Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

The construction and optimization of an ion mobility spectrometer for the analysis of explosives and drugs

Today, over 15,000 Ion Mobility Spectrometry (IMS) analyzers are employed at worldwide security checkpoints to detect explosives and illicit drugs. Current portal IMS instruments and other electronic nose technologies detect explosives and drugs by analyzing samples containing the headspace air and loose particles residing on a surface. Canines can outperform these systems at sampling and detecting the low vapor pressure explosives and drugs, such as RDX, PETN, cocaine, and MDMA, because these biological detectors target the volatile signature compounds available in the headspace rather than the non-volatile parent compounds of explosives and drugs.^ In this dissertation research volatile signature compounds available in the headspace over explosive and drug samples were detected using SPME as a headspace sampling tool coupled to an IMS analyzer. A Genetic Algorithm (GA) technique was developed to optimize the operating conditions of a commercial IMS (GE Itemizer 2), leading to the successful detection of plastic explosives (Detasheet, Semtex H, and C-4) and illicit drugs (cocaine, MDMA, and marijuana). Short sampling times (between 10 sec to 5 min) were adequate to extract and preconcentrate sufficient analytes (> 20 ng) representing the volatile signatures in the headspace of a 15 mL glass vial or a quart-sized can containing ≤ 1 g of the bulk explosive or drug.^ Furthermore, a research grade IMS with flexibility for changing operating conditions and physical configurations was designed and fabricated to accommodate future research into different analytes or physical configurations. The design and construction of the FIU-IMS were facilitated by computer modeling and simulation of ion’s behavior within an IMS. The simulation method developed uses SIMION/SDS and was evaluated with experimental data collected using a commercial IMS (PCP Phemto Chem 110). The FIU-IMS instrument has comparable performance to the GE Itemizer 2 (average resolving power of 14, resolution of 3 between two drugs and two explosives, and LODs range from 0.7 to 9 ng). ^ The results from this dissertation further advance the concept of targeting volatile components to presumptively detect the presence of concealed bulk explosives and drugs by SPME-IMS, and the new FIU-IMS provides a flexible platform for future IMS research projects.^

Improved sampling, pre-concentration, and detection of hidden explosives and illicit drugs by a novel solid phase microextraction geometry coupled to ion mobility spectrometry

The 9/11 Act mandates the inspection of 100% of cargo shipments entering the U.S. by 2012 and 100% inspection of air cargo by March 2010. So far, only 5% of inbound shipping containers are inspected thoroughly while air cargo inspections have fared better at 50%. Government officials have admitted that these milestones cannot be met since the appropriate technology does not exist. This research presents a novel planar solid phase microextraction (PSPME) device with enhanced surface area and capacity for collection of the volatile chemical signatures in air that are emitted from illicit compounds for direct introduction into ion mobility spectrometers (IMS) for detection. These IMS detectors are widely used to detect particles of illicit substances and do not have to be adapted specifically to this technology. For static extractions, PDMS and sol-gel PDMS PSPME devices provide significant increases in sensitivity over conventional fiber SPME. Results show a 50–400 times increase in mass detected of piperonal and a 2–4 times increase for TNT. In a blind study of 6 cases suspected to contain varying amounts of MDMA, PSPME-IMS correctly detected 5 positive cases with no false positives or negatives. One of these cases had minimal amounts of MDMA resulting in a false negative response for fiber SPME-IMS. A La (dihed) phase chemistry has shown an increase in the extraction efficiency of TNT and 2,4-DNT and enhanced retention over time. An alternative PSPME device was also developed for the rapid (seconds) dynamic sampling and preconcentration of large volumes of air for direct thermal desorption into an IMS. This device affords high extraction efficiencies due to strong retention properties under ambient conditions resulting in ppt detection limits when 3.5 L of air are sampled over the course of 10 seconds. Dynamic PSPME was used to sample the headspace over the following: MDMA tablets (12–40 ng detected of piperonal), high explosives (Pentolite) (0.6 ng detected of TNT), and several smokeless powders (26–35 ng of 2,4-DNT and 11–74 ng DPA detected). PSPME-IMS technology is flexible to end-user needs, is low-cost, rapid, sensitive, easy to use, easy to implement, and effective. ^

Perceptions of French and Creole Among First-Generation Adult Haitian English Language Learners

