber has coauthored a paper entitled “A quantitative real time data analysis in vehicular speech environment with varying SNR” in the 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP). The abstract is as follows:

The purpose of this paper is to compare the performance of two common filters operating on noisy speech recorded in automobiles travelling at various speeds. The filters are based on Spectral Subtraction (SS) and Kalman Filtering (KF). The literature contains studies based on simulated data whereas this paper uses real time data collected in car’s in search of an optimal solution. The comparisons were based on real recorded samples containing noisy speech signals with durations of approximately 2 minutes each. Different cases of noise levels which represent the most common situations experienced by drivers were created. The different settings used include varying car speeds (e.g., 40 mph, 70 mph), varying fan power, and window positions settings. The study was carried out using three different car models. The measured noisy voice signals were filtered using the different filtering techniques and the resulting filtered signals were compared in the time domain and the frequency domain, both quantitatively and psychometrically. Furthermore, the quantitative analysis approach was applied to the results for more accurate interpretation. Results show that SS outperforms KF in noise reduction, and with much less speech distortion at the different Signal to Noise Ratios (SNRs) tested. The audio test results subjected to human listening are comparable with the simulation results. Overall, SS showed superior performance over KF in vehicular hands-free speech applications.

For more information, see

Wireless Mobile Network Research Funded by DENSO for Undergraduates


In the lab: (L-R) Alula Kassa (undergraduate student), Alekhya Athmakuru (graduate student), Thaimur Abbas Mohammed (graduate student), and Dr. Kun Hua (ECE faculty).

With a $50,000 grant, DENSO North America Foundation is underwriting a research effort that gives LTU undergraduates in mechanical engineering, electrical/computer engineering and civil engineering an interdisciplinary opportunity to develop mobile, wireless sensor networks (MWSN) to help keep drivers safer.

“An MWSN combines vehicle-to-vehicle and vehicle-to-infrastructure connectivity to give drivers 360-awareness of their surroundings,” said Kun Hua, professor of electrical/computer engineering. Along with James Mynderse, who teaches in the department of mechanical engineering, and Nishantha Bandara, who teaches in the department of civil engineering, he is coordinating three different research programs that include nearly 100 students. A number of courses and labs in all three departments will provide students with the knowledge needed to conduct the research.

According to the National Highway Traffic Safety Administration, 5.7 million traffic accidents occurred in 2013 resulting in more than 32,700 fatalities. Intelligent traffic systems, like the ones the students are developing, will improve safety. In addition, information about road conditions and traffic congestion that will be transmitted to drivers also could improve traffic flow and reduce fuel waste.

The grant will give the students hands-on development experience, help build an inter-disciplinary lab and purchase the equipment and supplies necessary for the projects.

Currently in development, the sensor networks are expected to be installed on test vehicles in the summer of 2016.

“Our students are not only learning valuable, marketable skills, but they have the satisfaction of knowing that what they’re creating may actually save lives in the future,” added Hua.


(L-R): Dr. Kun Hua (ECE faculty), Deyuan Qu (graduate student), Yang Wu (graduate student), and Yuchen Lin (graduate student).

Professor Publishes Paper on Vehicular Communications

unnamedDr Nabih Jaber has had a paper accepted by the International Journal of Vehicular Communications, published by Elsevier. The abstract of the paper, entitled “Passive Cooperative Collision Warning (PCCW) MAC designs for reliable vehicular safety messaging”, is as follows:

This paper presents – Passive Cooperative Collision Warning (PCCW), and enhanced-PCCW (EPCCW) protocol designs for safety message reception reliability improvement in Dedicated Short Range Communications (DSRC). PCCW and EPCCW employ a cooperative warning scheme for reduction of collisions, without increasing packet traffic. EPCCW utilizes the physical layer (PHY) properties to create sub-slots for the purpose of further increasing reliability by both avoiding and minimizing probability of collision at slots that would nominally fail. Full analytical derivation of the relative reliability and delay performances for both PCCW and EPCCW protocols is provided. An accurate and complete simulation model is used, which combines an accurate DSRC PHY, MAC and federated mobility model designed using the Simulation of Urban MObility (SUMO) model. Analytical and simulation results agree and show that PCCW and EPCCW protocols significantly improve reliability performance relative to the leading safety messaging protocols. Under high collision scenarios and at optimal number of repetitions, an improvement of up to 40% in reliability is observed, and up to 80% of improvement is achieved at higher load. Improvement in average timeslots delay is also observed that is well within acceptable delay threshold. Thorough simulation results of the proposed protocols are presented under varying message range, coding rates, modulation schemes, channel models, vehicular densities, safety message lifetimes, and transmission frequencies.

The paper can be downloaded at

Professor to Present Conference Paper

JABER_Nabih Dr Nabih Jaber has had a paper accepted for publication in the IEEE NTMS’2014 – the Sixth IFIP International Conference on New Technologies, Mobility and Security. The conference will be held from March 30th to April 2nd at Zayed University, Dubai, UAE. The abstract of the paper entitled “Efficient Home Energy Management System” is as follows:

Consumer domain energy management systems or home energy management system (HEMS) is largely neglected in existing practical smart grid EMS studies. This paper presents a practical HEMS that supports various existing and emerging actors. Some of the proposed features include the support of automatic and manual scheduling and control of the devices, continuous monitoring and efficient notification. The goal of the design is to achieve optimized performance under dynamic situations. For better understanding and implementation of the concepts behind our proposed design, detailed Use Case diagrams of the various actors and their functionalities are presented. A substantial amount of peak shaving/shifting is observed using the proposed application.

Robust Adaptive Software Radio Communication System

Dr Kun Hua and student Robert Reichel in the LTU Wireless Communication Lab.

Article by Dr Kun Hua

The Wireless Communication Lab is now available for undergraduate senior projects and graduate class projects involving Software Defined Radio.

I am using seed grant funding to develop an adaptive and robust software radio communication system with my undergraduate and graduate students. The aim is to generate an automotive embedded system to scan commercial radio stations and automatically select a station that is currently playing music, sports, a talk show, weather information, etc. This would allow users to skip commercials and listen to preferred content continuously without having to manually scan for stations. The system is designed to perform real time analysis of an audio stream through pattern recognition, data mining, nonlinear optimization, signal processing, and embedded techniques. Features and more advanced adaptation algorithms can be implemented at a later stage. In the future, with just one click, you will be able to listen to all live local games — Red Wings, Lions, Pistons, Wolverines, Spartans — whether you are driving along the coast of California or skiing in rural Colorado.