An adaptive array for interference rejection

Adaptive array based on feedback system for rejection of interfering signal

The Mutual Interpretation of Active and Passive Microwave Sensor Outputs

Mutual interpretation of active and passive microwave sensor output

Adaptive optimization of signal to noise ratio in receiving arrays

Receiving dipole antenna array signal to noise ratio optimization based on steepest descent metho

Millimeter-wavelengths propagation studies Annual status report, Sep. 1, 1967 - Aug. 31, 1968

Millimeter wavelength propagation studies related to ATS-E communication transmission experimen

New names for old strains? Wolbachia wSim is actually wRi

A response to Serendipitous discovery of Wolbachia genomes in multiple Drosophila species by SL Salzberg, JC Dunning Hotopp, AL Delcher, M Pop, DR Smith, MB Eisen and WC Nelson. Genome Biology 2005, 6:R2

Parallelization of chip-based fluorescence immuno-assays with quantum-dot labelled beads

This paper presents an optical concept for the read-out of a parallel, bead-based fluorescence immunoassay conducted on a lab-on-a-disk platform. The reusable part of the modular setup comprises a detection unit featuring a single LED as light source, two emission-filters, and a color CCD-camera as standard components together with a spinning drive as actuation unit. The miniaturized lab-on-a-disk is devised as a disposable. In the read-out process of the parallel assay, beads are first identified by the color of incorporated quantum dots (QDs). Next, the reaction-specific fluorescence signal is quantified with FluoSpheres-labeled detection anti-bodies. To enable a fast and automated read-out, suitable algorithms have been implemented in this work. Based on this concept, we successfully demonstrated a Hepatitis-A assay on our disk-based lab-on-a-chip

A Frequency-Controlled Magnetic Vortex Memory

Using the ultra low damping NiMnSb half-Heusler alloy patterned into vortex-state magnetic nano-dots, we demonstrate a new concept of non-volatile memory controlled by the frequency. A perpendicular bias magnetic field is used to split the frequency of the vortex core gyrotropic rotation into two distinct frequencies, depending on the sign of the vortex core polarity $p=\pm1$ inside the dot. A magnetic resonance force microscope and microwave pulses applied at one of these two resonant frequencies allow for local and deterministic addressing of binary information (core polarity)

GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos

The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly detection using deep learning is therefore mainly constrained to generative models such as generative adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer from general deep learning issues and are hard to properly train. In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM architecture specifically for anomaly detection in complex videos such as surveillance footage. We have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation results and online learning capabilities prove the great potential of using our system for real-time unsupervised anomaly detection in complex videos

Learning and generation of long-range correlated sequences

We study the capability to learn and to generate long-range, power-law correlated sequences by a fully connected asymmetric network. The focus is set on the ability of neural networks to extract statistical features from a sequence. We demonstrate that the average power-law behavior is learnable, namely, the sequence generated by the trained network obeys the same statistical behavior. The interplay between a correlated weight matrix and the sequence generated by such a network is explored. A weight matrix with a power-law correlation function along the vertical direction, gives rise to a sequence with a similar statistical behavior.Comment: 5 pages, 3 figures, accepted for publication in Physical Review