Cognitive Sub-Nyquist Hardware Prototype of a Collocated MIMO Radar

We present the design and hardware implementation of a radar prototype that demonstrates the principle of a sub-Nyquist collocated multiple-input multiple-output (MIMO) radar. The setup allows sampling in both spatial and spectral domains at rates much lower than dictated by the Nyquist sampling theorem. Our prototype realizes an X-band MIMO radar that can be configured to have a maximum of 8 transmit and 10 receive antenna elements. We use frequency division multiplexing (FDM) to achieve the orthogonality of MIMO waveforms and apply the Xampling framework for signal recovery. The prototype also implements a cognitive transmission scheme where each transmit waveform is restricted to those pre-determined subbands of the full signal bandwidth that the receiver samples and processes. Real-time experiments show reasonable recovery performance while operating as a 4x5 thinned random array wherein the combined spatial and spectral sampling factor reduction is 87.5% of that of a filled 8x10 array.Comment: 5 pages, Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa) 201

Cross Modal Distillation for Flood Extent Mapping

The increasing intensity and frequency of floods is one of the many consequences of our changing climate. In this work, we explore ML techniques that improve the flood detection module of an operational early flood warning system. Our method exploits an unlabelled dataset of paired multi-spectral and Synthetic Aperture Radar (SAR) imagery to reduce the labeling requirements of a purely supervised learning method. Prior works have used unlabelled data by creating weak labels out of them. However, from our experiments we noticed that such a model still ends up learning the label mistakes in those weak labels. Motivated by knowledge distillation and semi supervised learning, we explore the use of a teacher to train a student with the help of a small hand labelled dataset and a large unlabelled dataset. Unlike the conventional self distillation setup, we propose a cross modal distillation framework that transfers supervision from a teacher trained on richer modality (multi-spectral images) to a student model trained on SAR imagery. The trained models are then tested on the Sen1Floods11 dataset. Our model outperforms the Sen1Floods11 baseline model trained on the weak labeled SAR imagery by an absolute margin of 6.53% Intersection-over-Union (IoU) on the test split

Characteristics of undernourished older medical patients and the identification of predictors for undernutrition status

<p>Abstract</p> <p>Background</p> <p>Undernutrition among older people is a continuing source of concern, particularly among acutely hospitalized patients. The purpose of the current study is to compare malnourished elderly patients with those at nutritional risk and identify factors contributing to the variability between the groups.</p> <p>Methods</p> <p>The study was carried out at the Soroka University Medical Center in the south of Israel. From September 2003 through December 2004, all patients 65 years-of-age or older admitted to any of the internal medicine departments, were screened within 72 hours of admission to determine nutritional status using the short version of the Mini Nutritional Assessment (MNA-SF). Patients at nutritional risk were entered the study and were divided into malnourished or 'at risk' based on the full version of the MNA. Data regarding medical, nutritional, functional, and emotional status were obtained by trained interviewers.</p> <p>Results</p> <p>Two hundred fifty-nine elderly patients, 43.6% men, participated in the study; 18.5% were identified as malnourished and 81.5% were at risk for malnutrition according to the MNA. The malnourished group was less educated, had a higher depression score and lower cognitive and physical functioning. Higher prevalence of chewing problems, nausea, and vomiting was detected among malnourished patients. There was no difference between the groups in health status indicators except for subjective health evaluation which was poorer among the malnourished group. Lower dietary score indicating lower intake of vegetables fruits and fluid, poor appetite and difficulties in eating distinguished between malnourished and at-risk populations with the highest sensitivity and specificity as compare with the anthropometric, global, and self-assessment of nutritional status parts of the MNA. In a multivariate analysis, lower cognitive function, education <12 years and chewing problems were all risk factors for malnutrition.</p> <p>Conclusion</p> <p>Our study indicates that low food consumption as well as poor appetite and chewing problems are associated with the development of malnutrition. Given the critical importance of nutritional status in the hospitalized elderly, further intervention trials are required to determine the best intervention strategies to overcome these problems.</p

Characterising the refractive error in paediatric patients with congenital stationary night blindness: a multicentre study

