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Translation is the transfer of the meaning of a text from one language to another. It is a means of sharing information across languages and therefore essential for addressing information inequalities. The work of translation was originally carried out by human translators and its limitations led to the development of machine translators. Machine Translation is a subfield of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. There are different approaches to machine translation. This paper reviews the two major approaches (single vs. hybrid) to machine translation and provides critique of existing machine translation systems with their merits and demerits. Several application areas of machine translation and various methods used in evaluating them were also discussed. Our conclusion from the reviewed literatures is that a single approach to machine translation fails to achieve satisfactory performance resulting in lower quality and fluency of the output. On the other hand, a hybrid approach combines the strength of two or more approaches to improve the overall quality and fluency of the translation.

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Hypertensive-patient monitoring is a continuous process of observing closely the situation of patient's blood pressure and alerting the appropriate personnel in case of any anomaly. It usually requires the use of non-invasive sensors that are hardwired to bedside monitors. Although, present systems allow continuous monitoring of patient vital signs and limit the patient to the bed, the readings are mostly stored on the system local memory over a period of time before it is assessed for analysis. Hence, the need for a real time hypertensive patients’ monitoring system which can meet up with immediate demands of emergency cases. This paper presents a Wireless Sensor Network (WSN)-based health monitoring system that addressed the aforementioned drawbacks for monitoring hypertensive in-patients. The design of the system comprises of hardware components such as blood pressure sensor, Bluetooth serial communication circuit, sensor node for base station interfaces and software components. Performance evaluation of the designed system gave an accuracy of 89.7% in blood pressure monitoring. The system is also cost effective, reliable and user friendly when compared with existing systems. Keywords— Blood Pressure, Health monitoring, Hypertension, Wireless Sensor Networks

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Handwritten character recognition has applications in several industries such as Banking for reading of cheques and Libraries/ National archives for digital searchable storage of historic texts. The main feature typically used for the recognition task is the character image. However, there are other possible features such as the hand (left or right) used by author, number of strokes and other geometric features that can be captured when writing on digital devices. This paper investigates the effect of using some non-image features on the recognition rate of three classifiers: Instance Based Learner (IBk), Support Vector Machines (SVM) and the Multilayer Perceptron (MLP) Neural Network for singly-written alpha-numeric character recognition. Our experiments were conducted using the WEKA machine learning tool on offline and online handwritten acquired locally. A percentage split (66%-34% train-test) evaluation methodology was adopted with the classification accuracy measured. Results indicate that non-image additional features improved the accuracy across the three classifiers for the online and offline character datasets. However, this improvement was not statistically significant. SVM gave the best accuracy for the online dataset while IBk performed better than the other two classifiers for the offline dataset. We intend to investigate the effect of non-image features at other levels of text granularity such as words and sentences. 5156 | P a g e S e p t e m b e r 2 3 , 2 0 1 4

In this paper, the design of an automatic window is presented. The proposed window closes and opens automatically during and after a rainfall. The automatic system was developed with a focus on hospitals in order to allow medical staff and other supporting staff to concentrate on their primary responsibilities of taking care of patients. The system design includes a PIC16F877A microcontroller which gets activated when a moisture detector sensor sends a high logic signal to it. The microcontroller executes its embedded program by activating the stepper motor through a ULN2003 current-dependent integrated circuit (IC) chip resulting in stepwise control of the window. Hence, the window is automatically closed when rainfall is detected but opens and remains open when no rain is detected. We intend to extend our design to automatic opening and closing of the windows at others times in addition to during and after rainfall; for instance, window opening and closing every day at specific times in the morning and evening respectively.