Experimental demonstration of cost-Effective intensity-modulation and direct-detection optical fast-OFDM over 40km SMF transmission

We demonstrate the first experimental implementation of intensity-modulation and direct-detection 7.6Gb/s DBPSK-based DSB optical Fast-OFDM with a reduced subcarrier spacing equal to half of the symbol rate per subcarrier over 40km SMF. © 2012 OSA.

Time resolved bit error rate analysis of a fast switching tunable laser for use in optically switched networks

We investigate the use of different direct detection modulation formats in a wavelength switched optical network. We find the minimum time it takes a tunable sampled grating distributed Bragg reflector laser to recover after switching from one wavelength channel to another for different modulation formats. The recovery time is investigated utilizing a field programmable gate array which operates as a time resolved bit error rate detector. The detector offers 93 ps resolution operating at 10.7 Gb/s and allows for all the data received to contribute to the measurement, allowing low bit error rates to be measured at high speed. The recovery times for 10.7 Gb/s non-return-to-zero on–off keyed modulation, 10.7 Gb/s differentially phase shift keyed signal and 21.4 Gb/s differentially quadrature phase shift keyed formats can be as low as 4 ns, 7 ns and 40 ns, respectively. The time resolved phase noise associated with laser settling is simultaneously measured for 21.4 Gb/s differentially quadrature phase shift keyed data and it shows that the phase noise coupled with frequency error is the primary limitation on transmitting immediately after a laser switching event.

Adaptive sending rate over wireless mesh networks using SNR

Wireless Mesh Networks (WMNs) have emerged as a key technology for the next generation of wireless networking. Instead of being another type of ad-hoc networking, WMNs diversify the capabilities of ad-hoc networks. Several protocols that work over WMNs include IEEE 802.11a/b/g, 802.15, 802.16 and LTE-Advanced. To bring about a high throughput under varying conditions, these protocols have to adapt their transmission rate. This paper proposes a scheme to improve channel conditions by performing rate adaptation along with multiple packet transmission using packet loss and physical layer condition. Dynamic monitoring, multiple packet transmission and adaptation to changes in channel quality by adjusting the packet transmission rates according to certain optimization criteria provided greater throughput. The key feature of the proposed method is the combination of the following two factors: 1) detection of intrinsic channel conditions by measuring the fluctuation of noise to signal ratio via the standard deviation, and 2) the detection of packet loss induced through congestion. The authors show that the use of such techniques in a WMN can significantly improve performance in terms of the packet sending rate. The effectiveness of the proposed method was demonstrated in a simulated wireless network testbed via packet-level simulation.

System-on-a-Chip (SOC) based environmental monitoring platform for the detection of hazardous materials

The purpose of this research is design considerations for environmental monitoring platforms for the detection of hazardous materials using System-on-a-Chip (SoC) design. Design considerations focus on improving key areas such as: (1) sampling methodology; (2) context awareness; and (3) sensor placement. These design considerations for environmental monitoring platforms using wireless sensor networks (WSN) is applied to the detection of methylmercury (MeHg) and environmental parameters affecting its formation (methylation) and deformation (demethylation). ^ The sampling methodology investigates a proof-of-concept for the monitoring of MeHg using three primary components: (1) chemical derivatization; (2) preconcentration using the purge-and-trap (P&T) method; and (3) sensing using Quartz Crystal Microbalance (QCM) sensors. This study focuses on the measurement of inorganic mercury (Hg) (e.g., Hg2+) and applies lessons learned to organic Hg (e.g., MeHg) detection. ^ Context awareness of a WSN and sampling strategies is enhanced by using spatial analysis techniques, namely geostatistical analysis (i.e., classical variography and ordinary point kriging), to help predict the phenomena of interest in unmonitored locations (i.e., locations without sensors). This aids in making more informed decisions on control of the WSN (e.g., communications strategy, power management, resource allocation, sampling rate and strategy, etc.). This methodology improves the precision of controllability by adding potentially significant information of unmonitored locations.^ There are two types of sensors that are investigated in this study for near-optimal placement in a WSN: (1) environmental (e.g., humidity, moisture, temperature, etc.) and (2) visual (e.g., camera) sensors. The near-optimal placement of environmental sensors is found utilizing a strategy which minimizes the variance of spatial analysis based on randomly chosen points representing the sensor locations. Spatial analysis is employed using geostatistical analysis and optimization occurs with Monte Carlo analysis. Visual sensor placement is accomplished for omnidirectional cameras operating in a WSN using an optimal placement metric (OPM) which is calculated for each grid point based on line-of-site (LOS) in a defined number of directions where known obstacles are taken into consideration. Optimal areas of camera placement are determined based on areas generating the largest OPMs. Statistical analysis is examined by using Monte Carlo analysis with varying number of obstacles and cameras in a defined space. ^

