Lisa G. Huettel

Discrimination mode processing for EMI and GPR sensors for hand-held land mine detection

Signal processing algorithms for hand-held mine detection sensors are described. The goals of the algorithms are to provide alarms to a human operator indicating the likelihood of the presence of a buried mine. Two modes of operations are considered: search mode and discrimination mode. Search mode generates an initial detection at a suspected location and discrimination mode confirms that the suspected location contains a land mine. Search mode requires that the signal processing algorithm generate a detection confidence value immediately at the current sample location and no delay in producing an alarm confidence is tolerable. Search mode detection has a high false-alarm rate. Discrimination mode allows the operator to interrogate the entire suspected location to eliminate false alarms. It does not require that the signal processing algorithm produce an alarm confidence immediately for the current sample location, but rather allows the system to process all the data acquired over the region before producing an alarm. This paper proposes discrimination mode processing algorithms for metal detectors (MDs), or electromagnetic induction sensors (EMIs), ground-penetrating radars (GPRs), and their fusion. The MD discrimination mode algorithm employs a model-based approach and uses the target model parameters to discriminate between mines and clutter objects. The GPR discrimination mode algorithm uses the consistency of detection as well as the shape of the detection peaks over several sweeps to improve the discrimination accuracy. The performances of the proposed algorithms were examined on a dataset collected at a government test site, and performance was compared with baseline techniques. Experimental results showed that the proposed method can reduce the probability of false alarm by as much as 70% at a 100% correct detection rate and performed comparable to the best human operator on a blind test with data collected at approximately 1000 locations.

Fundamentals of ECE: A Rigorous, Integrated Introduction to Electrical and Computer Engineering

The Electrical and Computer Engineering (ECE) Department at Duke University, Durham, NC, is undergoing extensive curriculum revisions that incorporate novel content, organization, and teaching methods. The cornerstone of the new curriculum is a theme-based introductory course, fundamentals of ECE. To introduce students to the major areas of ECE in their first year of study, this course is organized around three concepts: 1) how to interface with the physical world; 2) how to transmit energy and information; and 3) how to extract, interpret, and analyze information. To provide insight and motivation, the course is designed to introduce multiple areas of ECE, emphasizing how they are interrelated and how they contribute to the design and functioning of real-world applications. Also, the course must engage its students, many of whom are evaluating ECE as a prospective major and career. To achieve these goals, the course adopts a unifying theme, tightly couples lecture and laboratory exercises, and includes a laboratory experience that emphasizes design, integration, and real applications. The interactive classroom content and laboratory exercises are developed iteratively so that each course component supports the other, rather than one being dominant and driving the other. As the context focus of the laboratory, a robotic platform enables the exploration of a broad range of ECE concepts, both independently and integrated into an entire system. For their final design project, students form small groups, which in turn combine into larger teams, to create robots that work together to overcome realistic challenges. This paper describes the curricular objectives and key course elements that guide course development, the resulting content and structure of the course, and the assessment data that indicate successful achievement of the curricular goals.

Transcending the traditional: Using tablet PCs to enhance engineering and computer science instruction

Traditional instructional methods present many obstacles to effective teaching and learning in engineering and computer science courses. These include a reliance on text-based or static mediums to convey equation- and graphics-heavy concepts, a disconnect between theoretical lecture presentations and applied laboratory or homework exercises, and a difficulty in promoting collaborative activities that more accurately reflect an engineering approach to problem solving. Additionally, technical courses can suffer, like any other course, when students are not actively engaged in the learning and when instructors cannot gauge student understanding. This project has explored the utility of Tablet PCs for overcoming these challenges within a sample of courses in engineering and computer science. There were three primary questions: which knowledge domains benefit from the use of Tablet PCs; whether observed benefits are derived from Tablet PC-specific activities; and what problems limit the effectiveness of Tablet PCs in educational settings? The evaluation of assessment data using regression approaches demonstrated that Tablet-PC-specific activities had a consistent, meaningful, and positive impact upon engineering and computer science courses.

Sensor fusion of EMI and GPR data for improved land mine detection

It is widely accepted that single sensors cannot simultaneously achieve both high detection rates and low false alarm rates for the landmine detection problem. Thus, in this paper we consider the fusion of two types of sensors, electromagnetic induction (EMI) and ground penetrating radar (GPR). In its most common instantiation, EMI essentially provides metal detection and thus detects mines with high metal content as well as metal debris in the environment. More advanced EMI systems have begun to show potential for discriminating such debris from landmines. GPR is also used for landmine detection since it can detect and identify low-metallic subsurface anomalies. In our previous work, we have shown that a Bayesian detection approach can be applied to EMI and GPR data and provide improvements in false alarm rates. In this paper, we present results that indicate that statistical signal processing techniques can be applied simultaneously to GPR and EMI data and that reductions in false alarm rates can be achieved. We present results for two landmine detection systems, both handheld, and when possible compare the results to those obtained by a human operator who essentially fuses the outputs of the single sensor systems.

