Performance evaluation of wireless local area network with congested fading channels

The IEEE 802.11ay wireless communication standard consents gadgets to link in the spectrum of millimeter wave (mm-Wave) 60 Giga Hertz band through 100 Gbps bandwidth. The development of promising high bandwidth in communication networks is a must as QoS, throughput and error rates of bandwidth-intensive applications like merged reality (MR), artificial intelligence (AI) related apps or wireless communication boggling exceed the extent of the chronic 802.11 standard established in 2012. Thus, the IEEE 802.11ay task group committee has newly amended recent physical (PHY) and medium access control (MAC) blueprints to guarantee a technical achievement especially in link delay on multipath fading channels (MPFC). However, due to the congestion of super bandwidth intensive apps such as IoT and big data, we propose to diversify a propagation delay to practical extension. This article then focuses on a real-world situation and how the IEEE 802.11ay design is affected by the performance of mm-Wave propagation. In specific, we randomize the unstable MPFC link capacity by taking the divergence of congested network parameters into account. The efficiency of congested MPFC-based wireless network is simulated and confirmed by advancements described in the standard.

A Survey on Crime Detection And Prediction Techniques

Crime is a preeminent issue where the main concern has been worried by individual, the local area and government. Wrongdoing forecast utilizes past information and in the wake of investigating information, anticipate the future wrongdoing with area and time. In present days sequential criminal cases quickly happen so it is a provoking assignment to anticipate future wrongdoing precisely with better execution. This paper examines about various wrongdoing expectation and location. A productive wrongdoing forecast framework speeds up the method involved with addressing violations.. Wrongdoing Prediction framework utilizes recorded information and examinations the information utilizing a few dissecting strategies and later can anticipate the examples and patterns of wrongdoing utilizing any of the underneath referenced methodologies. Keywords: Crime Analysis, Data Mining, Classifiaction , Clustering

Investigation on Performance of Microstrip Patch Antenna for a Practical Wireless Local Area Network (WLAN) Application

The performance of a microstrip patch antenna for a practical wireless local area network application is investigated in this research. This design is built around the transmission line concept. The antenna design substrate is FR4 (lossy) with a dielectric constant (Er) of 4.3 dielectric material, and the ground and patch materials are copper (annealed). The substrate is 71.62mm in width and 55.47mm in length. The height of the dielectric material is 1.6mm, which is the normal size for FR4 material. The conducting patch element has a width of 35.81mm and a length of 27.73mm for a resonance frequency of 2.573 GHz. A simulation with CST studio suite was used to optimise the antenna design. Keywords: Microstrio patch antenna, CST suite, WLAN application, Transmission line, Antenna design

Implementation of a Miniaturized Planar Tri-Band Microstrip Patch Antenna for Wireless Sensors in Mobile Applications

Antennas in wireless sensor networks (WSNs) are characterized by the enhanced capacity of the network, longer range of transmission, better spatial reuse, and lower interference. In this paper, we propose a planar patch antenna for mobile communication applications operating at 1.8, 3.5, and 5.4 GHz. A planar microstrip patch antenna (MPA) consists of two F-shaped resonators that enable operations at 1.8 and 3.5 GHz while operation at 5.4 GHz is achieved when the patch is truncated from the middle. The proposed planar patch is printed on a low-cost FR-4 substrate that is 1.6 mm in thickness. The equivalent circuit model is also designed to validate the reflection coefficient of the proposed antenna with the S11 obtained from the circuit model. It contains three RLC (resistor–inductor–capacitor) circuits for generating three frequency bands for the proposed antenna. Thereby, we obtained a good agreement between simulation and measurement results. The proposed antenna has an elliptically shaped radiation pattern at 1.8 and 3.5 GHz, while the broadside directional pattern is obtained at the 5.4 GHz frequency band. At 1.8, 3.5, and 5.4 GHz, the simulated peak realized gains of 2.34, 5.2, and 1.42 dB are obtained and compared to the experimental peak realized gains of 2.22, 5.18, and 1.38 dB at same frequencies. The results indicate that the proposed planar patch antenna can be utilized for mobile applications such as digital communication systems (DCS), worldwide interoperability for microwave access (WiMAX), and wireless local area networks (WLAN).

