Multi-sensor fusion deals with the combination of complementary and sometimes competin sensor data into a reliable estimate of the environment to a&ieve a sum which is better than the parts. Multi-sensor systems have applications in automatic target recognition, autonomous robot navigation, and automatic manufacturing. This paper presents a current survey of the state of the art in multi-sensor fusion. We have surveyed papers related to fusion and classified them into six categories: scene segmentation, representation, 3-D shape, sensor modelin autonomous robots, and object recognition. A number of ksion strategies have been employed to combine sensor outputs. These strategies range from sim le set intersection, logical and operations, and heuristic profuction rules to more complex methods involving non-linear least square fit and maximum likelihood estimates. Sensor uncertainty has been modeled using Bayesian probabilities, and support and plausibility involving the Dempster-Shafer formalism.

Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6–77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people.Trial registrationClinicaltrials.gov (NCT01038583)

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The use of independent component analysis (ICA) methods for blind source separation of signals and images has been demonstrated in many applications and publications. While many ICA based algorithms for source separation exist, few impose physical constraints on the recovered independent components and the mixing matrix. Of particular interest is the non-negativity of the recovered independent components and the recovered mixing matrix. Such constraints are important for example when trying to do subpixel demixing on hyperspectral images. In this article, we propose a constrained non-negative maximum-likelihood ICA (CNML-ICA) algorithm that tackles the limitations of some existing non-negative ICA methods.

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