With improved technological successions, wireless communication applications have been incessantly evolving. Owing to the challenges posed by the multipath wireless channel, radio design prototypes have become elemental in all wireless systems before deployment. Further, different signal processing requirements of the applications, demand a highly versatile and reconfigurable radio such as Software Defined Radio (SDR) as a crucial device in the design phase. In this paper, two such SDR modules are used to develop an Orthogonal Frequency Division Multiplexing (OFDM) wireless link, the technology triumphant ever since 4G. In particular, a non-coherent end-to-end OFDM wireless link is developed in the Ultra High Frequency (UHF) band at a carrier frequency of 470 MHz. The transmitter includes Barker sequences as frame headers and pilot symbols for channel estimation. At the receiver, pulse alignment using Max energy method, frame synchronization using sliding correlator approach and carrier offset correction using Moose algorithm are incorporated. In addition, wireless channel is estimated using Least Square (LS) based pilot aided channel estimation approach with denoising threshold and link performance is analyzed using average Bit Error Rate (BER), in different pilot symbol scenarios. In a typical laboratory environment, the results of BER versus receiver gain show that with 4 pilot symbols out of 128 carriers, at a gain of 20 dB, BER is 0.160922, which is reduced to 0.136884 with 16 pilot symbols. The developed link helps OFDM researchers to mitigate different challenges posed by the wireless environment and thereby strengthen OFDM technology.

The electromagnetic field (EMF) is an environmental factor affecting living organisms. The aim of this study was to demonstrate the effect of an extremely low frequency electro- magnetic field (ELF-EMF) on selected chemical components of the honeybee (Apis mellifera L.) using Fourier Transform Infrared (FTIR) spectroscopy. The FTIR method provides information on the chemical structure of compounds through identification and analysis of functional groups. The honeybees were treated with EMF at a frequency of 50 Hz and magnetic induction of 1.6 mT for 2, 6, 12, 24 and 48 hours. Analysis of FTIR spectra showed that EMF exposure longer than 2 hours induced changes in the structure of chemical compounds, especially in the IR region corresponding to DNA, RNA, phospholipids and protein vibrations, compared to control samples (bees not EMF treated). The results confirm the effect of EMF on bees depending on the duration of exposure.

The subject of the article is the design and practical implementation of the wireless mesh network. IQRF radio modules were used for the network design. The IQRF® technique has enabled the construction of a mesh network with the possibility of reconfiguration. The theoretical part contains a description of the IQRF® hardware solutions used. The practical scope includes the design part, where the configuration of the radio modules was carried out and the parameters of the radio network were set to allow its implementation in various topologies. Then, a wireless network consisting of 10 IQRF modules was launched in the P3 building of the Opole University of Technology. At this stage, configured radio modules were placed in selected rooms on all five floors of the building in order to carry out tests of the radio network constructed in this way. The tests included measuring the packet transmission delay time as well as the received signal strength. Research was carried out for several network topologies.

Medicinal plants have a huge significance today as it is the root resource to treat several ailments and medical disorders that do not find a satisfactory cure using allopathy. The manual and physical identification of such plants requires experience and expertise and it can be a gradual and cumbersome task, in addition to resulting in inaccurate decisions. In an attempt to automate this decision making, a data set of leaves of 10 medicinal plant species were prepared and the Gray-level Co-occurence Matrix (GLCM) features were extracted. From our earlier implementations of the several machine learning algorithms, the k-nearest neighbor (KNN) algorithm was identified as best suited for classification using MATLAB 2019a and has been adopted here. Based on the confusion matrices for various k values, the optimum k was selected and the hardware implementation was implemented for the classifier on FPGA in this work. An accuracy of 88.3% was obtained for the classifier from the confusion chart. A custom intellectual property (IP) for the design is created and its verification is done on the ZedBoard for three classes of plants.