A DSP Hardware-Based Laboratory for Signals and Systems

Hardware-based laboratories have been successfully integrated into individual digital signal processing (DSP) courses at many universities. Typically, most hardware-based DSP laboratory experiences are offered to upper-level students and focus on programming the signal processor. While this approach may be ideal for preparing motivated upper-level students for future careers in signal processing, it is not suitable for students with no prior experience in the field. To address this issue, a hardware-based signal processing laboratory, based on the Texas Instruments 6713 DSP starter kit (DSK), suitable for students in an introductory signals and systems course has been developed. After two semesters of implementation, results indicate that student understanding is enhanced and interest levels increase when a DSP hardware-based laboratory is integrated into an introductory signal processing course

A theoretical comparison of information transmission in the peripheral auditory system: normal and impaired frequency discrimination

A theoretical analysis of auditory signal processing and the distortions introduced by various types of hearing impairments can aid in the design and development of digital hearing aids. In this paper, we investigate the differences between normal and impaired auditory processing on a frequency discrimination task by analyzing the responses of a computational auditory model using signal detection theory. Hearing impairments that were simulated included a threshold shift, damage to the outer hair cells, and impaired neural synchrony. We implemented two detectors, one using all of the information in the signal, the other using only the number of neural firings, and used them to generate theoretical predictions of performance. Evaluation of performance differences between theoretical detectors and experimental data allows quantification of both the type of information present in the auditory system and the efficiency of its use. This method of analysis predicted both the trends observed in comparable experimental data and the relation between normal and impaired behavior. Finally, a very simple hearing aid was simulated and the gains in performance in the impaired cases were related to the physiological bases of the impairments. This demonstrates the utility of the proposed approach in the design of more complex hearing aids.

Predicting auditory tone-in-noise detection performance: the effects of neural variability

Collecting and analyzing psychophysical data is a fundamental mechanism for the study of auditory processing. However, because this approach relies on human listening experiments, it can be costly in terms of time and money spent gathering the data. The development of a theoretical, model-based procedure capable of accurately predicting psychophysical behavior could alleviate these issues by enabling researchers to rapidly evaluate hypotheses prior to conducting experiments. This approach may also provide additional insight into auditory processing by establishing a link between psychophysical behavior and physiology. Signal detection theory has previously been combined with an auditory model to generate theoretical predictions of psychophysical behavior. Commonly, the ideal processor outperforms human subjects. In order for this model-based technique to enhance the study of auditory processing, discrepancies must be eliminated or explained. In this paper, we investigate the possibility that neural variability, which results from the randomness inherent in auditory nerve fiber responses, may explain some of the previously observed discrepancies. In addition, we study the impact of combining information across nerve fibers and investigate several models of multiple-fiber signal processing. Our findings suggest that neural variability can account for much, but not all, of the discrepancy between theoretical and experimental data.

Enhanced Auditory Displays for Discriminating Landmines from Clutter Using Electromagnetic Induction Sensors

Landmine detection is primarily performed using electromagnetic induction (EMI) sensors. These sensors detect the presence of metal and convey the information to the sensor operators via an audio signal. Reduction of false alarms from objects that contain metal but are not landmines, i.e. discrimination, is a challenging problem. Recent work on automated algorithms has shown promise towards reducing false alarm rates of EMI sensors. In this study, the audio signal was modified to encode the presence of metal as well as information regarding mine/non-mine belief in order to determine whether the additional information enabled operators to better discriminate mines from clutter. Using data collected from real landmines, we experimentally investigated which perceptual dimensions most effectively convey different aspects of the information contained in the sensor response to a listener. Results indicated that the presence of metal (detection) could be coded in the fundamental frequency of the audio signal, and that mine/non-mine belief (discrimination), determined using an automated algorithm, could be coded in a separate audio dimension. Operators performed better with this audio coding scheme than one where only metal content information was presented via the fundamental frequency of the audio signal.

A theoretical study of information transmission in the auditory system using signal detection theory: frequency discrimination by normal and impaired systems

We have investigated the differences between normal and impaired auditory processing for a frequency discrimination task by analyzing the responses of a computational auditory model using signal detection theory. Two detectors, one using all of the information in the signal, the other using only the number of neural responses, were implemented. An evaluation of the performance differences between the two theoretical detectors and experimental data may provide insight into quantifying the type of information present in the auditory system as well as whether the human auditory system uses this information efficiently. Results support previous hypotheses that, for lowand mid-range frequencies, the auditory system is able to use temporal information to perform frequency discrimination (see Moore, B.C.J., J. Acoust. Soc. Am., vol.54, p.610-19, 1973). The results also suggest that some temporal information is represented in the neural spike train, even at high frequencies However, the ability of the auditory system to use this information deteriorates at higher frequencies.

Integrating Sensing and Information Processing in an Electrical and Computer Engineering undergraduate curriculum

The Department of Electrical and Computer Engineering at Duke University has completed a full-scale redesign of its undergraduate program based on the theme of Integrated Sensing and Information Processing. This theme provides a coherent, overarching framework that links principles of ECE to each other and to real-world engineering problems. The cornerstone of the new ECE curriculum, Fundamentals of Electrical and Computer Engineering, has been designed to provide students with a holistic view of ECE and as a roadmap for the remainder of the curriculum. Each of four follow-on core courses integrates lateral and vertical connections to other courses through the use of thematic examples. Following the five core courses are seven ECE technical electives that include a theme-based culminating design course. Early and pervasive experiences with open-ended design and project-based learning are primary objectives of the curriculum redesign. Regression analyses of course/instructor evaluation data and descriptions of student design project complexity after the curriculum redesign are presented indicating a positive impact of the curriculum redesign on student learning.