Mohammad Hadi
Mohammad Hadi Eghlidi, Zuerich CH
Patent application number | Description | Published |
---|---|---|
20140205761 | METHOD FOR NANO-DRIPPING 1D, 2D OR 3D STRUCTURES ON A SUBSTRATE - A method for the production of nano- or microscaled ID, 2D and/or 3D depositions from an solution ( | 07-24-2014 |
Mohammad Hadi Izadi, Delta CA
Patent application number | Description | Published |
---|---|---|
20080259182 | High Gain Digital Imaging System - The present invention provides digital imaging architectures comprising detectors coupled to readout circuitry, wherein the readout circuitry is capable of providing large amplification to small, noise sensitive input signals to improve their noise immunity, as well as capable of providing a fast pixel readout time. The readout circuitry comprises an on-pixel amplification transistor as well as additional transistors used to read out the amplified signal and/or to reset the amplified output signal stored by a portion of the circuit prior to reading a subsequent signal. The present invention also provides readout circuitry that is capable of providing large amplification and thus additional noise immunity to the input signal from the detector by implementing another amplification stage within the readout circuitry. The readout circuitry can function in particular modes, the use of which can depend on characteristics of the input signals transferred to the readout circuitry from the detectors, or can depend on the characteristics of the output signal required from the readout circuitry. | 10-23-2008 |
Mohammad Hadi Mashinchi, Newtown AU
Patent application number | Description | Published |
---|---|---|
20130254141 | APPARATUS AND METHOD FOR ANALYSING EVENTS FROM SENSOR DATA BY OPTIMISATION - The present invention relates to sensor signal analysis. It relates particularly, but not exclusively, to methods, systems and devices for monitoring and processing the sensor signals to determine automatically characteristics of events represented by the sensor signals. The present invention is particularly, but not exclusively, related to methods, systems and devices for monitoring moisture in absorbent articles such as diapers, incontinence garments, dressings and pads resulting from wetness events caused by, for example, urinary and/or faecal incontinence. In an embodiment, the invention includes a method for processing sensor signals representing an event in an absorbent article. The method comprises: receiving sensor signals from a sensor representing one or more events in an absorbent article; and processing the sensor signals to determine a characteristic of at least one event in the absorbent article. One such characteristic can include the volume of a voiding event such as a urinary incontinence event. In another embodiment, the method includes carrying out a learning phase including the steps of: receiving sensor signals representing one or more events in each of one or more absorbent articles; receiving observation data indicative of a cumulative characteristic of the one or more events in each absorbent article; and identifying an optimal mathematical model describing a relationship between the sensor signals and the observation data. Such events can include urinary incontinence events occurring in absorbent articles such as diapers. Observation data can be measured cumulative volume of a cycle of voiding events occurring in a diaper. | 09-26-2013 |
Mohammad Hadi Mashinchi, North Bondi AU
Patent application number | Description | Published |
---|---|---|
20140244644 | EVENT DETECTION ALGORITHMS - A method for analysing incoming data, comprising the steps of processing the incoming data in segments to output a sequence of segment types by extracting one or more properties of an incoming data segment and forming an Unknown Property Vector for each segment of data in the incoming data, and processing the sequence of segment types to identify events in the incoming data. The sequence of segment types is determined, for each segment, by reference to a set of Reference Property Vectors that are relevant to the Unknown Property Vector. This may involve application of first and/or second and/or further functions to identify at least a first subset of Reference Property Vectors that are relevant to the Unknown Property Vector. Alternatively, a logistic regression algorithm, derived using clustering or classification methods for identifying candidate vectors, may be used. | 08-28-2014 |