Concentric ring electrodes are noninvasive and wearable sensors for electrophysiological measurement capable of estimating the surface Laplacian (second spatial derivative of surface potential) at each electrode. Recently, progress has been made toward optimization of inter-ring distances (distances between the recording surfaces of a concentric ring electrode), maximizing the accuracy of the surface Laplacian estimate based on the negligible dimensions model of the electrode. However, this progress was limited to tripolar (number of concentric rings n equal to 2) and quadripolar (n = 3) electrode configurations only. In this study, inter-ring distances optimization problem is solved for pentapolar (n = 4) and sextopolar (n = 5) concentric ring electrode configurations using a wide range of truncation error percentiles ranging from 1^st to 25^th. For example, for the 5^th percentile threshold, the optimal range of values of α, β and γ for the pentapolar concentric ring electrode configuration with middle rings radii equal to αr, βr and γr and outer ring radius of r such that 0 < α < β < γ < 1 was determined to be αβγ ≤ 0.213. Respective optimal range of values of α, β, γ and δ for the sextopolar concentric ring electrode configuration with an additional middle ring of radius δr such that 0 < α < β < γ < δ < 1 was determined to be αβγδ ≤ 0.204. Obtained results also suggest consistency between the optimal ranges for all the considered concentric ring electrode configurations corresponding to n ranging from 2 to 5 that may allow estimation of optimal ranges for electrode configurations with n ≥ 6. Therefore, this study may inform future concentric ring electrode design for n ≥ 4 which is important since the accuracy of surface Laplacian estimation has been previously shown to increase with an increase in n.

Research of high-efficiency ambient temperature photon sensors is ongoing due to the demand on measuring x-ray and gamma-ray radiation with high energy resolution. The inorganic wide-bandgap perovskites such as CsPbBr₃ have high mobility-lifetime product (10⁻² cm² V^-1), low defect densities, and long-term stability for photon and charge particles detection. These detector materials are promising for large crystal designs. The study of large-size perovskite detectors is necessary, including development of computational models. The performance of a gamma detector based on the CsPbBr3 crystal was studied using GEANT4 and ROOT toolkits. The Monte Carlo GEANT4 code utilizes geometry and materials data to model particle interactions with matter, event and track management, and visualization of results. The ROOT was used to process and analyze the gamma-ray energy distributions computed by GEANT4. The 8 cm³ CsPbBr₃ crystal characteristics for the incident 662-keV gamma rays were the following: 1.1% energy resolution and 29.2% photo peak efficiency. The energy resolution of the perovskite detector is comparable to that of a CZT detector of a similar geometry; however, the larger size perovskite detectors can be fabricated.

Automated damage detection in Carbon-Fibre and Fibre Metal Laminates is still a challenge. Impact damages are typically not visible from the outside. Different measuring and analysis methods are available to detect hidden damages, e.g., delaminations or cracks. Examples are X-ray computer tomography and methods based on guided ultrasonic waves (GUW). All measuring techniques are characterised by a high-dimensional sensor data, in the case of GUW that is a set of time-resolved signals as a response to a actuated stimulus. We present a simple but powerful two-level method that reduces the input data (time-resolved sensor signals) significantly by a signal feature selection computation finally applied to a damage predictor function. Beside multi-path sensing and analysis, the novelty of this work is a feed-forward ANN posing low complexity and that is used to implement the predictor function that combines a classifier and a spatial regression model.

Computer network communication is quickly growing in this pandemic situation. Phone Conferencing, Video Streaming, and Sharing File / Printing are all made easier with communications technologies. The internet is a vast network of interconnected computer networks that connect the world. In communication networks, the internet plays a critical role. Data access is a key element of any enterprise network. In an enterprise network, the routing protocol is used to transmit data or access data. Which chooses the optimum routes between any two nodes in an enterprise network. All transmitted data through the internet between the source and the destination need to be routed using one of the routing protocols. This research focuses on addressing the path/route of data packets to be sent will be governed by this routing protocol in the form of table routing. A routing table is used in the memory of a router that keeps the track of routes to a particular network destination and the most popular routing algorithms used to forward packets are Routing Information Protocol (RIP), Enhanced Interior Gateway Routing Protocol (EIGRP), and Open Shortest Path First (OSPF). In addition, this paper presents advances in routing technology that highlight the difference between RIP, EIGRP, OSPF in terms of metrics and other technical aspects.

In this work, we present a smart micro-opto-fluidic platform for the analytical detection of fluids exploiting the absorption spectroscopy technique in the near infrared wavelength region, from 1.15 μm to 1.65 μm. In the experimental configuration, the broadband radiation provided by a Tungsten lamp is fiber-coupled and shone onto a rectangular glass micro-capillary containing the sample. Top and bottom external sides of the micro-device are coated with thin Aluminum layers, deposited by sputtering, to create a zig-zag guiding effect: light crosses the capillary multiple times, thus increasing the optical path-length inside the sample fluid. Then, output light is directed towards an optical spectrum analyzer. For instance, this platform allows to detect pollution of alcohols by small quantities of water. As the work was performed during the Covid-19 pandemic, we took the opportunity to exploit it for specific quality control of hand sanitizer gels based on ethanol. The effect of water absorption around 1.45 μm is enhanced thanks to the long light path and the spectral shape of the absorption profile can be detected with a higher level of detail with respect to a single crossing of the channel, since the sensitivity increases. The sensing platform was calibrated by testing ethanol-water mixture and a responsivity parameter, defined as the ratio of the output power at two wavelengths, was retrieved. To validate the results, a sophisticated theoretical model, based on geometrical optical ray tracing approximation and Lambert-Beer law for solutions, was implemented in MATLAB environment: a good agreement between experimental results and theoretical predictions was found. The proposed optical readout technique is non-invasive, contactless and remote; moreover, the use of integrated reflectors is a simple and low-cost technology that makes our micro-opto-fluidic platform a smart device for specific sensing, suitable for investigation of ultra-low fluid volumes.

