In this paper, smartphone based aquatic debris monitoring robot design is proposed and discussed. Regularly monitoring aquatic waste or debris is of more interest to the environments, aquatic life, human health, and water transport. This paper presents the design of a robotic fish system that integrates an Android smartphone and a robotic fish for debris monitoring. The smartphone based aquatic robot can accurately detect debris in the presence of various environments.

There is an increasing trend of using automated and robotic systems for the tasks that are hazardous or inconvenient and dirty for humans. Sewers maintenance and cleaning is such a task where robots are already being used for inspection of underground pipes for blockages and damage. This paper reviews the existing robotic systems and various platforms and algorithms along with their capabilities and limitations being discussed. A typical mid-size city in a developing country, Pune, India is selected in order to understand the concerns and identify the requirements for developing robotic systems for the same. It is found that major concern of sewers are blockages but there is not enough information on both real-time detection and removal of it with robotic systems. On-board processing with computer vision algorithms has not been efficiently utilized in terms of performance and determinations for real-world implementations of sewer robotic systems. The review highlights the available methodologies that can be utilized in developing sewer inspection and cleaning robotic systems.

For pregnant ladies, various health parameters like ECG, blood pressure, SPO2 (stamina), respiration rate, blood glucose level, body temperature, etc. need to be monitored regularly and must be in a normal level. If the mother’ health become critical then definitely it will affect the baby. Hence it is recommended by physicians to do routine checkups at primary stages of pregnancy. But in rural areas, due to unavailability of well-equipped hospital facilities and also people don’t have awareness about their health which yields in abnormalities or creates critical issues. The presented paper summarizes, the available system and their strength and weakness and challenges in health monitoring of pregnant women. The proposed system is used to analyze various pregnancy biological factors like heart rate of pregnant women & Fetus, changes in blood pressure, blood glucose level, temperature, and weight. The proposed system will help for the rapid decision making and treatment through the high speed medical data transfer to the physicians over mobile for consulting and remote medical examinations. These values are transferred to database, to upload on server which can be monitored from any corner of the world.

Computer vision in consideration of automated and robotic systems has come up as a steady and robust platform in sewer maintenance and cleaning tasks. The AI revolution has enhanced the ability of computer vision and is being used to detect problems with underground sewer pipes, such as blockages and damages. A large amount of appropriate, validated, and labeled imagery data is always a key requirement for learning AI-based detection models to generate the desired outcomes. In this paper, a new imagery dataset S-BIRD (Sewer-Blockages Imagery Recognition Dataset) is presented to draw attention to the predominant sewers’ blockages issue caused by grease, plastic and tree roots. The need for the S-BIRD dataset and various parameters such as its strength, performance, consistency and feasibility have been considered and analyzed for real-time detection tasks. The YOLOX object detection model has been trained to prove the consistency and viability of the S-BIRD dataset. It also specified how the presented dataset will be used in an embedded vision-based robotic system to detect and remove sewer blockages in real-time. The outcomes of an individual survey conducted at a typical mid-size city in a developing country, Pune, India, give ground for the necessity of the presented work.