Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Selective detection of single-enantiomer spectrum of chiral molecules aligned in the polypeptide liquid crystalline solvent: Transition selective one-dimensional H-1-H-1 COSY

One-dimensional (1D) proton NMR spectra of enantiomers are generally undecipherable in chiral orienting poly-gamma-benzyl-L-glutamate (PBLG)/CDCl3 solvent. This arises due to large number of couplings, in addition to superposition of spectra from both the enantiomers, severely hindering the H-1 detection. On the other hand in the present study the benefit is derived front the presence of several couplings among the entire network of interacting protons. Transition selective 1D H-1-H-1 correlation experiment (1D-COSY) which utilizes the Coupling assisted transfer of magnetization not only for unraveling the overlap but also for the selective detection of enantiopure spectrum is reported. The experiment is simple, easy to implement and provides accurate eanantiomeric excess in addition to the determination of the proton-proton couplings of an enantiomer within a short experimental time (few minutes). (C) 2009 Elsevier Inc. All rights reserved.

High-Rate Space-Time Coded Large-MIMO Systems: Low-Complexity Detection and Channel Estimation

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal space-time block coded (STBC) large-multiple-input multiple-output (MIMO) systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel estimation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-to-capacity performance are achieved by the proposed multistage likelihood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low complexities. The fact that we could show such good results for large STBCs like 16 X 16 and 32 X 32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/Hz (even after accounting for the overheads meant for pilot based training for channel estimation and turbo coding) establishes the effectiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detector. With the feasibility of such a low-complexity detection/channel estimation scheme, large-MIMO systems with tens of antennas operating at several tens of bps/Hz spectral efficiencies can become practical, enabling interesting high data rate wireless applications.

Business busts and U.S. auditors' interest in fraud detection: Evidence from the 20th century

We find evidence that U.S. auditors increased their attention to fraud detection during or immediately after the economic contractions of the 20th century, based on a content analysis of the 12 volumes of the 20th-century auditing reference series Montgomery’s Auditing. Contractions, however, do not seem to have affected auditors’ attention to the formal goal of fraud detection. The study suggests that auditors’ aversion to the heightened risks of fraud during economic downturns leads them to focus more on fraud detection at those times regardless of the particular guidance in formal audit standards. This study is the first to find some evidence of a recession-influenced difference between fraud detection practices and formal fraud detection goals.

Depression, Anxiety, Psychiatric Comorbidity and Dimensions of Temperament and Personality

This study is part of the Mood Disorders Project conducted by the Department of Mental Health and Alcohol Research, National Public Health Institute, and consists of a general population survey sample and a major depressive disorder (MDD) patient cohort from Vantaa Depression Study (VDS). The general population survey study was conducted in 2003 in the cities of Espoo and Vantaa. The VDS is a collaborative depression research project between the Department of Mental Health and Alcohol Research of the National Public Health Institute and the Department of Psychiatry of the Peijas Medical Care District (PMCD) beginning in 1997. It is a prospective, naturalistic cohort study of 269 secondary-level care psychiatric out- and inpatients with a new episode of Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) MDD. In the general population survey study, a total of 900 participants (300 from Espoo, 600 from Vantaa) aged 20 70 years were randomly drawn from the Population Register Centre in Finland. A self-report booklet, including the Eysenck Personality Inventory (EPI), the Temperament and Character Inventory Revised (TCI-R), the Beck Depression Inventory and the Beck Anxiety Inventory was mailed to all subjects. Altogether 441 participants responded (94 returned only the shortened version without TCI-R) and gave their informed consent. VDS involved screening all patients aged 20-60 years (n=806) in the PMCD for a possible new episode of DSM-IV MDD. 542 consenting patients were interviewed with a semi-structured interview (the WHO Schedules for Clinical Assessment in Neuropsychiatry, version 2.0). 269 patients with a current DSM-IV MDD were included in the study and further interviewed with semi-structured interviews to assess all other axis I and II psychiatric diagnoses. Exclusion criteria were DSM-IV bipolar I and II, schizoaffective disorder, schizophrenia or another psychosis, organic and substance-induced mood disorders. In the present study are included those 193 (139 females, 54 males) individuals who could be followed up at both 6 and 18 months, and their depression had remained unipolar. Personality was investigated with the EPI. Personality dimensions associated not only to the symptoms of depression, but also to the symptoms of anxiety among general population and in depressive patients, as well as to comorbid disorders in MDD patients, supporting the dimensional view of depression and anxiety. Among the general population High Harm Avoidance and low Self-Directedness associated moderately, whereas low extraversion and high neuroticism strongly with the depressive and anxiety symptoms. The personality dimensions, especially high Harm Avoidance, low Self-Directedness and high neuroticism were also somewhat predictive of self-reported use of health care services for psychiatric reasons, and lifetime mental disorder. Moreover, high Harm Avoidance associated with a family history of mental disorder. In depressive patients, neuroticism scores were found to decline markedly and extraversion scores to increase somewhat with recovery. The predictive value of the changes in symptoms of depression and anxiety in explaining follow-up neuroticism was about 1/3 of that of baseline neuroticism. In contrast to neuroticism, the scores of extraversion showed no dependence on the symptoms of anxiety, and the change in the symptoms of depression explained only 1/20 of the follow-up extraversion compared with baseline extraversion. No evidence was found of the scar effect during a one-year follow-up period. Finally, even after controlling for symptoms of both depression and anxiety, depressive patients had a somewhat higher level of neuroticism (odds ratio 1.11, p=0.001) and a slightly lower level of extraversion (odds ratio 0.92, p=0.003) than subjects in the general population. Among MDD patients, a positive dose-exposure relationship appeared to exist between neuroticism and prevalence and number of comorbid axis I and II disorders. A negative relationship existed between level of extraversion and prevalence of comorbid social phobia and cluster C personality disorders. Personality dimensions are associated with the symptoms of depression and anxiety. Futhermore these findings support the hypothesis that high neuroticism and somewhat low extraversion might be vulnerability factors for MDD, and that high neuroticism and low extraversion predispose to comorbid axis I and II disorders among patients with MDD.

