Cooperative Aerial Search and Localization Using Lissajous Patterns

This paper presents a cooperative aerial search-and-localization framework for applications where knowledge about the target of concern is minimal. The proposed framework leverages the sweeping oscillatory properties of Lissajous curves to improve an agent\u27s chances of encountering a target. To accurately estimate the states of cooperative search drones, a discrete-time linear Lissajous motion model approximation is presented in such a way that uncertainty in physical model parameters can be accounted for. These uncertainties are propagated through estimation formulas to improve both agent and target localization relative to a static base station. Numerous experiments conducted in a physics-driven simulation environment show that Lissajous search patterns are a logical and effective substitute for many existing search pattern standards. Furthermore, parametric Monte Carlo simulation studies validate the proposed estimation framework as a more accurate target localizer than other traditional methods which do not account for inaccuracy in the motion model. These techniques hold promise for both static and dynamic target search-and-localization scenarios, allowing for robust estimation by eliminating the need for knowledge of low-level control input to search agents

A Generalized Bayesian Approach for Localizing Static Natural Obstacles on Unpaved Roads

This paper presents an approach that implements sensor fusion and recursive Bayesian estimation (RBE) to improve a vehicle\u27s ability to perform obstacle detection and localization in unpaved road environments. The proposed approach utilizes RADAR, LiDAR and stereovision fully for sensor fusion to detect and localize static natural obstacles. Each sensor is characterized by a probabilistic sensor model which quantifies level of confidence (LOC) and probability of detection (POD) associatively. Deploying these sensor models enables the fusion of heterogeneous sensors without extensive formulations and with the incorporation of each sensor\u27s strengths. An Extended Kalman filter (EKF) is formulated and implemented for robust and computationally efficient RBE of obstacles\u27 locations while a sensor-equipped vehicle moves and observes them. Results with a test vehicle show the successful detection and localization of a static natural object on an unpaved road has demonstrated the effectiveness of the proposed approach

Gambling symptoms, behaviors, and cognitive distortions in Japanese university students

Background: This study was conducted to investigate the relationship between symptoms of gambling problems, gambling behaviours, and cognitive distortions among a university student population in Japan ages 20 to 29 years. We aimed to address the gap in knowledge of gambling disorders and treatment for this population. Methods: Data were obtained from 1471 Japanese undergraduate students from 19 universities in Japan. Descriptive statistics and hierarchical multivariate regression analysis were used to investigate whether the factors of gambling cognitive distortions would have predictive effects on gambling disorder symptoms. Results: Results indicated that 5.1% of the participants are classifiable as probable disordered gamblers. The bias of the gambling type to pachinko and pachislot was unique to gamblers in Japan. Of the students sampled, 342 self-reported gambling symptoms via the South Oaks Gambling Screen. Hierarchical multivariate regression analysis indicated that one domain of gambling cognitive distortions was associated significantly with gambling symptoms among the 342 symptomatic participants: gambling expectancy (β = 0.19, p < .05). The multivariate model explained 47% of the variance in the gambling symptoms. Conclusion: This study successfully contributed to the sparse research on university student gambling in Japan. Specifically, our results indicated a statistically significant relationship between gambling cognitive distortions and gambling disorder symptoms. These results can inform the development of preventive education and treatment for university students with gambling disorder in Japan. The report also describes needs for future research of university students with gambling disorder

Prognostic factors and effect modifiers for personalisation of internet-based cognitive behavioural therapy among university students with subthreshold depression: A secondary analysis of a factorial trial

BACKGROUND: Internet-cognitive behavioural therapy (iCBT) for depression can include multiple components. This study explored depressive symptom improvement prognostic factors (PFs) and effect modifiers (EMs) for five common iCBT components including behavioural activation, cognitive restructuring, problem solving, self-monitoring, and assertion training. METHODS: We used data from a factorial trial of iCBT for subthreshold depression among Japanese university students (N = 1093). The primary outcome was the change in PHQ-9 scores at 8 weeks from baseline. Interactions between each component and various baseline characteristics were estimated using a mixed-effects model for repeated measures. We calculated multiplicity-adjusted p-values at 5 % false discovery rate using the Benjamini-Hochberg procedure. RESULTS: After multiplicity adjustment, the baseline PHQ-9 total score emerged as a PF and exercise habits as an EM for self-monitoring (adjusted p-values <0.05). The higher the PHQ-9 total score at baseline (range: 5-14), the greater the decrease after 8 weeks. For each 5-point increase at baseline, the change from baseline to 8 weeks was bigger by 2.8 points. The more frequent the exercise habits (range: 0-2 points), the less effective the self-monitoring component. The difference in PHQ-9 change scores between presence or absence of self-monitoring was smaller by 0.94 points when the participant exercised one level more frequently. Additionally, the study suggested seven out of 36 PFs and 14 out of 160 EMs examined were candidates for future research. LIMITATIONS: Generalizability is limited to university students with subthreshold depression. CONCLUSIONS: These results provide some helpful information for the future development of individualized iCBT algorithms for depression

Components of smartphone cognitive-behavioural therapy for subthreshold depression among 1093 university students: a factorial trial

BACKGROUND: Internet-based cognitive-behavioural therapy (iCBT) is effective for subthreshold depression. However, which skills provided in iCBT packages are more effective than others is unclear. Such knowledge can inform construction of more effective and efficient iCBT programmes. OBJECTIVE: To examine the efficacy of five components of iCBT for subthreshold depression. METHODS: We conducted an factorial trial using a smartphone app, randomly allocating presence or absence of five iCBT skills including self-monitoring, behavioural activation (BA), cognitive restructuring (CR), assertiveness training (AT) and problem-solving. Participants were university students with subthreshold depression. The primary outcome was the change on the Patient Health Questionnaire-9 (PHQ-9) from baseline to week 8. Secondary outcomes included changes in CBT skills. FINDINGS: We randomised a total of 1093 participants. In all groups, participants had a significant PHQ-9 reduction from baseline to week 8. Depression reduction was not significantly different between presence or absence of any component, with corresponding standardised mean differences (negative values indicate specific efficacy in favour of the component) ranging between -0.04 (95% CI -0.16 to 0.08) for BA and 0.06 (95% CI -0.06 to 0.18) for AT. Specific CBT skill improvements were noted for CR and AT but not for the others. CONCLUSIONS: There was significant reduction in depression for all participants regardless of the presence and absence of the examined iCBT components. CLINICAL IMPLICATION: We cannot yet make evidence-based recommendations for specific iCBT components. We suggest that future iCBT optimisation research should scrutinise the amount and structure of components to examine. TRIAL REGISTRATION NUMBER: UMINCTR-000031307

Parameter Identification with Weightless Regularization

This paper presents a technique for deriving solutions without the use of the parameters and, further, an optimization method, which can work e#ciently for problems of concern. Numerical examples show that the technique can e#ciently search for appropriate solutions. Copyright ? 2001 John Wiley &amp; Sons, Lt