Field programmable gate array based target detection and gesture recognition

The move from Standard Definition (SD) to High Definition (HD) represents a six times increases in data, which needs to be processed. With expanding resolutions and evolving compression, there is a need for high performance with flexible architectures to allow for quick upgrade ability. The technology advances in image display resolutions, advanced compression techniques, and video intelligence. Software implementation of these systems can attain accuracy with tradeoffs among processing performance (to achieve specified frame rates, working on large image data sets), power and cost constraints. There is a need for new architectures to be in pace with the fast innovations in video and imaging. It contains dedicated hardware implementation of the pixel and frame rate processes on Field Programmable Gate Array (FPGA) to achieve the real-time performance. ^ The following outlines the contributions of the dissertation. (1) We develop a target detection system by applying a novel running average mean threshold (RAMT) approach to globalize the threshold required for background subtraction. This approach adapts the threshold automatically to different environments (indoor and outdoor) and different targets (humans and vehicles). For low power consumption and better performance, we design the complete system on FPGA. (2) We introduce a safe distance factor and develop an algorithm for occlusion occurrence detection during target tracking. A novel mean-threshold is calculated by motion-position analysis. (3) A new strategy for gesture recognition is developed using Combinational Neural Networks (CNN) based on a tree structure. Analysis of the method is done on American Sign Language (ASL) gestures. We introduce novel point of interests approach to reduce the feature vector size and gradient threshold approach for accurate classification. (4) We design a gesture recognition system using a hardware/ software co-simulation neural network for high speed and low memory storage requirements provided by the FPGA. We develop an innovative maximum distant algorithm which uses only 0.39% of the image as the feature vector to train and test the system design. Database set gestures involved in different applications may vary. Therefore, it is highly essential to keep the feature vector as low as possible while maintaining the same accuracy and performance^

Detection of antibodies directed at M. hyorhinis p37 in the serum of men with newly diagnosed prostate cancer

Background: Recent epidemiologic, genetic, and molecular studies suggest infection and inflammation initiate certain cancers, including cancers of the prostate. Over the past several years, our group has been studying how mycoplasmas could possibly initiate and propagate cancers of the prostate. Specifically, Mycoplasma hyorhinis encoded protein p37 was found to promote invasion of prostate cancer cells and cause changes in growth, morphology and gene expression of these cells to a more aggressive phenotype. Moreover, we found that chronic exposure of benign human prostate cells to M. hyorhinis resulted in significant phenotypic and karyotypic changes that ultimately resulted in the malignant transformation of the benign cells. In this study, we set out to investigate another potential link between mycoplasma and human prostate cancer. Methods: We report the incidence of men with prostate cancer and benign prostatic hyperplasia (BPH) being seropositive for M. hyorhinis. Antibodies to M. hyorhinis were surveyed by a novel indirect enzyme-linked immunosorbent assay (ELISA) in serum samples collected from men presenting to an outpatient Urology clinic for BPH (N = 105) or prostate cancer (N = 114) from 2006-2009. Results: A seropositive rate of 36% in men with BPH and 52% in men with prostate cancer was reported, thus leading us to speculate a possible connection between M. hyorhinis exposure with prostate cancer. Conclusions: These results further support a potential exacerbating role for mycoplasma in the development of prostate cancer.

