Pilots Optimization and Surface Area Effects on Channel Estimation in RIS Aided MIMO System

Reconfigurable intelligent surface (RIS) is an emerging tool for 5G and wireless communication technologies that have attracted researchers' interest. However, the passive nature and the high number of reflecting elements in RIS result in a large pilot overhead, which makes channel estimation challenging in multi-user multiple-input multiple-output (MU-MIMO) wireless communication systems. Previous works have shown an improvement in reducing the pilot overhead by exploiting the structured sparsity in rows and columns, which was further improved by compensating offset among users in angular cascaded channels of RIS aided system. To further reduce the pilot overhead, we analyze and adopt coherence-optimized pilots for channel estimation and propose an algorithm to analyze the combined effect of low-coherence pilots with an optimum size of RIS elements for a given number of users, transmit antennas, and normalized error threshold performance. The simulation results illustrate better NMSE performance as compared to contemporary techniques

Stability and performance improvement of a submerged anaerobic membrane bioreactor (SAMBR) for wastewater treatment

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Characterization of dissolved compounds in submerged anaerobic membrane bioreactors (SAMBRs).

Two submerged anaerobic membrane bioreactors (SAMBRs) with essentially 100% cell recycle (150 days retention time, SRT), one with powdered activated carbon addition (PAC 1.7 gL−1) and one without, were continuously fed a low-strength feed (450mgCODL−1) in order to investigatemembrane fouling and to characterize the foulants. The SAMBR which did not receive PAC experienced more fouling, and the molecular weight (MW) distribution showed that there was a greater amount of high-MW compounds in this reactor when compared with the reactor with PAC. Size exclusion chromatography showed that although extracellular polymeric substances (EPS) seemed to contribute to the soluble chemical oxygen demand (COD) inside the reactor, it was mainly rejected by the membrane. High-MW protein and carbohydrate material originating mainly from cell lysis and EPS seemed to be the main organics that contributed to the internal fouling of the membrane

APPRAISAL OF HEAVY METALS IN DRINKING WATER SOURCES ALONG THE RIVER INDUS DISTRICT DERA GHAZI KHAN

  Safe drinking water is very important for the existence of life on earth. Access to safe drinking water is basic human right. Water quality is declining day by day due to anthropogenic activities, which are causing various health related issues all over the world. Certain health problems are associated with the occurrence of heavy metals and other contaminations in water. The main purpose of this research work to evaluate and investigate the underground drinking water Sources of various pollutants like physico-chemical parameters (pH, Turbidity, Electric Conductivity and Total Dissolved Solid) and Heavy metals (Iron, Zinc, Manganese, Arsenic and Copper) along the western bank of River Indus in Dera Ghazi Khan District (30.2748° N, 70.2408° E), Punjab, Pakistan. Water samples were collected from rural areas most common sources of drinking water of three regions Kot Chutta (K), Dera Ghazi Khan (D) and Taunsa (T) and analyzed physico-Chemical (pH, Turbidity, Electric Conductivity and Total Dissolved Solids), heavy metals (Iron, Zinc, Manganese, Arsenic and Copper) using standard methods. Results of water analysis of certain physicochemical parameters (pH, Turbidity, Electric Conductivity and Total Dissolved Solids). Turbidity values are exceeding at points Ranjha ala, Chah sobar wala, Chah inayat wala, Khoo mooli wala and Basti Bohar in three regions were higher than the world health organization (WHO) Standard value and Pakistan environmental quality standards for drinking water (PEQSDW). Total Dissolved solids (TDS) and Electrical conductivity (E.C) exceeded the standards value at Jakkar Imam Shah, Dauo Wala, Dawood kot, Chah kalar wala, Chah kabeer wala, and Mari gharbi than the WHO and PEQSDW. Furthermore, heavy metals analysis results shown higher concentration of arsenic (As) 37.1 % samples exceeded than WHO standard value and only 3.22 % samples exceeded from PEQSDW standards value, while 1.61% samples exceeded the WHO and PEQSDW standard value but Iron (Fe), Zinc (Zn) and Manganese (Mn) were within the standards limits. The heavy metals concentration in study area follows the order As>Cu>Fe>Zn>Mn. The groundwater drinking sources quality in these areas is poor and is not fit for drinking purposes, which is harsh condition for the human being of this area

Joint Placement and Device Association of UAV Base Stations in IoT Networks

Drone base stations (DBSs) have received significant research interest in recent years. They provide a flexible and cost-effective solution to improve the coverage, connectivity, quality of service (QoS), and energy efficiency of large-area Internet of Things (IoT) networks. However, as DBSs are costly and power-limited devices, they require an efficient scheme for their deployment in practical networks. This work proposes a realistic mathematical model for the joint optimization problem of DBS placement and IoT users’ assignment in a massive IoT network scenario. The optimization goal is to maximize the connectivity of IoT users by utilizing the minimum number of DBS, while satisfying practical network constraints. Such an optimization problem is NP-hard, and the optimal solution has a complexity exponential to the number of DBSs and IoT users in the network. Furthermore, this work also proposes a linearization scheme and a low-complexity heuristic to solve the problem in polynomial time. The simulations are performed for a number of network scenarios, and demonstrate that the proposed heuristic is numerically accurate and performs close to the optimal solution

Automatic Scene Recognition through Acoustic Classification for Behavioral Robotics

Classification of complex acoustic scenes under real time scenarios is an active domain which has engaged several researchers lately form the machine learning community. A variety of techniques have been proposed for acoustic patterns or scene classification including natural soundscapes such as rain/thunder, and urban soundscapes such as restaurants/streets, etc. In this work, we present a framework for automatic acoustic classification for behavioral robotics. Motivated by several texture classification algorithms used in computer vision, a modified feature descriptor for sound is proposed which incorporates a combination of 1-D local ternary patterns (1D-LTP) and baseline method Mel-frequency cepstral coefficients (MFCC). The extracted feature vector is later classified using a multi-class support vector machine (SVM), which is selected as a base classifier. The proposed method is validated on two standard benchmark datasets i.e., DCASE and RWCP and achieves accuracies of 97.38 % and 94.10 % , respectively. A comparative analysis demonstrates that the proposed scheme performs exceptionally well compared to other feature descriptors

A novel framework for approximating resistance–temperature characteristics of a superconducting film based on artificial neural networks

Funding Information: The authors are grateful to the Deanship of Scientific Research at King Saud University , Saudi Arabia for funding this work through the Vice Deanship of Scientific Research Chairs: Chair of Pervasive and Mobile Computing. Publisher Copyright: © 2021 The AuthorsPeer reviewedPublisher PD