Molecularly imprinted polymer nanoparticle-carbon nanotube composite electrochemical gas sensor for highly selective and sensitive detection of methanol vapour.

An electrochemical gas sensor has been fabricated using molecularly imprinted polymer nanoparticles (nanoMIPs) and multiwalled carbon nanotubes on screen-printed electrodes. Methanol vapour was chosen as the target due to its toxicity as its suitability as a model for more harmful pollutant gases. The sensor functions under ambient conditions and in the required concentration range, in contrast to all previous MIP-based gas sensors for methanol. The sensitivity of the sensor was greatly improved by the addition of multiwall carbon nanotubes, resulting in a limit of detection of approximately 10 ppm. The nanoMIPs provide an inherent selectivity for the target inherent in its design. Selectivity studies were performed with structurally analogous alcohols at various concentrations, demonstrating selectivity for methanol 12.1 times that for ethanol at 2 mmol dm-3 and 4.2 times that for ethanol at 1 mmol dm-3. Interactions with isopropanol and n-propanol were found to be non-specific, and the response to water was negligible. This demonstrates an improvement over previous methanol gas sensors based on molecularly imprinted polymers. No response was observed with carbon nanotubes alone, and no selectivity was observed with non-imprinted equivalents of the nanoMIP sensor. The resulting device is by far the most practical MIP-based instrument for methanol gas sensing thus far described in the literature, being the only example capable of functioning at the necessary methanol vapour concentrations and at the required temperature and humidity. With the selectivity and sensitivity described and the simple design, the developed device provides a substantial advance in the field of molecularly imprinted gas sensors.

Dual-functional communication and sensing antenna system.

In this article, a dual functional antenna system for communication and sensing applications is designed on a single substrate having a common input port. The main motivation behind the proposed design is to build an antenna system, which can work as an antenna sensor and also can communicate over a fixed band not related to the resonance frequency shifts of the antenna sensor. The dual functionality of the system has been achieved by designing a frequency selective multipath filter (FSMF). The FSMF has an input port and two output ports. Both the communicating and sensing antennas are integrated on the output ports of the FSMF. The FSMF ensures minimum effect of the antennas on each other's performance. The performance of the antenna sensor is first shown by characterizing different standard substrates with [Formula: see text] to 6.15 and then demonstrated for ice and water detection. The communicating antenna is used for Wi-Fi (2.45 GHz) applications. The simulated and measured results of the dual-functional antenna systems are in good agreement. The proposed design is fabricated on a PCB with [Formula: see text] and [Formula: see text] with an overall size of [Formula: see text].

Template Imprinting Versus Porogen Imprinting of Small Molecules: A Review of Molecularly Imprinted Polymers in Gas Sensing.

The selective sensing of gaseous target molecules is a challenge to analytical chemistry. Selectivity may be achieved in liquids by several different methods, but many of these are not suitable for gas-phase analysis. In this review, we will focus on molecular imprinting and its application in selective binding of volatile organic compounds and atmospheric pollutants in the gas phase. The vast majority of indexed publications describing molecularly imprinted polymers for gas sensors and vapour monitors have been analysed and categorised. Specific attention was then given to sensitivity, selectivity, and the challenges of imprinting these small volatile compounds. A distinction was made between porogen (solvent) imprinting and template imprinting for the discussion of different synthetic techniques, and the suitability of each to different applications. We conclude that porogen imprinting, synthesis in an excess of template, has great potential in gas capture technology and possibly in tandem with more typical template imprinting, but that the latter generally remains preferable for selective and sensitive detection of gaseous molecules. More generally, it is concluded that gas-phase applications of MIPs are an established science, capable of great selectivity and parts-per-trillion sensitivity. Improvements in the fields are likely to emerge by deviating from standards developed for MIP in liquids, but original methodologies generating exceptional results are already present in the literature.

Acoustic- and Radio-Frequency-Based Human Activity Recognition.