Wireless Local Area Network Technologies as Communication Solutions for Unmanned Surface Vehicles

As the number of research activities and practical deployments of unmanned vehicles has shown a rapid growth, topics related to their communication with operator and external infrastructure became of high importance. As a result a trend of employing IP communication for this purpose is emerging and can be expected to bring significant advantages. However, its employment can be expected to be most effective using broadband communication technologies such as Wireless Local Area Networks (WLANs). To verify the effectiveness of such an approach in a specific case of surface unmanned vehicles, the paper includes an overview of IP-based MAVLink communication advantages and requirements, followed by a laboratory and field-experiment study of selected WLAN technologies, compared to popular narrowband communication solutions. The conclusions confirm the general applicability of IP/WLAN communication for surface unmanned vehicles, providing an overview of their advantages and pointing out deployment requirements.

Field Test of In Situ Groundwater Treatment Applying Oxygen Diffusion and Bioaugmentation Methods in an Area with Sustained Total Petroleum Hydrocarbon (TPH) Contaminant Flow

Contamination of groundwater by petroleum hydrocarbons is a widespread environmental problem in many regions. Contamination of unsaturated and saturated zones could also pose a significant risk to human health. The main purpose of the study was to assess the efficiency of biodegradation of total petroleum hydrocarbon (TPH) in situ, in an area with loam and sandy loam soils, and to identify features and characteristics related to groundwater treatment in an area with a persistent flow of pollutants. We used methods of biostimulation (oxygen as stimulatory supplement) and bioaugmentation to improve water quality. Oxygen was added to the groundwater by diffusion through silicone tubing. The efficiency of groundwater treatment was determined by detailed monitoring. Implementation of the applied measure resulted in an average reduction in TPH concentration of 73.1% compared with the initial average concentration (4.33 mg/L), and in the local area, TPH content was reduced by 95.5%. The authors hope that this paper will contribute to a better understanding of the topic of groundwater treatment by in situ biodegradation of TPH. Further studies on this topic are particularly needed to provide more data and details on the efficiency of groundwater treatment under adverse geological conditions.

Wind Turbines, Public Acceptance, and Electoral Outcomes

Despite a widespread public support for wind energy in general, wind turbine proposals attract a considerable amount of public opposition. At a time of political commitments to building more wind turbines for climate risk mitigation, we study the potential causes of this opposition and their electoral effects. Our analysis draws on a survey experiment in Switzerland, where the number of wind turbines will grow from a couple of dozens to many hundreds in the next three decades. We find that exposure to wind turbines increases public acceptance, but this affect does not translate into electoral turnout or vote choice. Moreover, locality or politicisation does not seem to have an effect at all—neither on acceptance nor on electoral outcomes. Our results suggest that voters do not reward or punish political parties for their positions on wind energy, even when turbines might soon be rising in their local area.

eSCIFI: An Energy Saving Mechanism for WLANs Based on Machine Learning

As wireless local area networks grow in size to provide access to users, power consumption becomes an important issue. Power savings in a large-scale Wi-Fi network, with low impact to user service, is undoubtedly desired. In this work, we propose and evaluate the eSCIFI energy saving mechanism for Wireless Local Area Networks (WLANs). eSCIFI is an energy saving mechanism that uses machine learning algorithms as occupancy demand estimators. The eSCIFI mechanism is designed to cope with a broader range of WLANs, which includes Wi-Fi networks such as the Fluminense Federal University (UFF) SCIFI network. The eSCIFI can cope with WLANs that cannot acquire data in a real time manner and/or possess a limited CPU power. The eSCIFI design also includes two clustering algorithms, named cSCIFI and cSCIFI+, that help to guarantee the network’s coverage. eSCIFI uses those network clusters and machine learning predictions as input features to an energy state decision algorithm that then decides which Access Points (AP) can be switched off during the day. To evaluate eSCIFI performance, we conducted several trace-driven simulations comparing the eSCIFI mechanism using both clustering algorithms with other energy saving mechanisms found in the literature using the UFF SCIFI network traces. The results showed that eSCIFI mechanism using the cSCIFI+ clustering algorithm achieves the best performance and that it can save up to 64.32% of the UFF SCIFI network energy without affecting the user coverage.

