Study of absorption loss effects on acoustic wave propagation in shallow water using different empirical models

Efficient underwater acoustic communication and target locating systems require detailed study of acoustic wave propagation in the sea. Many investigators have studied the absorption of acoustic waves in ocean water and formulated empirical equations such as Thorp's formula, Schulkin and Marsh model and Fisher and Simmons formula. The Fisher and Simmons formula found the effect associated with the relaxation of boric acid on absorption and provided a more detailed form of absorption coefficient which varies with frequency. However, no simulation model has made for the underwater acoustic propagation using these models. This paper reports the comparative study of acoustic wave absorption carried out by means of modeling in MATLAB. The results of simulation have been evaluated using measured data collected at Desaru beach on the eastern shore of Johor in Malaysia. The model has been used to determine sound absorption for given values of depth (D), salinity (S), temperature (T), pH, and acoustic wave transmitter frequency (f). From the results a suitable range, depth and frequency can be found to obtain best propagation link with low absorption loss

Preliminary Evaluation of a Rooftop Grid-Connected Photovoltaic System Installation under the Climatic Conditions of Texas (USA)

Solar photovoltaic (PV) systems have demonstrated growing competitiveness as a viable alternative to fossil fuel-based power plants to mitigate the negative impact of fossil energy sources on the environment. Notwithstanding, solar PV technology has not made yet a meaningful contribution in most countries globally. This study aims to encourage the adoption of solar PV systems on rooftop buildings in countries which have a good solar energy potential, and even if they are oil or gas producers, based on the obtained results of a proposed PV system. The performance of a rooftop grid-tied 3360 kWp PV system was analyzed by considering technical, economic, and environmental criteria, solar irradiance intensity, two modes of single-axis tracking, shadow effect, PV cell temperature impact on system efficiency, and Texas A&M University as a case study. The evaluated parameters of the proposed system include energy output, array yield, final yield, array and system losses, capacity factor, performance ratio, return on investment, payback period, Levelized cost of energy, and carbon emission. According to the overall performance results of the proposed PV system, it is found to be a technically, economically, and environmentally feasible solution for electricity generation and would play a significant role in the future energy mix of Texas

Bingo: A Semi-Centralized Password Storage System

A lack of security best practices in modern password storage has led to a dramatic rise in the number of online data breaches, resulting in financial damages and lowered trust in online service providers. This work aims to explore the question of how leveraging decentralized storage paired with a centralized point of authentication may combat such attacks. A solution, “Bingo”, is presented, which implements browser side clients which store password shares for a centralized proxy server. Bingo is a fully formed system which allows for modern browsers to store and retrieve a dynamic number of anonymized password shares, which are used when authenticating users. Thus, Bingo is the first solution to prove that distributed password storage functions in the context of the modern web. Furthermore, Bingo is evaluated in both simulation and cloud in order to show that it achieves high rates of system liveness despite its dependence on its users being active at given intervals. In addition, a novel simulator is presented which allows future researchers to mock scheduled behavior of online users. This work concludes that with the rise in online activity, decentralization may play a role in increasing data security

Bingo: A Semi-Centralized Password Storage System

A lack of security best practices in modern password storage has led to a dramatic rise in the number of online data breaches, resulting in financial damages and lowered trust in online service providers. This work aims to explore the question of how leveraging decentralized storage paired with a centralized point of authentication may combat such attacks. A solution, “Bingo”, is presented, which implements browser side clients which store password shares for a centralized proxy server. Bingo is a fully formed system which allows for modern browsers to store and retrieve a dynamic number of anonymized password shares, which are used when authenticating users. Thus, Bingo is the first solution to prove that distributed password storage functions in the context of the modern web. Furthermore, Bingo is evaluated in both simulation and cloud in order to show that it achieves high rates of system liveness despite its dependence on its users being active at given intervals. In addition, a novel simulator is presented which allows future researchers to mock scheduled behavior of online users. This work concludes that with the rise in online activity, decentralization may play a role in increasing data security

Experimental multipath delay profile of underwater acoustic communication channel in shallow water

The shallow water channel is an environment that is of particular interest to many research fields. An underwater acoustic channel is characterized as a multipath channel. Time-varying multipath propagation is one of the major factors that limit the acoustic communication performance in shallow water. This study conducts two underwater acoustic experiments in Tanjung Balau, Johor, Malaysia. A transducer and a hydrophone are submerged at different depths and separated by different distances. Linear frequency modulated (LFM) pulses are chosen as the main transmit signal for the experiments. The crosscorrelation between the transmitted and received signals represents the impulse response of the channel (multipath profile). The results show that the amplitude of the successive paths will not rapidly decline, and vice versa, when the distance between the sender and the receiver increases. Moreover, the time difference between the different paths will be small in the case of distance increase. In other words, the successive paths will converge in time

Improved underwater signal detection using efficient time–frequency de-noising technique and Pre-whitening filter