Detection of microorganisms using biosensors-a smarter way towards detection techniques.

Along with useful microorganisms, there are some that cause potential damage to the animals and plants. Detection and identification of these harmful organisms in a cost and time effective way is a challenge for the researchers. The future of detection methods for microorganisms shall be guided by biosensor, which has already contributed enormously in sensing and detection technology. Here, we aim to review the use of various biosensors, developed by integrating the biological and physicochemical/mechanical properties (of tranducers), which can have enormous implication in healthcare, food, agriculture and biodefence. We have also highlighted the ways to improve the functioning of the biosensor.

Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.

Identification of genetic susceptibility loci for migraine

Migraine is the common cause of chronic episodic headache, affecting 12%-15% of the Caucasian population (41 million Europeans and some half a million Finns), and causes considerable loss of quality of life to its sufferers, as well as being linked to increased risk for a wide range of conditions, from depression to stroke. Migraine is the 19th most severe disease in terms of disability-adjusted life years, and 9th among women. It is characterized by attacks of headache accompanied by sensitivity to external stimuli lasting 4-72 hours, and in a third of cases by neurological aura symptoms, such as loss of vision, speech or muscle function. The underlying pathophysiology, including what triggers migraine attacks and why they occur in the first place, is largely unknown. The aim of this study was to identify genetic factors associated with the hereditary susceptibility to migraine, in order to gain a better understanding of migraine mechanisms. In this thesis, we report the results of genetic linkage and association analyses on a Finnish migraine patient collection as well as migraineurs from Australia, Denmark, Germany, Iceland and the Netherlands. Altogether we studied genetic information of nearly 7,000 migraine patients and over 50,000 population-matched controls. We also developed a new migraine analysis method called the trait component analysis, which is based on individual patient responses instead of the clinical diagnosis. Using this method, we detected a number of new genetic loci for migraine, including on chromosome 17p13 (HLOD 4.65) and 10q22-q23 (female-specific HLOD 7.68) with significant evidence of linkage, along with five other loci (2p12, 8q12, 4q28-q31, 18q12-q22, and Xp22) detected with suggestive evidence of linkage. The 10q22-q23 locus was the first genetic finding in migraine to show linkage to the same locus and markers in multiple populations, with consistent detection in six different scans. Traditionally, ion channels have been thought to play a role in migraine susceptibility, but we were able to exclude any significant role for common variants in a candidate gene study of 155 ion transport genes. This was followed up by the first genome-wide association study in migraine, conducted on 2,748 migraine patients and 10,747 matched controls followed by a replication in 3,209 patients and 40,062 controls. In this study, we found interesting results with genome-wide significance, providing targets for future genetic and functional studies. Overall, we found several promising genetic loci for migraine providing a promising base for future studies in migraine.

Infections in lung and heart transplant recipients : Studies on the impact of bronchoscopy in the diagnosis of respiratory infections and detection of viral infections in blood and bronchoalveolar lavage fluid

