With the progression of advancements in technology, several innovations has been made in the field of communications that are transiting to Internet of Things. In this domain, Wireless Sensor Networks (WSN) are one of those independent sensing devices to monitor physical and environmental conditions along with thousands of applications in other fields. As air pollution being a major environmental change that causes many hazardous effects on human beings that need to be controlled. Hence, we deployed WSN nodes for constant monitoring of the air pollution around the city and the moving public transport buses and cars. This methodology gave us the monitoring data from the stationary nodes deployed in the city to the mobile nodes on Public Transport buses and cars. The data of the air pollution particles such as gases, smoke and other pollutants is collected via sensors on the Public transport buses and the data is being analyzed when the buses and cars reach back to the source destination after passing through the stationary nodes around the city. Our proposed architecture having innovative mesh network will be more efficient way of gathering data from the nodes of WSN. It will have lots of benefits with respect to the future concept of Smart Cities that will have the new technologies related to Internet of Things.

Presumptive identification of different Enterobacteriaceae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochemical property of the unknown sample with known reference samples and inference of its identity based on the maximum similarity pattern with the known samples. This process is labor-intensive, time-consuming, error-prone, and subjective. Therefore, automation of sorting and similarity in calculation would be advantageous. Here we present a MATLAB-based graphical user interface (GUI) tool named BioCluster. This tool was designed for automated clustering and identification of Enterobacteriaceae based on biochemical test results. In this tool, we used two types of algorithms, i.e., traditional hierarchical clustering (HC) and the Improved Hierarchical Clustering (IHC), a modified algorithm that was developed specifically for the clustering and identification of Enterobacteriaceae species. IHC takes into account the variability in result of 1–47 biochemical tests within this Enterobacteriaceae family. This tool also provides different options to optimize the clustering in a user-friendly way. Using computer-generated synthetic data and some real data, we have demonstrated that BioCluster has high accuracy in clustering and identifying enterobacterial species based on biochemical test data. This tool can be freely downloaded at http://microbialgen.du.ac.bd/biocluster/.

The software implementation of Dyadic Digital Pulse Modulators (DDPMs) for Digital to Analog (D/A) conversion is addressed in this paper. In particular, an enhanced software DDPM implementation is proposed and compared with a plain, iterative software transposition of the basic DDPM hardware architecture. Experimental results on an 8-bit software-defined DDPM D/A converter implemented on a Texas Instrument c2000 microcontroller platform validate the approach, revealing for the novel optimized software DDPM a 6X maximum sample rate compared to the simple iterative implementation on the same microcontroller and at the same system clock frequency. Based on measurements, an 8-bit DDPM DAC featuring the proposed optimized implementation operates at 7.8kS/s with a maximum INL of 1.64LSB, a maximum DNL of 1.79LSB, an SFDR of 47.02dB and a SNDR of 45.27dB, corresponding to 7.23 ENOB, demonstrating the effectiveness and the applicability of the proposed approach to implement a low cost, software-defined D/A converters in microcontroller-based embedded systems.

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