Project report on dtmf based door opening systemMukul Mohal
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The document describes a cell phone security system that allows a user to unlock a door using their cell phone. It works by using DTMF (dual tone multi-frequency) signaling, where each button on the phone's keypad generates a unique tone. When the user calls their home security system and enters a valid password using their phone's keypad, the system verifies the password using the DTMF tones and automatically unlocks the door. The project aims to automate the manual process of unlocking a door with a physical key by allowing it to be unlocked remotely using a cell phone. It provides convenience by allowing entry without needing to get out of a car to unlock the door.
This document describes a new algorithm called GRAph ALigner (GRAAL) for topological network alignment. GRAAL aims to provide a unique global alignment of nodes between two networks based solely on topological similarity, without using other a priori information. It works by first finding a dense core between the networks to seed the alignment, then expanding outwards in spheres of increasing radius to align additional nodes. The alignment score is based on the percentage of edges from the first network that are correctly aligned to edges in the second network. The algorithm is tested on sample networks and achieves an edge correctness of 0.089.
DnaBind is a hybrid algorithm that uses machine learning and template-based methods to predict DNA-binding residues in proteins, showing strong performance. This approach leverages statistical features and structural properties to improve classification accuracy compared to other software. The hybrid method demonstrates effectiveness in reducing the time and cost associated with experimental determination of protein-DNA interactions.
This study evaluated changes in hepatitis B virus (HBV) quasispecies diversity and complexity during lamivudine treatment. 25 patients were treated with lamivudine for 4 weeks. Viral DNA was cloned from patients at baseline and 4 weeks. Patients were classified as responders or non-responders based on HBV DNA and liver enzyme levels. Responders had lower quasispecies complexity and diversity after 4 weeks compared to non-responders, and responders were more likely to be monoclonal while non-responders were polyclonal.
This document presents a new statistical method called smooth isotonic regression (sIR) to calibrate predictive models. sIR improves upon existing calibration methods like logistic regression (LR) and isotonic regression (IR) by using spline curves to provide a smooth, non-parametric calibration curve. The sIR method was shown to outperform LR and IR in calibration based on both simulation data and analysis of a real biological dataset, providing calibrated predicted values that better reflected the actual observed values.
DnaBind is a hybrid algorithm that uses machine learning and template-based methods to predict DNA-binding residues in proteins, showing strong performance. This approach leverages statistical features and structural properties to improve classification accuracy compared to other software. The hybrid method demonstrates effectiveness in reducing the time and cost associated with experimental determination of protein-DNA interactions.
This study evaluated changes in hepatitis B virus (HBV) quasispecies diversity and complexity during lamivudine treatment. 25 patients were treated with lamivudine for 4 weeks. Viral DNA was cloned from patients at baseline and 4 weeks. Patients were classified as responders or non-responders based on HBV DNA and liver enzyme levels. Responders had lower quasispecies complexity and diversity after 4 weeks compared to non-responders, and responders were more likely to be monoclonal while non-responders were polyclonal.
This document presents a new statistical method called smooth isotonic regression (sIR) to calibrate predictive models. sIR improves upon existing calibration methods like logistic regression (LR) and isotonic regression (IR) by using spline curves to provide a smooth, non-parametric calibration curve. The sIR method was shown to outperform LR and IR in calibration based on both simulation data and analysis of a real biological dataset, providing calibrated predicted values that better reflected the actual observed values.