In the modern era Resource Allocation is one of the most challenging issues in LTE & 5G communication. In the field of wireless communication, the right method of assigning resources to subscribers has received a considerable deal of attention. So, in this research, we are introducing a method based to visualize resource allocation. It will help the users to allocate their resources in more correct and effective ways and will divide the subscriber’s situation into three parts (Good, Mid, Cell edge) by checking the RF condition. If the subscriber is in a cell edge situation, we won't count it as it will reduce computational complexity. Those users to whom we have allocated the resource if they are in a stable condition will save a lot of time and energy for us. We've used Adobe XD (UI/UX) software interface to design the layout of the resource allocation scenario. The layout shows our prototype on a live server.

In the area of affective computing, machine learning is used to recognize patterns in datasets based on extracted features. Feature selection methods are used to select the relevant features from the large number of extracted features. This paper presents a feature selection approach based on evolutionary algorithms using techniques inspired by natural evolution, such as inheritance, mutation, selection and crossover. Our proposed method consists of the steps Initialize, Evaluate, Mutate, Crossover and Select. First, an initial population consisting of a set of individuals is generated, in which every individual has a randomised set of features. Then, the fitness of every individual representing the accuracy of the prediction is evaluated to select the fittest individuals for the next steps Mutation and Crossover. Mutation sets used attributes to unused atrributes and vice versa, while with Crossover one part of one individual is crossed over with another part of the other individual. Finally, the performance of the new individuals is calculated and they are pitted against each other. Individuals with the higher performance have a higher chance to survive for the next round. The feature selection method with evolutionary algorithms is integrated within our previously developed workflow for affective computing and stress recognition from biosignals and is evaluated using the University of Ulm Multimodal Affective Corpus (uulmMAC) for Affective Computing in Human-Computer Interaction. Our proposed approach is much faster than the Forward Selection (FS) and Backward Elimination (BE) methods and does not stop at a local optimum, allowing a promising feature selection alternative in the field of affective computing.

The development of electrocardiogram (ECG) wearable devices has increase due to its applications on ambulatory patients. ECG signals provide useful information about the heart's behavior, but when daily activities are monitored, motion artifacts are introduced producing saturation of the signal, thus losing the information. The typical resolution used to record ECG signals is of maximum 16-bit, which might not be enough to detect low-amplitude potentials and at the same time avoid saturation due to baseline wander, since this last issue demands a low-gain signal chain. A 24-bit resolution provides a more detailed ECG signal under a low gain input, and if the signal is corrupted by motion artifact noise but is not saturated, it can be filtered to recover the signal of interest. On this work, a 24-bit ADC is used to record the ECG, and a new method, the rest ECG cycle template, is proposed to remove the baseline wander. This new method is compared to high-pass filter and spline interpolation methods in their ability to remove baseline wander. This new method presumes that a user is able to stablish a rest ECG during his/her daily activities.

Forest areas are profoundly important for the planet Earth due to the considerable advantages they offer. Therefore, it is essential that the forest areas are closely monitored, but unfortunately, in the past decades we have witnessed some forest fires that have led to missing some parts of forest areas. Mapping and estimation of the burned forest areas are critical to the next decision makings. In this case, remote sensing can be of great help. This paper presents a method to estimate burned areas on the Sentinel-2 imagery using CNN algorithm. This framework touches change detection using pre/post-fire datasets. The proposed CNN architecture has four convolution layers that are able to extract deep features. We have investigated the performance of the proposed method by visual and numerical analysis. The case study of this research is Golestan’s forest which is located in north of Iran. The result of the `burned area detection shows that the proposed method produces a performance which is more than 91.35% by Overall Accuracy.

Conductive polymers (CPs) have been the subject of intensive research and served as key material components in a number of applications, of which chemical sensors and biosensors represent a trendsetting example. CPs can be electropolymerized and deposited on electrodes to form thin films that grant a number of advantages: The film enhances the electron transfer between the working electrode and the redox mediator [1], it reduces electrode poisoning [2], and demonstrates great chemical and structural diversity for subsequent modification. Due to their strong absorption on gold, however, the removal of CP films for electrode regeneration presents a challenging task and is oftentimes highlighted as a significant drawback in the utilization of CPs [3].

In this communication, we want to equip the reader with a starter kit for electropolymerization of high-quality polythiophene films and efficient removal of such films for electrode regeneration. To enable film removal, the films can be swollen so that a sheer force is induced at the interface polymer/electrode, which flakes the films off the electrode. This way, we were able to remove polythiophene and polypyrrole films in a simple and fast, yet gentle approach, that allows to reuse the electrodes for up to fifteen times.

We lately discovered that the electropolymerization of thiophene can be catalysed by Lewis acids. Due to the milder synthesis conditions, films with significantly smoother surface topographies can be obtained, that may minimize sterical hindrance of subsequently immobilized bioreceptors and therefore improve target binding. We furthermore present an effortless protocol to dry the working solution, as residue water negatively affects electropolymerization of thiophene.

References

[1.] Wong, A., et al., Sensors and Actuators B: Chemical, 2018. 255: p. 2264-2273.

[2.] Zanardi, C., et al., Anal Bioanal Chem, 2013. 405(2-3): p. 509-31.

[3.] Bobade, R.S., et al., Journal of Polymer Engineering, 2011. 31(2-3).