A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

Ensemble fuzzy belief intrusion detection design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

The development of an optimized system of narcotic and explosive contraband mimics for calibration and training of biological detectors

Current commercially available mimics contain varying amounts of either the actual explosive/drug or the chemical compound of suspected interest by biological detectors. As a result, there is significant interest in determining the dominant chemical odor signatures of the mimics, often referred to as pseudos, particularly when compared to the genuine contraband material. This dissertation discusses results obtained from the analysis of drug and explosive headspace related to the odor profiles as recognized by trained detection canines. Analysis was performed through the use of headspace solid phase microextraction in conjunction with gas chromatography mass spectrometry (HS-SPME-GC-MS). Upon determination of specific odors, field trials were held using a combination of the target odors with COMPS. Piperonal was shown to be a dominant odor compound in the headspace of some ecstasy samples and a recognizable odor mimic by trained detection canines. It was also shown that detection canines could be imprinted on piperonal COMPS and correctly identify ecstasy samples at a threshold level of approximately 100ng/s. Isosafrole and/or MDP-2-POH show potential as training aid mimics for non-piperonal based MDMA. Acetic acid was shown to be dominant in the headspace of heroin samples and verified as a dominant odor in commercial vinegar samples; however, no common, secondary compound was detected in the headspace of either. Because of the similarities detected within respective explosive classes, several compounds were chosen for explosive mimics. A single based smokeless powder with a detectable level of 2,4-dinitrotoluene, a double based smokeless powder with a detectable level of nitroglycerine, 2-ethyl-1-hexanol, DMNB, ethyl centralite and diphenylamine were shown to be accurate mimics for TNT-based explosives, NG-based explosives, plastic explosives, tagged explosives, and smokeless powders, respectively. The combination of these six odors represents a comprehensive explosive odor kit with positive results for imprint on detection canines. As a proof of concept, the chemical compound PFTBA showed promise as a possible universal, non-target odor compound for comparison and calibration of detection canines and instrumentation. In a comparison study of shape versus vibration odor theory, the detection of d-methyl benzoate and methyl benzoate was explored using canine detectors. While results did not overwhelmingly substantiate either theory, shape odor theory provides a better explanation of the canine and human subject responses.

Ensemble Fuzzy Belief Intrusion Detection Design

With the rapid growth of the Internet, computer attacks are increasing at a fast pace and can easily cause millions of dollar in damage to an organization. Detecting these attacks is an important issue of computer security. There are many types of attacks and they fall into four main categories, Denial of Service (DoS) attacks, Probe, User to Root (U2R) attacks, and Remote to Local (R2L) attacks. Within these categories, DoS and Probe attacks continuously show up with greater frequency in a short period of time when they attack systems. They are different from the normal traffic data and can be easily separated from normal activities. On the contrary, U2R and R2L attacks are embedded in the data portions of the packets and normally involve only a single connection. It becomes difficult to achieve satisfactory detection accuracy for detecting these two attacks. Therefore, we focus on studying the ambiguity problem between normal activities and U2R/R2L attacks. The goal is to build a detection system that can accurately and quickly detect these two attacks. In this dissertation, we design a two-phase intrusion detection approach. In the first phase, a correlation-based feature selection algorithm is proposed to advance the speed of detection. Features with poor prediction ability for the signatures of attacks and features inter-correlated with one or more other features are considered redundant. Such features are removed and only indispensable information about the original feature space remains. In the second phase, we develop an ensemble intrusion detection system to achieve accurate detection performance. The proposed method includes multiple feature selecting intrusion detectors and a data mining intrusion detector. The former ones consist of a set of detectors, and each of them uses a fuzzy clustering technique and belief theory to solve the ambiguity problem. The latter one applies data mining technique to automatically extract computer users’ normal behavior from training network traffic data. The final decision is a combination of the outputs of feature selecting and data mining detectors. The experimental results indicate that our ensemble approach not only significantly reduces the detection time but also effectively detect U2R and R2L attacks that contain degrees of ambiguous information.

The Development of an Optimized System of Narcotic and Explosive Contraband Mimics for Calibration and Training of Biological Detectors

Current commercially available mimics contain varying amounts of either the actual explosive/drug or the chemical compound of suspected interest by biological detectors. As a result, there is significant interest in determining the dominant chemical odor signatures of the mimics, often referred to as pseudos, particularly when compared to the genuine contraband material. This dissertation discusses results obtained from the analysis of drug and explosive headspace related to the odor profiles as recognized by trained detection canines. Analysis was performed through the use of headspace solid phase microextraction in conjunction with gas chromatography mass spectrometry (HS-SPME-GC-MS). Upon determination of specific odors, field trials were held using a combination of the target odors with COMPS. Piperonal was shown to be a dominant odor compound in the headspace of some ecstasy samples and a recognizable odor mimic by trained detection canines. It was also shown that detection canines could be imprinted on piperonal COMPS and correctly identify ecstasy samples at a threshold level of approximately 100ng/s. Isosafrole and/or MDP-2-POH show potential as training aid mimics for non-piperonal based MDMA. Acetic acid was shown to be dominant in the headspace of heroin samples and verified as a dominant odor in commercial vinegar samples; however, no common, secondary compound was detected in the headspace of either. Because of the similarities detected within respective explosive classes, several compounds were chosen for explosive mimics. A single based smokeless powder with a detectable level of 2,4-dinitrotoluene, a double based smokeless powder with a detectable level of nitroglycerine, 2-ethyl-1-hexanol, DMNB, ethyl centralite and diphenylamine were shown to be accurate mimics for TNT-based explosives, NG-based explosives, plastic explosives, tagged explosives, and smokeless powders, respectively. The combination of these six odors represents a comprehensive explosive odor kit with positive results for imprint on detection canines. As a proof of concept, the chemical compound PFTBA showed promise as a possible universal, non-target odor compound for comparison and calibration of detection canines and instrumentation. In a comparison study of shape versus vibration odor theory, the detection of d-methyl benzoate and methyl benzoate was explored using canine detectors. While results did not overwhelmingly substantiate either theory, shape odor theory provides a better explanation of the canine and human subject responses.

Improved Electronics for the Hall A Detectors at JLab: Summing Modules and VDC Amplifier/Discriminator Cards

Testing of summing electronics and VDC A/D Cards was performed to assure proper functioning and operation within defined parameters. In both the summing modules and the VDC A/D cards, testing for minimum threshold voltage for each channel and crosstalk between neighboring channels was performed. Additionally, the modules were installed in Hall A with input signals from shower detectors arranged to establish a trigger by summing signals together with the use of tested modules. Testing involved utilizing a pulser to mimic PMT signals, a discriminator, an attenuator, a scaler, a level translator, an oscilloscope, a high voltage power supply, and a special apparatus used to power and send signal to the A/D cards. After testing, modules were obtained that meet necessary criteria for use in the APEX experiment, and the A/D cards obtained were determined to have adequate specifications for their utilization, with specific results included in the appendix.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.