Context: Due to a unique combination of factors, outdoor athletes in the Southeastern United States are at high risk of lightning deaths and injuries. Lightning detection methods are available to minimize lightning strike victims. Objective: Becoming aware of the risk factors that predispose athletes to lightning strikes and determining the most reliable detection method against hazardous weather will enable Certified Athletic Trainers to develop protocols that protect athletes from injury. Data Sources: A comprehensive literature review of Medline and Pubmed using key words: lightning, lightning risk factors, lightning safety, lightning detection, and athletic trainers and lightning was completed. Data Synthesis: Factors predisposing athletes to lighting death or injury include: time of year, time of day, the athlete’s age, geographical location, physical location, sex, perspiration level, and lack of education and preparedness by athletes and staff. Although handheld lightning detectors have become widely accessible to detect lightning strikes, their performance has not been independently or objectively confirmed. There is evidence that these detectors inaccurately detect strike locations by recording false strikes and not recording actual strikes. Conclusions: Lightning education and preparation are two factors that can be controlled. Measures need to be taken by Certified Athletic Trainers to ensure the safety of athletes during outdoor athletics. It is critical for athletic trainers and supervising staff members to become fully aware of the risks of lightning strikes in order to most effectively protect everyone under their supervision. Even though lightning detectors have been manufactured in an attempt to minimize death and injuries due to lightning strikes, none of the detectors have been proven to be 100% effective. Educating coaches, athletes, and parents on the risks of lightning and the detection methods available, while implementing an emergency action plan for lightning safety, is crucial to ensure the well being of the student-athlete population.

Personal computer security system

This work consists on the design and implementation of a complete monitored security system. Two computers make up the basic system: one computer is the transmitter and the other is the receiver. Both computers interconnect by modems. Depending on the status of the input sensors (magnetic contacts, motion detectors and others) the transmitter detects an alarm condition and sends a detailed report of the event via modem to the receiver computer.

The Creation and Evaluation of Novel Canine Training Aids for Cocaine Using Molecularly Encapsulated Sol-Gel Polymers and an Investigation of Canine Field Accuracy

Biological detectors, such as canines, are valuable tools used for the rapid identification of illicit materials. However, recent increased scrutiny over the reliability, field accuracy, and the capabilities of each detection canine is currently being evaluated in the legal system. For example, the Supreme Court case, State of Florida v. Harris, discussed the need for continuous monitoring of canine abilities, thresholds, and search capabilities. As a result, the fallibility of canines for detection was brought to light, as well as a need for further research and understanding of canine detection. This study is two-fold, as it looks to not only create new training aids for canines that can be manipulated for dissipation control, but also investigates canine field accuracy to objects with similar odors to illicit materials. It was the goal of this research to improve upon current canine training aid mimics. Sol-gel polymer training aids, imprinted with the active odor of cocaine, were developed. This novel training aid improved upon the longevity of currently existing training aids, while also provided a way to manipulate the polymer network to alter the dissipation rate of the imprinted active odors. The manipulation of the polymer network could allow handlers to control the abundance of odors presented to their canines, familiarizing themselves to their canine’s capabilities and thresholds, thereby increasing the canines’ strength in court. The field accuracy of detection canines was recently called into question during the Supreme Court case, State of Florida v. Jardines, where it was argued that if cocaine’s active odor, methyl benzoate, was found to be produced by the popular landscaping flower, snapdragons, canines will false alert to said flowers. Therefore, snapdragon flowers were grown and tested both in the laboratory and in the field to determine the odors produced by snapdragon flowers; the persistence of these odors once flowers have been cut; and whether detection canines will alert to both growing and cut flowers during a blind search scenario. Results revealed that although methyl benzoate is produced by snapdragon flowers, certified narcotics detection canines can distinguish cocaine’s odor profile from that of snapdragon flowers and will not alert.

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

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.

Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our national highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

The Construction and Optimization on an Ion Mobility Spectrometer for the Analysis of Explosives and Drugs

Today, over 15,000 Ion Mobility Spectrometry (IMS) analyzers are employed at worldwide security checkpoints to detect explosives and illicit drugs. Current portal IMS instruments and other electronic nose technologies detect explosives and drugs by analyzing samples containing the headspace air and loose particles residing on a surface. Canines can outperform these systems at sampling and detecting the low vapor pressure explosives and drugs, such as RDX, PETN, cocaine, and MDMA, because these biological detectors target the volatile signature compounds available in the headspace rather than the non-volatile parent compounds of explosives and drugs. In this dissertation research volatile signature compounds available in the headspace over explosive and drug samples were detected using SPME as a headspace sampling tool coupled to an IMS analyzer. A Genetic Algorithm (GA) technique was developed to optimize the operating conditions of a commercial IMS (GE Itemizer 2), leading to the successful detection of plastic explosives (Detasheet, Semtex H, and C-4) and illicit drugs (cocaine, MDMA, and marijuana). Short sampling times (between 10 sec to 5 min) were adequate to extract and preconcentrate sufficient analytes (> 20 ng) representing the volatile signatures in the headspace of a 15 mL glass vial or a quart-sized can containing ≤ 1 g of the bulk explosive or drug. Furthermore, a research grade IMS with flexibility for changing operating conditions and physical configurations was designed and fabricated to accommodate future research into different analytes or physical configurations. The design and construction of the FIU-IMS were facilitated by computer modeling and simulation of ion’s behavior within an IMS. The simulation method developed uses SIMION/SDS and was evaluated with experimental data collected using a commercial IMS (PCP Phemto Chem 110). The FIU-IMS instrument has comparable performance to the GE Itemizer 2 (average resolving power of 14, resolution of 3 between two drugs and two explosives, and LODs range from 0.7 to 9 ng). The results from this dissertation further advance the concept of targeting volatile components to presumptively detect the presence of concealed bulk explosives and drugs by SPME-IMS, and the new FIU-IMS provides a flexible platform for future IMS research projects.