Electronic Corpus of Linguaculture of the Northern Angara Region: Foundations, Structure, and Application

Linguaculture is a heterogeneous linguacognitive phenomenon that depends on the local area, values, and community, as well as on national and ethno-specifics cultural codes and subcodes of mythology, theology, anthropomorphism, biomorphism, household, commerce, etc. The article introduces the linguacultural corpus of the Northern Angara Region that unites oral and multimedia (polycode) texts of traditional regional linguaculture. The corpus is being developed at the Siberian Federal University and includes empirical material of complex folklore, dialectological, and ethnographic expeditions to Kezhemskiy, Boguchany and Motygino areas. The article describes the basic principles of the corpus, i.e. representativeness, synchronism, and regionality, as well as the thematic, genre, functional, and semantic diversity of oral and polycode texts. Culturally marked oral texts are included in the corpus based on their linguistic and cultural informativeness. The corpus is divided into dialect, folklore, and multimedia subcorpuses of valuable information, which can solve various issues of Russian studies, as well as practical issues of linguacultural ecology. The information search process depends on several parameters: speech and folklore genre, topic, basic concept, and word. The article also contains a comparative analysis with other specialized corpora of oral speech, which revealed prospects for further development and application. The linguacultural corpus of the Northern Angara Region contributes to regional lexicography and other branches of Russian studies, e.g. Siberian linguaculture, language and culture, language and mentality, etc.

Artificial Intelligence Segmentation Model-Based Computed Tomography Angiography Image in the Diagnosis of Congenital Aortic Constriction

This study was to analyze the impacts of the image segmentation model and computed tomography angiography (CTA) on the clinical diagnosis of aortic constriction under the background of artificial intelligence. In this study, 126 patients with congenital aortic constriction (CAC) diagnosed by surgery were selected as the research objects and routine digital subtraction angiography (DSA) and CTA were performed. Then, the traditional active contour model (AC model) was optimized based on the local area information to construct a new image segmentation model for intelligent segmentation and reconstruction of the CTA images of patients. The results revealed that compared with the AC model and the image segmentation model based on region growth (RG model) obtained from angiography segmentation, the algorithm constructed in this study showed a smaller segmentation range for angiography images and more accurate segmentation results. The quantitative data results suggested that the evolution times and running time of the constructed model were less than those of the AC and RG models P < 0.05 . Based on the gold standard of DSA examination results, there were 122 correctly diagnosed cases, 3 missed diagnosed cases, and 1 misdiagnosed by CTA, so the diagnosis coincidence rate was 96.83%. Compared with DSA, the average inner diameter and average pressure difference of patients with precatheter, paracatheter, and postcatheter type were not greatly different in CTA P > 0.05 . The CTA examination suggested there were 154 cases with intracardiac structural abnormalities, with a detection rate of 86.52%; there were 32 cases of cardiac-vascular connection abnormalities, with a detection rate of 100%; and there were 79 extracardiac vascular abnormalities, with the detection rate of 95.18%. It indicated that the optimized image segmentation model based on local area information proposed in this paper has excellent segmentation performance for CT angiography images and has good segmentation effect and efficiency. The CTA based on the artificial intelligence image segmentation model showed a better diagnostic effect on abnormal heart-vascular connection and abnormal extracardiac blood vessels and can be used as an effective examination method for clinical diagnosis of CAC.