There is a growing demand for the environmental pollution monitoring and control systems. In the view of ever increasing sources of toxic chemicals, these systems should have the facilities to detect and calibrate the source quickly. Toxic gases are the ones that cause health impact but humans are being exposed to it in various situations. These gases have to be monitored such that increase in the normal level of them could be known and proper precaution measures can be undertaken. So, an embedded system is designed using a microcontroller with internet of things, for the purpose of detecting and monitoring the hazardous gas leakage, which aids in the evasion of endangering of human lives. The hazardous gases can be sensed and displayed each and every second, in proximity to one more sensor for tracking heart beats which help to monitor the condition of the sewer labourers. If both the gases along with a pulse detector exceeds the normal level then an alarm is generated immediately and also an alert warning message can be sent to the authorised administrator and as well to the nearest health center to make the sewer labourers feel comfortable with necessary first aid and possibilities with the treatment in the case of emergency. Once the message is received by the health center, they enforce their team with necessary first aid to the current location to save the sewer labourer. Once this system is established for a particular user this will completely become fully automated and does not need any other additional people for monitoring and alerting purpose. It has an advantage over the manual method in offering quick response time and accurate detection of an emergency.

A wireless body-centric nano-network consists of various nano-sized sensors with the purpose of healthcare application. One of the main challenges in the network is caused by the very limited power that can be stored in nano-batteries in comparison with the power required to drive the device for communications. Recently, novel rectifying antennas (rectennas) based on carbon nanotubes (CNTs), metal and graphene have been proposed. At the same time, research on simultaneous wireless information and power transfer (SWIPT) schemes has progressed apace. Body-centric nano-networks can overcome their energy bottleneck using these mechanisms. In this Letter, a nano-rectenna energy harvesting model is developed. The energy harvesting is realised by a nano-antenna and an ultra-high-speed rectifying diode combined as a nano-rectenna. This device can be used to power nanosensors using part of the terahertz (THz) information signal without any other system external energy source. The broadband properties of nano-rectennas enable them to generate direct current (DC) electricity from inputs with THz to optical frequencies. The authors calculate the output power generated by the nano-rectenna and compare this with the power required for nanosensors to communicate in the THz band. The calculation and analysis suggest that the nano-rectenna can be a viable approach to provide power for nanosensors in body-centric nano-networks.

One of the major causes of eye blindness is identified to be as diabetic retinopathy, which if not detected in earlier stage would cause a serious issue. Long-term diabetes causes diabetic retinopathy. The significant key factor leading to diabetic retinopathy is exudates which affect the retina part and causes eye defects. Thus the first and foremost task in the automated detection of macular oedema is to detect the presence of these exudates. The authors use image processing techniques to detect the optic disc, exudates and the presence of macular oedema. Their method has the sensitivity 96.07%, selectivity 97.36%, and accuracy 96.62% for the exudates detection and in the case of macular oedema detection the sensitivity 97.75%, selectivity 100%, and accuracy 98.86% is achieved. The performance comparison with other methods reveals that their method can be used as a screening process for diabetic retinopathy. In addition to that, the algorithm can help to detect macular oedema.

Pain is an unpleasant subjective experience. At present, clinicians are using self-report or pain scales to recognise and monitor pain in children. However, these techniques are not efficient to observe the pain in children having cognitive disorder and also require highly skilled observers to measure pain. Using these techniques it is also difficult to choose the analgesic drug dosages to the patients after surgery. Thus, this conceptual work explains the demand for automatic coding techniques to evaluate pain and also it documents some evidence of techniques that act as an alternative approach for objectively determining pain in children. In this review, some good indicators of pain in children are explained in detail; they are facial expressions from an RGB image, thermal image and also feature from well proven physiological signals such as electrocardiogram, skin conductance, body temperature, surgical pleth index, pupillary reflex dilation, analgesia nociception index, photoplethysmography, perfusion index etc.

Cancer is one of the deadly diseases of human life. The patient may likely to survive if the disease is diagnosed in its early stages. In this Letter, the authors propose a genetic search fuzzy rough (GSFR) feature selection algorithm, which is hybridised using the evolutionary sequential genetic search technique and fuzzy rough set to select features. The genetic operator's selection, crossover and mutation are applied to generate the subset of features from dataset. The generated subset is subjected to the evaluation with the modified dependency function of the fuzzy rough set using positive and boundary regions, which act as a fitness function. The generation and evaluation of the subset of features continue until the best subset is arrived at to develop the classification model. Selected features are applied to the different classifiers, from the classifiers fuzzy-rough nearest neighbour (FRNN) classifier, which outperforms in terms of classification accuracy and computation time. Hence, the FRNN is applied for performance analysis of existing feature selection algorithms against the proposed GSFR feature selection algorithm. The result generated from the proposed GSFR feature selection algorithm proved to be precise when compared to other feature selection algorithms.