Optimal signal detection is important in sonar and underwater digital communication. However, a detailed knowledge of the statistics of the noise present is required to achieve optimal signal detection. Additive white Gaussian noise (AWGN) is assumed in many applications; thus, a linear correlator (LC), which is known to be optimal in the presence of AWGN, is normally used. However, underwater acoustic noise (UWAN) influences the reliability of signal detection in applications, in which the noise is non-white and non-Gaussian. As a result, an LC detector performs poorly in underwater applications. Accordingly, the Gaussian noise injection detector (GNID) is proposed in this study to improve detection probability (PD) based on a noise-enhanced signal detection using a pre-whitening filter, a time–frequency de-noising method based on S-transform, and an inverse whitening filter. Sea-truth data are collected at Desaru Beach on the eastern shore of Johor in Malaysia using broadband hydrophones. These data are used as UWAN to validate the proposed method. The performances of four different detectors, namely, the proposed GNID, a locally optimal (LO) detector, a sign correlation (SC) detector, and a conventional LC detector, are evaluated according to their PD values. Given a false alarm probability of 0.01 and PD value of 90%, the energy-to-noise ratios of the GNID are better than the LO, SC, and LC detectors by 1.77, 3.81, and 3.61 dB, respectively, for a time-varying signal

An Integrated Approach to Water-Energy Nexus in Shale-Gas Production

Shale gas production is associated with significant usage of fresh water and discharge of wastewater. Consequently, there is a necessity to create proper management strategies for water resources in shale gas production and to integrate conventional energy sources (e.g., shale gas) with renewables (e.g., solar energy). The objective of this study is to develop a design framework for integrating water and energy systems including multiple energy sources, the cogeneration process and desalination technologies in treating wastewater and providing fresh water for shale gas production. Solar energy is included to provide thermal power directly to a multi-effect distillation plant (MED) exclusively (to be more feasible economically) or indirect supply through a thermal energy storage system. Thus, MED is driven by direct or indirect solar energy and excess or direct cogeneration process heat. The proposed thermal energy storage along with the fossil fuel boiler will allow for the dual-purpose system to operate at steady-state by managing the dynamic variability of solar energy. Additionally, electric production is considered to supply a reverse osmosis plant (RO) without connecting to the local electric grid. A multi-period mixed integer nonlinear program (MINLP) is developed and applied to discretize the operation period to track the diurnal fluctuations of solar energy. The solution of the optimization program determines the optimal mix of solar energy, thermal storage and fossil fuel to attain the maximum annual profit of the entire system. A case study is solved for water treatment and energy management for Eagle Ford Basin in Texas

A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty

The production of shale gas and oil is associated with the generation of substantial amounts of wastewater. With the growing emphasis on sustainable development, the energy sector has been intensifying efforts to manage water resources while diversifying the energy portfolio used in treating wastewater to include fossil and renewable energy. The nexus of water and energy introduces complexity in the optimization of the water management systems. Furthermore, the uncertainty in the data for energy (e.g., solar intensity) and cost (e.g., price fluctuation) introduce additional complexities. The objective of this work is to develop a novel framework for the optimizing wastewater treatment and water-management systems in shale gas production while incorporating fossil and solar energy and accounting for uncertainties. Solar energy is utilized via collection, recovery, storage, and dispatch of heat. Heat integration with an adjacent industrial facility is considered. Additionally, electric power production is intended to supply a reverse osmosis (RO) plant and the local electric grid. The optimization problem is formulated as a multi-scenario mixed integer non-linear programming (MINLP) problem that is a deterministic equivalent of a two-stage stochastic programming model for handling uncertainty in operational conditions through a finite set of scenarios. The results show the capability of the system to address water-energy nexus problems in shale gas production based on the system’s economic and environmental merits. A case study for Eagle Ford Basin in Texas is solved by enabling effective water treatment and energy management strategies to attain the maximum annual profit of the entire system while achieving minimum environmental impact

Comparison of methodologies for signal detection in underwater acoustic noise in shallow tropical waters

Signal detection is important in sonar and underwater digital communication. Optimum signal detection in underwater acoustic noise (UWAN) can be achieved with the knowledge of noise statistics. The assumption of Gaussian noise allows the use of linear correlation (LC) detectors. However, the non-Gaussian nature of UWAN results in the poor performance of such detectors. This study presents an empirical model of the characteristics of UWAN in the shallow waters of an acoustic underwater channel. Four detectors for the detection of known signals in UWAN are compared: locally optimal (LO) detector, sign correlation (SC) detector, LC detector, and the proposed Gaussian noise injection detector (GNID). The performance of the detectors is evaluated according to the detection probability (PD) and receiver operating characteristic curves. Given a probability of false alarm (PFA) rate of 0.01 and PD of 90 percent, the energy-to-noise ratios of the LO detector, SC detector, GIND, and LC detector are 8.854, 10.8, 10, and 12 dB, respectively. Among the four detectors, the LO detector achieves the best performance, whereas the LC detector shows the weakest performance in the presence of non-Gaussian noise

On Wide-Area IoT Networks, Lightweight Security and Their Applications—A Practical Review

The Internet of Things (IoT) allows users to collect sensor data, control devices, and analyze collected data over the Internet. IoT devices are located in diverse environments and support many applications. To protect IoT systems from cyber threats, Confidentiality, Integrity, and Authentication—the CIA triad—must be supported. However, IoT devices have limited energy and computational resources. Lightweight encryption algorithms have been proposed for IoT, and have been reviewed by previous studies. Some cover communication protocols, while others cover lightweight security or review the challenges in implementing a secure IoT system. The aim of this literature review is to combine the first two topics: communication protocols and lightweight security. They will be approached from a practitioner’s standpoint. Several applications are provided that help readers with a minor background in security to understand these technologies and which elements of the CIA triad have more priority. This paper describes wide-area IoT networks, such as LoRAWAN, Sigfox, and NB-IoT, and their security. It also describes applications throughout the world, and how to enhance their security by implementing emerging lightweight security—specifically, approaches to make well-known ciphers such as Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) more lightweight