No abstract

The road surface condition information is very useful for the safety of road users and to inform road administrators for conducting appropriate maintenance. Roughness features of road surface; such as speed bumps and potholes, have bad effects on road users and their vehicles. Usually speed bumps are used to slow motor-vehicle traffic in specific areas in order to increase safety conditions. On the other hand driving over speed bumps at high speeds could cause accidents or be the reason for spinal injury. Therefore informing road users of the position of speed bumps through their journey on the road especially at night or when lighting is poor would be a valuable feature. This paper exploits a mobile sensor computing framework to monitor and assess road surface conditions. The framework measures the changes in the gravity orientation through a gyroscope and the shifts in the accelerometer's indications, both as an assessment for the existence of speed bumps. The proposed classification approach used the theory of rough mereology to rank the modified data in order to make a useful recommendation to road users.

This paper proposed a Smart Wearable system that able to monitor the heart beat rate condition of patient. The heart beat rate is detected using photoplethysmogram (PPG). The signal is processed using ATmega32 Microcontroller to determine heart beat rate per minute. Then, it sends the heart rate represented as BPM to Android App Via Bluetooth Communication, Android app sends SMS alert to the mobile phone of medical experts or patient's family member, or their relatives via SMS contains user's current location, Android app calculates daily steps count. Android/Desktop app allow user to check nearest hospitals, cardiac centers, nearest Health centers (GYM) and also user's current location. Android/Desktop app allow user to know if he suffers from heart disease or not by one click which run a machine/Deep learning module that analyze user 's data to detect heart disease.