Handwritten Character Recognition of South Indian Scripts: A Review

Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telungu.Comment: Paper presented on the "National Conference on Indian Language Computing", Kochi, February 19-20, 2011. 6 pages, 5 figure

Artificial Neural Networks applied to improve low-cost air quality monitoring precision

It is a fact that air pollution is a major environmental health problem that affects everyone, especially in urban areas. Furthermore, the cost of high-end air pollution monitoring sensors is considerably high, so public administrations are unable to afford to place an elevated number of measuring stations, leading to the loss of information that could be very helpful. Over the last few years, a large number of low-cost sensors have been released, but its use is often problematic, due to their selectivity and precision problems. A calibration process is needed in order to solve an issue with many parameters with no clear relationship among them, which is a field of application of Machine Learning. The objectives of this project are first, integrating three low-cost air quality sensors into a Raspberry Pi and then, training an Artificial Neural Network model that improves precision in the readings made by the sensors.Es un hecho que la contaminación del aire es un gran problema para la salud a nivel mundial, especialmente en zonas urbanas. Además, el coste de los sensores de contaminación de gama alta es considerablemente alto, por lo que los organismos públicos no pueden permitirse emplazar un gran número de estaciones de medida, perdiendo información que podría ser muy útil. A lo largo de los últimos años, han surgido muchos sensores de contaminación de bajo coste, pero su uso suele ser complicado, ya que tienen problemas de selectividad y precisión. Los objetivos de este proyecto son primero integrar tres sensores de contaminación de bajo coste en una Raspberry Pi y sobre todo, entrenar un modelo basado en una red neuronal artificial que mejore la precisión de las lecturas realizadas por los sensores.Està demostrat que la contaminació de l'aire és un gran problema per a la salut a nivell mundial, especialment en zones urbanes. A més, el cost dels sensors de contaminació de gama alta és considerablement alt, motiu pel qual els organismes públics no es poden permetre emplaçar una gran quantitat d'estacions de mesura, perdent informació que podria resultar molt útil. Al llarg dels últims anys, han sorgit molts sensors de contaminació de baix cost, però el seu ús és sovint complicat, ja que tenen problemes de selectivitat i precisió. Els objectius d'aquest projecte són primer de tot integrar tres sensors de contaminació de baix cost en una Raspberry Pi i sobretot, entrenar un model basat en una xarxa neuronal artificial que millori la precisió de les lectures realitzades pels sensors

Advances in quantum machine learning

Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.Comment: 38 pages, 17 Figure