The pressure on the radio spectrum increases as more and more IoT devices are deployed, since most of them need to communicate via wireless technology. Spectrum availability and bandwidth are limited, and their shared use poses serious challenges when massive amounts of data need to be transmitted, even after the advent of 5G technology. In search of solutions, there is a growing interest in incorporating cognitive radio technologies into IoT devices [1]. A cognitive radio is a wireless transceiver that can adapt its behavior to the environment, for which the radio automatically selects the best channel in real time. The ultimate goal is the optimal and efficient use of the radio spectrum [2].

The key feature of cognitive radio devices is their spectrum sensing capability: they can detect whether a wireless channel is busy and, if so, recognize the type of modulation used in the channel. This is necessary to detect if a signal from a certain primary user, or even from an interferer, is present in the spectrum. To perform this recognition task, traditionally either matched filters or certain properties of the modulated signals, such as cyclostationarity, have been exploited [3, 4]. Furthermore, deep learning techniques have recently been reported to perform well in the categorization of radio communication signals [5].

However, the above techniques for modulated-signal recognition have high computational complexity and must be tuned or trained ‘off-line’, limiting transceivers to adapt to variations in the environment. In this communication, we will present a new algorithm for spectrum sensing and the categorization of modulated signals that has two main features: (i) it is unsupervised and can handle unforeseen situations in real-time and (ii) it is computationally simple, so that it can operate even with the limited capabilities of common IoT devices. The idea exploits properties of the L1-norm that have been explored in our previous works [6]. Experiments with real and simulated data will demonstrate the effectiveness of the proposed approach.

REFERENCES.

[1] A. Khan, M. Rehmani and A. Rachedi, “Cognitive-radio-based internet of things: Applications, architectures, spectrum related functionalities, and future research directions”, IEEE wireless communications, vol. 24, no. 3, pp. 17-25, 2017.

[2] S. Peyman and S. Haykin, “Fundamentals of cognitive radio”, John Wiley & Sons, 2017.

[3] P. Urriza, E. Rebeiz and D. Cabric, “Multiple antenna cyclostationary spectrum sensing based on the cyclic correlation significance test”, IEEE Journal of Selected Areas in Communications, vol. 31, no. 11, pp. 2185–2195, 2013.

[4] X. Zhang, R. Chai and F. Gao, “Matched filter based spectrum sensing and power level detection for cognitive radio network”, in IEEE Global Conference on Signal and Information Processing (Global SIP), Atlanta, pp. 1267–1270, Dec. 2014.

[5] T. O’Shea, T. Roy and T. Charles Clancy, "Over-the-air deep learning based radio signal classification", IEEE Journal of Selected Topics in Signal Processing, vol. 12, no.1, pp. 168-179, 2018.

[6] J. Camargo, R. Martín-Clemente, S. Hornillo-Mellado and V. Zarzoso, "L1-norm unsupervised Fukunaga-Koontz transform", Signal Processing, vol.182, 2021.

Two of the most relevant gasses correlating to honeybee colony health are likely carbon dioxide and humidity. There are a wide variety of sensors on the market for monitoring these gasses, covering a range of different sizes, prices and accuracy. The most accurate carbon dioxide sensors, at an appropriate physical size for use in honeybee hives, are based on Non-Dispersive Infra-Red (NDIR) detectors. In this work we investigate the use of two of these sensors . As molecular diffusion will severely impact the local equilibrium of any gas measurement, we investigate the most appropriate placement of the sensors by positioning them in the comb of a frame in the brood box, above a modified crown board and in the queen excluder. Both sensors also inherently provide relative humidity and temperature data. Data logging was provided by Teensy 3.5 microcontroller circuits with a current consumption low enough to allow battery deployment if required. With a temporal resolution of less than a minute and several thousands of hours of data for comparison, we present the daily and long-term trends in these important gasses in multiple honeybee colonies.

A series of MOX sensors, having CuO and CoO sensitive thick films were prepared using an eco-friendly technique (sol-gel). The sensor transducers are based on a custom made thick alumina wafer having Au or Pt interdigital electrodes (IDE) printed onto the alumina surface.

The sensing experiment took place inside a custom made ceramic sensing cell, with quartz walls, Teflon seal caps and a metallic plate with dual role: sample holder and sensor heater, due to a special electric heater insertion to insure corresponding working temperature of the sensor.