Advanced functionality for radio analysis in the Offline software framework of the Pierre Auger Observatory.

The advent of the Auger Engineering Radio Array (AERA) necessitates the development of a powerful framework for the analysis of radio measurements of cosmic ray air showers. As AERA performs "radio-hybrid" measurements of air shower radio emission in coincidence with the surface particle detectors and fluorescence telescopes of the Pierre Auger Observatory, the radio analysis functionality had to be incorporated in the existing hybrid analysis solutions for fluorescence and surface detector data. This goal has been achieved in a natural way by extending the existing Auger Offline software framework with radio functionality. In this article, we lay out the design, highlights and features of the radio extension implemented in the Auger Offline framework. Its functionality has achieved a high degree of sophistication and offers advanced features such as vectorial reconstruction of the electric field, advanced signal processing algorithms, a transparent and efficient handling of FFTs, a very detailed simulation of detector effects, and the read-in of multiple data formats including data from various radio simulation codes. The source code of this radio functionality can be made available to interested parties on request. (C) 2011 Elsevier B.V. All rights reserved.

High-resolution spatial-response measurements of optical nano-antennas in the visible

A few years ago, some of the authors of the paper demonstrated the resonance of optical antennas in the visible frequencies. The results of that paper were obtained using experimental techniques that were primarily developed for the measurement of antenna-coupled detectors in the infrared. In the present paper, we show the results of spatial-response mapping obtained by using a dedicated measurement station for the characterization of optical antennas in the visible. At the same time, the bottleneck in the spatial responsivity calculation represented by the beam characterization has been approached from a different perspective. The proposed technique uses a collection of knife edge measurements in order to avoid the use of any model of the laser beam irradiance. By taking all this into account we present the spatial responsivity of optical antennas measured with high spatial resolution in the visible.

Micromachined silicon lenses for terahertz applications

Silicon microlenses are a very important tool for coupling terahertz (THz) radiation into antennas and detectors in integrated circuits. They can be used in a large array structures at this frequency range reducing considerably the crosstalk between the pixels. Drops of photoresist have been deposited and their shape transferred into the silicon by means of a Reactive Ion Etching (RIE) process. Large silicon lenses with a few mm diameter (between 1.5 and 4.5 mm) and hundreds of μm height (between 50 and 350 μm) have been fabricated. The surface of such lenses has been characterized using Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM), resulting in a surface roughness of about ∼3 μm, good enough for any THz application. The beam profile at the focal plane of such lenses has been measured at a wavelength of 10.6 μm using a tomographic knife-edge technique and a CO2 laser.

A methodology for hierarchical image segmentation evaluation

This paper proposes a method to evaluate hierarchical image segmentation procedures, in order to enable comparisons between different hierarchical algorithms and of these with other (non-hierarchical) segmentation techniques (as well as with edge detectors) to be made. The proposed method builds up on the edge-based segmentation evaluation approach by considering a set of reference human segmentations as a sample drawn from the population of different levels of detail that may be used in segmenting an image. Our main point is that, since a hierarchical sequence of segmentations approximates such population, those segmentations in the sequence that best capture each human segmentation level of detail should provide the basis for the evaluation of the hierarchical sequence as a whole. A small computational experiment is carried out to show the feasibility of our approach.

Optical fiber sensors embedded in flexible polymer foils

In traditional electrical sensing applications, multiplexing and interconnecting the different sensing elements is a major challenge. Recently, many optical alternatives have been investigated including optical fiber sensors of which the sensing elements consist of fiber Bragg gratings. Different sensing points can be integrated in one optical fiber solving the interconnection problem and avoiding any electromagnetical interference (EMI). Many new sensing applications also require flexible or stretchable sensing foils which can be attached to or wrapped around irregularly shaped objects such as robot fingers and car bumpers or which can even be applied in biomedical applications where a sensor is fixed on a human body. The use of these optical sensors however always implies the use of a light-source, detectors and electronic circuitry to be coupled and integrated with these sensors. The coupling of these fibers with these light sources and detectors is a critical packaging problem and as it is well-known the costs for packaging, especially with optoelectronic components and fiber alignment issues are huge. The end goal of this embedded sensor is to create a flexible optical sensor integrated with (opto)electronic modules and control circuitry. To obtain this flexibility, one can embed the optical sensors and the driving optoelectronics in a stretchable polymer host material. In this article different embedding techniques for optical fiber sensors are described and characterized. Initial tests based on standard manufacturing processes such as molding and laser structuring are reported as well as a more advanced embedding technique based on soft lithography processing.