In this work, a hybrid radio frequency (RF)- and acoustic-based activity recognition system was developed to demonstrate the advantage of combining two non-invasive sensors in Human Activity Recognition (HAR) systems and smart assisted living. We used a hybrid approach, employing RF and acoustic signals to recognize falling, walking, sitting on a chair, and standing up from a chair. To our knowledge, this is the first work that attempts to use a mixture of RF and passive acoustic signals for Human Activity Recognition purposes. We conducted experiments in the lab environment using a Vector Network Analyzer measuring the 2.4 GHz frequency band and a microphone array. After recording data, we extracted the Mel-spectrogram feature of the audio data and the Doppler shift feature of the RF measurements. We fed these features to six classification algorithms. Our result shows that using a hybrid acoustic- and radio-based method increases the accuracy of recognition compared to just using only one kind of sensory data and shows the possibility of expanding for a variety of other different activities that can be recognized. We demonstrate that by using a hybrid method, the recognition accuracy increases in all classification algorithms. Among these classifiers, five of them achieve over 98% recognition accuracy.

Communication Technologies for Smart Grid: A Comprehensive Survey.

With the ongoing trends in the energy sector such as vehicular electrification and renewable energy, the Smart Grid (SG) is clearly playing a more and more important role in the electric power system industry. One essential feature of the SG is the information flow over high-speed, reliable, and secure data communication networks in order to manage the complex power systems effectively and intelligently. SGs utilize bidirectional communication to function whereas traditional power grids mainly only use one-way communication. The communication requirements and suitable techniques differ depending on the specific environment and scenario. In this paper, we provide a comprehensive and up-to-date survey on the communication technologies used in the SG, including the communication requirements, physical layer technologies, network architectures, and research challenges. This survey aims to help the readers identify the potential research problems in the continued research on the topic of SG communications.

Wideband Channel Characterization for 6G Networks in Industrial Environments.

Wireless data traffic has increased significantly due to the rapid growth of smart terminals and evolving real-time technologies. With the dramatic growth of data traffic, the existing cellular networks including Fifth-Generation (5G) networks cannot fully meet the increasingly rising data rate requirements. The Sixth-Generation (6G) mobile network is expected to achieve the high data rate requirements of new transmission technologies and spectrum. This paper presents the radio channel measurements to study the channel characteristics of 6G networks in the 107-109 GHz band in three different industrial environments. The path loss, K-factor, and time dispersion parameters are investigated. Two popular path loss models for indoor environments, the close-in free space reference distance (CI) and floating intercept (FI), are used to examine the path loss. The mean excess delay (MED) and root mean squared delay spread (RMSDS) are used to investigate the time dispersion of the channel. The path loss results show that the CI and FI models fit the measured data well in all industrial settings with a path loss exponent (PLE) of 1.6-2. The results of the K-factor show that the high value in industrial environments at the sub-6 GHz band still holds well in our measured environments at a high frequency band above 100 GHz. For the time dispersion parameters, it is found that most of the received signal energy falls in the early delay bins. This work represents a first step to establish the feasibility of using 6G networks operating above 100 GHz for industrial applications.

Characterization of the Body-to-Body Propagation Channel for Subjects during Sports Activities.

Body-to-body wireless networks (BBWNs) have great potential to find applications in team sports activities among others. However, successful design of such systems requires great understanding of the communication channel as the movement of the body components causes time-varying shadowing and fading effects. In this study, we present results of the measurement campaign of BBWN during running and cycling activities. Among others, the results indicated the presence of good and bad states with each state following a specific distribution for the considered propagation scenarios. This motivated the development of two-state semi-Markov model, for simulation of the communication channels. The simulation model was validated using the available measurement data in terms of first and second order statistics and have shown good agreement. The first order statistics obtained from the simulation model as well as the measured results were then used to analyze the performance of the BBWNs channels under running and cycling activities in terms of capacity and outage probability. Cycling channels showed better performance than running, having higher channel capacity and lower outage probability, regardless of the speed of the subjects involved in the measurement campaign.

On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks.

Wireless sensor networks (WSNs) will play a fundamental role in the realization of Internet of Things and Industry 4.0. Arising from the presence of spatially distributed sensor nodes in a sensor network, cooperative diversity can be achieved by using the sensor nodes between a given source-destination pair as intermediate relay stations. In this paper, we investigate the end-to-end average bit error rate (BER) and the channel capacity of a multi-hop relay network in the presence of impulsive noise modeled by the well-known Middleton's class-A model. Specifically, we consider a multi-hop decode-and-forward (DF) relay network over Nakagami-m fading channel due to its generality, but also due to the absence of reported works in this area. Closed-form analytical expressions for the end-to-end average BER and the statistical properties of the end-to-end channel capacity are obtained. The impacts of the channel parameters on these performance quantities are evaluated and discussed.

Fall Detection Using Smartphone Audio Features.

An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.