A SIMPLE GIBBS SAMPLER FOR THE STATE ESTIMATION IN WIRELESS COMMUNICATIONS

In the recent years, wireless communications are extremely useful in many disciplines including health monitoring, environment monitoring, signal processing etc. State estimation and prediction are quite challenging tasks in wireless communications. Traditionally, in the literature, dynamic state-space models have been used for the state estimation and predic- tion purpose. The estimation method is based on Kalman-Filter which is computationally demanding. In this work, we consider computationally simpler Gibbs sampler algorithm for the state estimation. We consider three different cases, (i) continuous state values, (ii) binary (0/1) state values, and (iii) categorical state values with more than two categories. We consider a simple linear model for the prediction purpose, and the underlying regression coefficients are estimated by Gibbs sampler. We compute the misclassification proportions for assessing the practical usefulness of our estimation approach. Areal dataset where 200 wireless sensor nodes are used for measuring the temperature of a chamber is analysed in this work

Myositis And Immune Reconstitution Inflammatory Syndrome In An Immuno Compromised Patient: A Therapeutic Conundrum

Microsporidia are ubiquitous fungi that may infect animals, fish, insects as well as humans. Human infection is uncommon and only seen in the immunocompromised individual. Here, we report the case of a 40-year-old known AIDS patient presented with severe myalgia and inability to walk. Investigations revealed microsporidial myositis. Her treatment was further complicated by the development of acute inflammatory demyelinating polyneuropathy (AIDP) 2 weeks later. However, treating human immunodeficiency virus, microsporidiosis, and AIDP as a consequence of immune reconstitution inflammatory syndrome concomitantly led to clinical improvement