The real-time location of people remains a subject of research, particularly for those suffering from Alzheimer disease. In indoor the powerful method is to localize RFID (Radio Frequency Identification) tags carried by people, but in outdoor we can easily use GPS (Global Positioning System) for its accuracy. In this paper, we study the issue of the implementation of a system for tracking Alzheimer patients who can be lost once out. For this we propose a prototype of an autonomous and wireless system combining the two technologies that enables getting informations about the position of Alzheimer patient from the intelligent tag, also warn about his absence. Finally, we propose how to incorporate this prototype in a ZigBee network to make it usable in wider fields and applications.

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<p class="0abstract"><span lang="EN-US">One of the primary concerns of the World Health Organization is the improvement of medical care by reducing adverse events in the medication process and enhancing the safety of patients. These issues are mainly related to the management of expensive and high-risk medicines in hospitals. In this paper, we enhanced medication management by minimizing the possibility of medication errors from its prescription-validation to its preparation. For this purpose, we designed a hospital and pharmacy services management system by employing digital signature using Radio Frequency IDentification technology. Our proposed system is equipped with ESP8266 modules, RFID readers and tags which allow the detection of taking medications and ensures an automated medication management. </span></p>

Locating services have come under the spotlight in recent years in various applications. However, locating methods that use received signal strength have low accuracy due to signal fluctuations. For this purpose, we present a Wi-Fi based locating system using artificial neural network to enhance the positioning process performances. We optimized the Levenberg Marquardt algorithm to propose the better configuration of the multi-layer time-delay perception neural network. We achieved an average error of 10.3 centimeters with a grid of 0.4 meter in four tests. Yet, due to the instability of the received signal strength RSS-based locating systems present a limitation in the resolution finesse that depends on the grid size.

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