BACKGROUND/AAIMS: Congenital stationary night blindness (CSNB) is an inherited retinal disease that is often associated with high myopia and can be caused by pathological variants in multiple genes, most commonly CACNA1F, NYX and TRPM1. High myopia is associated with retinal degeneration and increased risk for retinal detachment. Slowing the progression of myopia in patients with CSNB would likely be beneficial in reducing risk, but before interventions can be considered, it is important to understand the natural history of myopic progression. METHODS: This multicentre, retrospective study explored CSNB caused by variants in CACNA1F, NYX or TRPM1 in patients who had at least 6 measurements of their spherical equivalent of refraction (SER) before the age of 18. A mixed-effect model was used to predict progression of SER overtime and differences between genotypes were evaluated. RESULTS: 78 individuals were included in this study. All genotypes showed a significant myopic predicted SER at birth (-3.076D, -5.511D and -5.386D) for CACNA1F, NYX and TRPM1 respectively. Additionally, significant progression of myopia per year (-0.254D, -0.257D and -0.326D) was observed for all three genotypes CACNA1F, NYX and TRPM1, respectively. CONCLUSIONS: Patients with CSNB tend to be myopic from an early age and progress to become more myopic with age. Patients may benefit from long-term myopia slowing treatment in the future and further studies are indicated. Additionally, CSNB should be considered in the differential diagnosis for early-onset myopia

Spectral Embedding Norm

Anomaly detection is a data partitioning algorithm which separates outliers from normative data points. An unsupervised learning approach to this problem does not assume any prior information. Anomaly detection is a primary data analysis task with diverse applications and has been studied under many models and assumptions.Spectral methods such as spectral clustering have been widely used to solve the clustering problem. These methods make use of the leading K eigenvectors of the graph Laplacian matrix to detect K clusters, if the graph has a clear community structure. In a setting where the data consists of unbalanced clusters, as in anomaly detection, the spectral properties are determined by a dominating component. In such cases, traditional graph clustering methods fail, while the spectral embedding norm was found to overcome this challenge. This thesis generalizes the spectral embedding norm definition, formerly used, by introducing the capability of a weighted norm. With just one simple natural condition: allowing limited connectivity between the clusters and the background, we prove that this quantity can be used to detect the clusters of interest. Experiments on both synthetic data sets and real-world defect detection images demonstrate the effectiveness of the algorithm and its performance was found to be stable, with respect to parameter choices

Identification and control of non-linear time-varying dynamical systems using artificial neural networks

Identification and control of non-linear dynamical systems is a very complex task which requires new methods of approaching. This research addresses the problem of emulation and control via the use of distributed parallel processing, namely artificial neural networks. Four models for describing non-linear MIMO dynamical systems are presented. Based on these models a combined feedforward and recurrent neural networks are structured to emulate the dynamical system. Further, a procedure to emulate multiple systems is suggested. A method for finding a minimal realization of a network is introduced. The minimization greatly reduces the complexity of the network without degrading the operating performance of the network. This work also examines the application of artificial neural networks for adaptive control. The multiple system approach is used to find an adaptive neural network controller for non-linear MIMO time-varying system in a direct model reference control scheme. The controller network is trained using a procedure called back-propagation through the plant, which was extended in this work. The application of neural networks is demonstrated on a longitudinal model of the F/A-18A fighter aircraft both with the undamaged aircraft and with a damage mechanism as a time-varying MIMO dynamical system.http://archive.org/details/identificationco00drorLieutenant Commander, Israeli NavyApproved for public release; distribution is unlimited

Spectral Embedding Norm

Anomaly detection is a data partitioning algorithm which separates outliers from normative data points. An unsupervised learning approach to this problem does not assume any prior information. Anomaly detection is a primary data analysis task with diverse applications and has been studied under many models and assumptions.Spectral methods such as spectral clustering have been widely used to solve the clustering problem. These methods make use of the leading K eigenvectors of the graph Laplacian matrix to detect K clusters, if the graph has a clear community structure. In a setting where the data consists of unbalanced clusters, as in anomaly detection, the spectral properties are determined by a dominating component. In such cases, traditional graph clustering methods fail, while the spectral embedding norm was found to overcome this challenge. This thesis generalizes the spectral embedding norm definition, formerly used, by introducing the capability of a weighted norm. With just one simple natural condition: allowing limited connectivity between the clusters and the background, we prove that this quantity can be used to detect the clusters of interest. Experiments on both synthetic data sets and real-world defect detection images demonstrate the effectiveness of the algorithm and its performance was found to be stable, with respect to parameter choices