Evaluation of non-contact sampling and detection of explosives using receiver operating characteristic curves

The growing need for fast sampling of explosives in high throughput areas has increased the demand for improved technology for the trace detection of illicit compounds. Detection of the volatiles associated with the presence of the illicit compounds offer a different approach for sensitive trace detection of these compounds without increasing the false positive alarm rate. This study evaluated the performance of non-contact sampling and detection systems using statistical analysis through the construction of Receiver Operating Characteristic (ROC) curves in real-world scenarios for the detection of volatiles in the headspace of smokeless powder, used as the model system for generalizing explosives detection. A novel sorbent coated disk coined planar solid phase microextraction (PSPME) was previously used for rapid, non-contact sampling of the headspace containers. The limits of detection for the PSPME coupled to IMS detection was determined to be 0.5-24 ng for vapor sampling of volatile chemical compounds associated with illicit compounds and demonstrated an extraction efficiency of three times greater than other commercially available substrates, retaining >50% of the analyte after 30 minutes sampling of an analyte spike in comparison to a non-detect for the unmodified filters. Both static and dynamic PSPME sampling was used coupled with two ion mobility spectrometer (IMS) detection systems in which 10-500 mg quantities of smokeless powders were detected within 5-10 minutes of static sampling and 1 minute of dynamic sampling time in 1-45 L closed systems, resulting in faster sampling and analysis times in comparison to conventional solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) analysis. Similar real-world scenarios were sampled in low and high clutter environments with zero false positive rates. Excellent PSPME-IMS detection of the volatile analytes were visualized from the ROC curves, resulting with areas under the curves (AUC) of 0.85-1.0 and 0.81-1.0 for portable and bench-top IMS systems, respectively. Construction of ROC curves were also developed for SPME-GC-MS resulting with AUC of 0.95-1.0, comparable with PSPME-IMS detection. The PSPME-IMS technique provides less false positive results for non-contact vapor sampling, cutting the cost and providing an effective sampling and detection needed in high-throughput scenarios, resulting in similar performance in comparison to well-established techniques with the added advantage of fast detection in the field.

Integrity-based kernel malware detection

Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. ^ We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. ^ We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. ^ We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). ^ In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.^

Monitoring of down-hole parameters for early kick detection

The sudden hydrocarbon influx from the formation into the wellbore poses a serious risk to the safety of the well. This sudden influx is termed a kick, which, if not controlled, may lead to a blowout. Therefore, early detection of the kick is crucial to minimize the possibility of a blowout occurrence. There is a high probability of delay in kick detection, apart from other issues when using a kick detection system that is exclusively based on surface monitoring. Down-hole monitoring techniques have a potential to detect a kick at its early stage. Down-hole monitoring could be particularly beneficial when the influx occurs as a result of a lost circulation scenario. In a lost circulation scenario, when the down-hole pressure becomes lower than the formation pore pressure, the formation fluid may starts to enter the wellbore. The lost volume of the drilling fluid is compensated by the formation fluid flowing into the well bore, making it difficult to identify the kick based on pit (mud tank) volume observations at the surface. This experimental study investigates the occurrence of a kick based on relative changes in the mass flow rate, pressure, density, and the conductivity of the fluid in the down-hole. Moreover, the parameters that are most sensitive to formation fluid are identified and a methodology to detect a kick without false alarms is reported. Pressure transmitter, the Coriolis flow and density meter, and the conductivity sensor are employed to observe the deteriorating well conditions in the down-hole. These observations are used to assess the occurrence of a kick and associated blowout risk. Monitoring of multiple down-hole parameters has a potential to improve the accuracy of interpretation related to kick occurrence, reduces the number of false alarms, and provides a broad picture of down-hole conditions. The down-hole monitoring techniques have a potential to reduce the kick detection period. A down-hole assembly of the laboratory scale drilling rig model and kick injection setup were designed, measuring instruments were acquired, a frame was fabricated, and the experimental set-up was assembled and tested. This set-up has the necessary features to evaluate kick events while implementing down-hole monitoring techniques. Various kick events are simulated on the drilling rig model. During the first set of experiments compressed air (which represents the formation fluid) is injected with constant pressure margin. In the second set of experiments the compressed air is injected with another pressure margin. The experiments are repeated with another pump (flow) rate as well. This thesis consists of three main parts. The first part gives the general introduction, motivation, outline of the thesis, and a brief description of influx: its causes, various leading and lagging indicators, and description of the several kick detection systems that are in practice in the industry. The second part describes the design and construction of the laboratory scale down-hole assembly of the drilling rig and kick injection setup, which is used to implement the proposed methodology for early kick detection. The third part discusses the experimental work, describes the methodology for early kick detection, and presents experimental results that show how different influx events affect the mass flow rate, pressure, conductivity, and density of the fluid in the down-hole, and the discussion of the results. The last chapter contains summary of the study and future research.

Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

Integrity-Based Kernel Malware Detection

Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.

Process Monitoring and Defect Detection of Additive Manufacturing Using Inline Coherent Imaging

With applications ranging from aerospace to biomedicine, additive manufacturing (AM) has been revolutionizing the manufacturing industry. The ability of additive techniques, such as selective laser melting (SLM), to create fully functional, geometrically complex, and unique parts out of high strength materials is of great interest. Unfortunately, despite numerous advantages afforded by this technology, its widespread adoption is hindered by a lack of on-line, real time feedback control and quality assurance techniques. In this thesis, inline coherent imaging (ICI), a broadband, spatially coherent imaging technique, is used to observe the SLM process in 15 - 45 $\mu m$ 316L stainless steel. Imaging of both single and multilayer builds is performed at a rate of 200 $kHz$, with a resolution of tens of microns, and a high dynamic range rendering it impervious to blinding from the process beam. This allows imaging before, during, and after laser processing to observe changes in the morphology and stability of the melt. Galvanometer-based scanning of the imaging beam relative to the process beam during the creation of single tracks is used to gain a unique perspective of the SLM process that has been so far unobservable by other monitoring techniques. Single track processing is also used to investigate the possibility of a preliminary feedback control parameter based on the process beam power, through imaging with both coaxial and 100 $\mu m$ offset alignment with respect to the process beam. The 100 $\mu m$ offset improved imaging by increasing the number of bright A-lines (i.e. with signal greater than the 10 $dB$ noise floor) by 300\%. The overlap between adjacent tracks in a single layer is imaged to detect characteristic fault signatures. Full multilayer builds are carried out and the resultant ICI images are used to detect defects in the finished part and improve upon the initial design of the build system. Damage to the recoater blade is assessed using powder layer scans acquired during a 3D build. The ability of ICI to monitor SLM processes at such high rates with high resolution offers extraordinary potential for future advances in on-line feedback control of additive manufacturing.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Variation in pre-PCR processing of FFPE samples leads to discrepancies in BRAF and EGFR mutation detection: a diagnostic RING trial.

AIMS: Mutation detection accuracy has been described extensively; however, it is surprising that pre-PCR processing of formalin-fixed paraffin-embedded (FFPE) samples has not been systematically assessed in clinical context. We designed a RING trial to (i) investigate pre-PCR variability, (ii) correlate pre-PCR variation with EGFR/BRAF mutation testing accuracy and (iii) investigate causes for observed variation. METHODS: 13 molecular pathology laboratories were recruited. 104 blinded FFPE curls including engineered FFPE curls, cell-negative FFPE curls and control FFPE tissue samples were distributed to participants for pre-PCR processing and mutation detection. Follow-up analysis was performed to assess sample purity, DNA integrity and DNA quantitation. RESULTS: Rate of mutation detection failure was 11.9%. Of these failures, 80% were attributed to pre-PCR error. Significant differences in DNA yields across all samples were seen using analysis of variance (p

A quality control program for mutation detection in KIT and PDGFRA in gastrointestinal stromal tumours.

BACKGROUND: Although most gastrointestinal stromal tumours (GIST) carry oncogenic mutations in KIT exons 9, 11, 13 and 17, or in platelet-derived growth factor receptor alpha (PDGFRA) exons 12, 14 and 18, around 10% of GIST are free of these mutations. Genotyping and accurate detection of KIT/PDGFRA mutations in GIST are becoming increasingly useful for clinicians in the management of the disease. METHOD: To evaluate and improve laboratory practice in GIST mutation detection, we developed a mutational screening quality control program. Eleven laboratories were enrolled in this program and 50 DNA samples were analysed, each of them by four different laboratories, giving 200 mutational reports. RESULTS: In total, eight mutations were not detected by at least one laboratory. One false positive result was reported in one sample. Thus, the mean global rate of error with clinical implication based on 200 reports was 4.5%. Concerning specific polymorphisms detection, the rate varied from 0 to 100%, depending on the laboratory. The way mutations were reported was very heterogeneous, and some errors were detected. CONCLUSION: This study demonstrated that such a program was necessary for laboratories to improve the quality of the analysis, because an error rate of 4.5% may have clinical consequences for the patient.