Infection is a major cause of mortality and morbidity after thoracic organ transplantation. The aim of the present study was to evaluate the infectious complications after lung and heart transplantation, with a special emphasis on the usefulness of bronchoscopy and the demonstration of cytomegalovirus (CMV), human herpes virus (HHV)-6, and HHV-7. We reviewed all the consecutive bronchoscopies performed on heart transplant recipients (HTRs) from May 1988 to December 2001 (n = 44) and lung transplant recipients (LTRs) from February 1994 to November 2002 (n = 472). To compare different assays in the detection of CMV, a total of 21 thoracic organ transplant recipients were prospectively monitored by CMV pp65-antigenemia, DNAemia (PCR), and mRNAemia (NASBA) tests. The antigenemia test was the reference assay for therapeutic intervention. In addition to CMV antigenemia, 22 LTRs were monitored for HHV-6 and HHV-7 antigenemia. The diagnostic yield of the clinically indicated bronchoscopies was 41 % in the HTRs and 61 % in the LTRs. The utility of the bronchoscopy was highest from one to six months after transplantation. In contrast, the findings from the surveillance bronchoscopies performed on LTRs led to a change in the previous treatment in only 6 % of the cases. Pneumocystis carinii and CMV were the most commonly detected pathogens. Furthermore, 15 (65 %) of the P. carinii infections in the LTRs were detected during chemoprophylaxis. None of the complications of the bronchoscopies were fatal. Antigenemia, DNAemia, and mRNAemia were present in 98 %, 72 %, and 43 % of the CMV infections, respectively. The optimal DNAemia cut-off levels (sensitivity/specificity) were 400 (75.9/92.7 %), 850 (91.3/91.3 %), and 1250 (100/91.5 %) copies/ml for the antigenemia of 2, 5, and 10 pp65-positive leukocytes/50 000 leukocytes, respectively. The sensitivities of the NASBA were 25.9, 43.5, and 56.3 % in detecting the same cut-off levels. CMV DNAemia was detected in 93 % and mRNAemia in 61 % of the CMV antigenemias requiring antiviral therapy. HHV-6, HHV-7, and CMV antigenemia was detected in 20 (91 %), 11 (50 %), and 12 (55 %) of the 22 LTRs (median 16, 31, and 165 days), respectively. HHV-6 appeared in 15 (79 %), HHV-7 in seven (37 %), and CMV in one (7 %) of these patients during ganciclovir or valganciclovir prophylaxis. One case of pneumonitis and another of encephalitis were associated with HHV-6. In conclusion, bronchoscopy is a safe and useful diagnostic tool in LTRs and HTRs with a suspected respiratory infection, but the role of surveillance bronchoscopy in LTRs remains controversial. The PCR assay acts comparably with the antigenemia test in guiding the pre-emptive therapy against CMV when threshold levels of over 5 pp65-antigen positive leukocytes are used. In contrast, the low sensitivity of NASBA limits its usefulness. HHV-6 and HHV-7 activation is common after lung transplantation despite ganciclovir or valganciclovir prophylaxis, but clinical manifestations are infrequently linked to them.

A Low-Complexity Near-ML Performance Achieving Algorithm for Large MIMO Detection

In this paper, we present a low-complexity, near maximum-likelihood (ML) performance achieving detector for large MIMO systems having tens of transmit and receive antennas. Such large MIMO systems are of interest because of the high spectral efficiencies possible in such systems. The proposed detection algorithm, termed as multistage likelihood-ascent search (M-LAS) algorithm, is rooted in Hopfield neural networks, and is shown to possess excellent performance as well as complexity attributes. In terms of performance, in a 64 x 64 V-BLAST system with 4-QAM, the proposed algorithm achieves an uncoded BER of 10(-3) at an SNR of just about 1 dB away from AWGN-only SISO performance given by Q(root SNR). In terms of coded BER, with a rate-3/4 turbo code at a spectral efficiency of 96 bps/Hz the algorithm performs close to within about 4.5 dB from theoretical capacity, which is remarkable in terms of both high spectral efficiency as well as nearness to theoretical capacity. Our simulation results show that the above performance is achieved with a complexity of just O(NtNt) per symbol, where N-t and N-tau denote the number of transmit and receive antennas.

A method of fraud & intrusion detection for e-payment systems in mobile

The need for paying with mobile devices has urged the development of payment systems for mobile electronic commerce. In this paper we have considered two important abuses in electronic payments systems for detection. The fraud, which is an intentional deception accomplished to secure an unfair gain, and an intrusion which are any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. Most of the available fraud and intrusion detection systems for e-payments are specific to the systems where they have been incorporated. This paper proposes a generic model called as Activity-Event-Symptoms(AES) model for detecting fraud and intrusion attacks which appears during payment process in the mobile commerce environment. The AES model is designed to identify the symptoms of fraud and intrusions by observing various events/transactions occurs during mobile commerce activity. The symptoms identification is followed by computing the suspicion factors for event attributes, and the certainty factor for a fraud and intrusion is generated using these suspicion factors. We have tested the proposed system by conducting various case studies, on the in-house established mobile commerce environment over wired and wire-less networks test bed.

Stationary processes for object detection and non-rigid structure-from-motion

Stationary processes are random variables whose value is a signal and whose distribution is invariant to translation in the domain of the signal. They are intimately connected to convolution, and therefore to the Fourier transform, since the covariance matrix of a stationary process is a Toeplitz matrix, and Toeplitz matrices are the expression of convolution as a linear operator. This thesis utilises this connection in the study of i) efficient training algorithms for object detection and ii) trajectory-based non-rigid structure-from-motion.