Nowadays, Internet of Things (IoT) makes everything interconnected and perceived as the succeeding specialized rebellion. There are numerous applications of IoT such as smart automation at home, smart city, smart vehicle parking, in various agriculture fields, industrial automation process and health monitoring system. In human health monitoring system, IoT makes health equipment more effective by permitting real-time intensive care of patient health, in which various sensors take statistics of the patient and diminish humanoid error. IoT is emerging as a most important platform for numerous amenities and applications. In IoT, health monitoring system monitors patient's health remotely outside the hospital settings. This system continuously uploads patient's report on cloud system which helps the doctor to study in detail the variations in the patient's health conditions. Remote health monitoring structure is totally built on Global System for Mobile (GSM) network. By means of this system, the time period of both doctor and patients is saved; in addition, the surgeon can also assist in an emergency situation. The paper describes various techniques of human health monitoring system using IoT devices. Raspberry Pi and FPGA platform are mostly used to set up a platform for human health monitoring.

In the last few years, wireless communication technologies have undergone several advancements. In this context, Unmanned Aerial Vehicles (UAVs) play a vital role. It is a big challenge to get accurate data with minimum collision in UAVs. The features of Direct Sequence Spread Spectrum (DSSS) like multiple access and antijamming are useful to get accurate data with minimum collision. DSSS can transmit and control data. DSSS is a common technique for data transmission. As the number of users increases, DSSS may not be sufficient to transmit and control data. To overcome this problem, we propose a novel hybrid technique for data transmission. The paper introduces a hybrid approach with the combination of Time Division Multiple Access (TDMA) with DSSS. TDMA can divide the frame into equal time slots, so it is easy to access more number of users with minimum data collision. In DSSS, the user data is multiplied with Barker code for spreading and TDMA allocates time slots to each user to allow the same frequency channel by dividing the signal into time slots. The paper uses 13-bit Barker code with the spread spectrum, i.e.,13 users can allow at a time and Bit Error Rate (BER) is evaluated. The work can be carried out in MATLAB.

Image segmentation is a process of dividing the image into some distinct regions. These regions are specially coherent in nature and have similar attributes. This technique is widely used for image analysis and to interpret the desired feature. The paper studies the Hidden Markov Random Fields (HMRF) and finds its Expectation Maximization (EM) algorithms. The main idea behind developing HMRF is to adjoin the "data faithfulness" and "model smoothness" which has very similar nature with the active contours, Gradient Vector Flow (GVF), graph cuts and random walks. The paper also uses HMRF-EM along with the Gaussian mixture models, and then color image segmentation process. These algorithms are implemented in MATLAB. In color image segmentation experiments, it is observed that the results obtained from HMRF segmentation are much smoother than the direct k-means clustering. The segmented object is much closer to the original shape than clustering. The segmentation time for Bacteria 1, Bacteria 2, SAR and brain images is 0.35, 0.43, 0.12 and 0.12, respectively. The accuracy for Bacteria 1, Bacteria 2, SAR and brain images is 97.70%, 98.06%, 98.89% and 97.35%, respectively.