Sensor response (sensor electrical resistance variations, measured at the IDE contact pads) was recorded at different methane concentrations, using a RLC bridge having a GPIB interface linked with a computer, running custom made Labview based acquisition software.

The sensing experiment took place under lab conditions (dried target and carrier gas from gas cylinders), in a constant gas flow, with target gas concentrations in the 5-2000 ppm domain (calibrated by a mass-flow controller system or MFC) and a direct current (DC) applied to the IDE as sensor operating voltage.

Acknowledgements:

This research was funded by the Romanian National Authority for Scientific Research on Innovation, grant number PN-III-P2-2.1-PED-2019-2073.

Sensors with the electrochemically formed polymeric films as sensitive layer are of high interest in electroanalysis. Various monomers are successfully used for the sensors creation in particular compounds with phenolic moiety. Among them, natural phenolic acids and triphenylmethane dyes forming non-conductive polymeric coverages are of interest. Therefore, carbon nanomaterials are successfully applied as a platform for further electropolymerization of a suitable monomer. Phenolic acids (gallic and ellagic) and triphenylmethane dyes (thymolphthalein and aluminon) have been studied as monomers. Their potentiodynamic electropolymerization conditions (monomer concentration, supporting electrolyte type and pH, potential scan rate and range, number of cycles) on the surface of glassy carbon electrode (GCE) modified with multi- or functionalized single-walled carbon nanotubes and carbon nanofibers have been optimized. The electrode surface has been characterized with SEM, cyclic voltammetry, chronoamperometry and electrochemical impedance spectroscopy (EIS). Polymeric coverages exhibit porous structure with the shape of particles and their aggregates (folded structure with pores and channels in the case of polyaluminon) deposited on the surface of carbon nanomaterials. Modified electrodes have shown increase of the electroactive surface area and statistically significant decrease of the charge transfer resistance in comparison to bare GCE. The electrodes have shown a sensitive and selective response to different classes of the antioxidants (capsaicinoids, flavanones (hesperidin and naringin) and flavonols (rutin and quercetin)). The electrooxidation parameters of the antioxidants have been found. Under conditions of differential pulse voltammetry, the electrodes act as sensitive and selective sensors for capsaicinoids, flavanones and flavonols including possibility of the simultaneous quantification. The analytical characteristics obtained are improved vs. reported earlier for other electrochemical sensors. The practical applicability of the sensors has been demonstrated on food and plant samples. Thus, electropolymerized phenol-containing compound/carbon nanomaterial composites can be considered as a promising sensing platform in the antioxidants electroanalysis.

Essential oils are of high interest in analytical chemistry due to their bioactive properties and wide application area (in aromatherapy, medicine, and food industry). Gas chromatography with mass- spectrometric detection (GC-MS) is the golden standard in their characterization and investigations. On the other hand, the presence of volatile phenolics and terpenoids make it possible to use electrochemical methods for the characterization and screening of essential oils using antioxidant parameters in particular antioxidant capacity. Unfortunately, essential oils are fully out of consideration in modern electroanalysis from this point of view. Voltammetric behavior of essential oils (from 15 types of plant material) at the electrode modified with carboxylated multi-walled carbon nanotubes have been studied for the first time. All samples are electrochemically active in neutral medium under conditions of differential pulse voltammetry. There are well-pronounced signals on the voltammograms in the ranges of 0.0-0.75 and 0.75-1.5 V caused by electrooxidation of phenolic constituents and terpenoids, respectively that is confirmed by oxidation potential of individual standard compounds. Moreover, clear oxidation peaks for 9 essential oils are registered in the range of 0.75-1.5 V only that agrees well with GC-MS data on their major constituents. Two-step chronoamperometric method has been developed for the evaluation of the essential oils antioxidant capacity. Two anodic potentials of 0.80 and 1.4 V have been chosen for these purposes. The electrolysis steady-state is achieved at 75 s of electrolysis. The antioxidant capacity has been expressed as current consumed per 1 mL of essential oil. Screening of 37 samples of essential oils by their antioxidant capacity has been performed. The data obtained are compared to the standard antioxidant parameters (antioxidant activity towards 2,2-diphenyl-1-picrylhydrazyl and total phenolics).