Abstract-We consider the problem of estimating and detecting sparse signals over a large area of an image or other medium. We introduce a novel cost function that captures the tradeoff between allocating energy to signal regions, called regions of interest (ROI), versus exploration of other regions. We show that minimizing our cost guarantees reduction of both the error probability over the unknown ROI and the mean square error (MSE) in estimating the ROI content. Two solutions to the resource allocation problem, subject to a total resource constraint, are derived. Asymptotic analysis shows that the estimated ROI converges to the true ROI. We show that our adaptive sampling method outperforms exhaustive search and are nearly optimal in terms of MSE performance. An illustrative example of our method in radar imaging is given.

Background: Insulin therapy is most effective if dosage titrations are done regularly and frequently, which is seldom possible for busy clinicians. The d-Nav® Insulin Guidance System was design to address the insulin titration gap in patients with type 2 diabetes. It relies on the d-Nav handheld device, which is used to measure glucose, determine the glucose patterns and automatically determine the appropriate next insulin dose. It closes the titration gap in a scalable way utilizing the support of dedicated health care professionals (HCP). This multicenter randomized controlled study tested whether the combination of the d-Nav system and HCP support (d-Nav+HCP-S) is superior to HCP support alone (HCP-S).Methods: 181 subjects using insulin with sub-optimally controlled type 2 diabetes were randomized 1:1 to either d-Nav+HCP-S or HCP-S alone. Both groups were contacted 7 times

Abstract-We consider the problem of energy constrained and noise-limited search for targets that are sparsely distributed over a large area. We propose a multi-scale search algorithm that significantly reduces the search time of the adaptive resource allocation policy (ARAP) introduced in [Bashan et all, 2008]. Similarly to ARAP, the proposed approach scans a Q-cell partition of the search area in two stages: first the entire domain is scanned and second a subset of the domain, suspected of containing targets, is re-scanned. The search strategy of the proposed algorithm is driven by maximization of a modified version of the previously introduced ARAP objective function, which is a surrogate for energy constrained target detection performance. We analyze the performance of the proposed multistage ARAP approach and show that it can reduce mean search time with respect to ARAP for equivalent energy constrained detection performance. To illustrate the potential gains of M-ARAP, we simulate a moving target indicator (MTI) radar system and show that M-ARAP achieves an estimation performance gain of 7 dB and a 85% reduction in scan time as compared to an exhaustive search. This comes within 1 dB of the previously introduced ARAP algorithm at a fraction of its required scan time.

Background: Most patients who use insulin do not achieve optimal glycemic control and become susceptible to complications. Numerous clinical trials have shown that frequent insulin dosage titration is imperative to achieve glycemic control. Unfortunately, implementation of such a paradigm is often impractical. We hypothesized that the Diabetes Insulin Guidance System (DIGSÔ) (Hygieia, Inc., Ann Arbor, MI) software, which automatically advises patients on adjustment of insulin dosage, would provide safe and effective weekly insulin dosage adjustments. Subjects and Methods: In a feasibility study we enrolled patients with type 1 and type 2 diabetes, treated with a variety of insulin regimens and having suboptimal glycemic control. The 12-week intervention period followed a 4-week baseline runin period. During the intervention, DIGS processed patients' glucose readings and provided insulin dosage adjustments on a weekly basis. If approved by the study team, the adjusted insulin dosage was communicated to the patients. Insulin formulations were not changed during the study. The primary outcome was the fraction of DIGS dosage adjustments approved by the study team, and the secondary outcome was improved glycemic control. Results: Forty-six patients were recruited, and eight withdrew. The DIGS software recommended 1,734 insulin dosage adjustments, of which 1,731 (99.83%) were approved. During the run-in period the weekly average glucose was stable at 174.2 -36.7 mg/dL (9.7 -2.0 mmol/L). During the following 12 weeks, DIGS dosage adjustments resulted in progressive improvement in average glucose to 163.3 -35.1 mg/dL (9.1 -1.9 mmol/L) (P < 0.03). Mean glycosylated hemoglobin decreased from 8.4 -0.8% to 7.9 -0.9% (P < 0.05). Concomitantly, the frequency of hypoglycemia decreased by 25.2%. Conclusions: The DIGS software provided patients with safe and effective weekly insulin dosage adjustments. Widespread implementation of DIGS may improve the outcome and reduce the cost of implementing effective insulin therapy.

Our findings may call attention to a major contributing factor to hypoglycemia among insulin users. In reality, insulin dosage is seldom adjusted and thus transient periods of decrease in insulin requirements and overtreatment are usually overlooked.