LSBR Speech Steganalysis Based on Percent of Equal Adjacent Samples

Wide utilization of audio files has attracted the attention of cyber-criminals to employ this media as a cover for their concealed communications. As a countermeasure and to protect cyberspace, several techniques have been introduced for steganalysis of various audio formats, such as MP3, VoIP, etc. The combination of machine learning and signal processing techniques has helped steganalyzers to obtain higher accuracies. However, as the statistical characteristics of a normal audio file differ from the speech ones, the current methods cannot discriminate clean and stego speech instances efficiently. Another problem is the high numbers of extracted features and analysis dimensions that drastically increase the implementation cost. To tackle these, this paper proposes the Percent of Equal Adjacent Samples (PEAS) feature for single-dimension least-significant-bit replacement (LSBR) speech steganalysis. The model first classifies the samples into speech and silence groups according to a threshold which has been determined through extensive experiments. It then uses an MLP classifier to detect stego instances and determine the embedding ratio. PEAS steganalysis detects 99.8% of stego instances in the lowest analyzed embedding ratio — 12.5% — and its sensitivity increases to 100% for the ratios of 37.5% and above.

ENHANCING COASTAL RESILIENCY OF HYPOTHETICAL LAND RECLAMATION SCENARIOS WITH MANGROVE FOREST AND OYSTER REEF ASSESSED BY ADCIRC AND SWAN STORM SURGE MODEL

This study aims to assess the effectiveness of mangrove forests and oyster reefs on reducing the damages from typhoons in hypothetical land reclamation scenarios in Atimonan, Philippines. Storm surge simulations were ran using ADCIRC and SWAN coupled model on the local government unit’s (LGU) land reclamation plan and the proposed crenulate bay reclamation plan, both with concrete seawall, mangrove forests and oyster reefs. Inputs to the model include modified topography and bathymetry, coastline, land cover, typhoon Durian data and tidal potential constituents. Simulations show that the crenulate bay reclamation plan is better by 39.15% than the LGU’s land reclamation plan on reducing typhoon winds and storm surge inundation extent induced by Typhoon Durian. However, this advantage comes with an additional implementation cost of 11.02%. This study is envisioned to help the land reclamation project of Atimonan LGU to be resilient against typhoon winds and coastal inundation.

A Method of Optimizing a Set of Programs for Mitigating Threats Related to the Undertaking of a Contract for the Execution of Construction Works

This article deals with the problem of limiting the risk of taking up a construction contract for the execution of construction works. The authors have developed an advisory system that will support the analysis of threats on the basis of existing experiences for a specific activity without having to construct an individualized organizational model of an investment. In order to identify a relatively complete set of threats that occur in investment and construction processes in road construction investments, as well as to identify possible programs of their reduction, a model and a method of optimizing programs for reducing risks related to contracts was developed. Threats are considered to be possible events that take place during the preparation, implementation and settlement of any contract. The programs concern specific actions that can be taken in relation to specific threats. Every program contains a set of threats that will be limited as a result of its execution and also has a specific implementation cost. The aim of the proposed optimization is to determine, with regard to costs, a combination of risk reduction programs that is appropriate for the risk states that are accepted by a decision maker. The problem is solved using graph theory and a minimum cover determination algorithm with the use of the minimum alternative formula (mfa) of the Boolean function. A method of actively responding to identified threats during the implementation of a construction contract should take the form of an advisory system that will provide an answer as to what risks should be taken into account when undertaking a contract, as well as what actions can be taken to reduce these risks.

Non-Intrusive Load Monitoring Based on Unsupervised Optimization Enhanced Neural Network Deep Learning

Non-intrusive load monitoring has broad application prospects because of its low implementation cost and little interference to energy users, which has been highly expected in the industrial field recently due to the development of learning algorithms. Targeting at the investigation of practical and reliable load monitoring in field implementations, a non-intrusive load disaggregation approach based on an enhanced neural network learning algorithm is proposed in this article. The presented appliance monitoring approach establishes the neural network model following the supervised learning strategy at first and then utilizes the unsupervised learning based optimization to enhance the flexibility and adaptability for diverse scenarios, leading to the improvement of disaggregation performance. By verifications on the REDD public dataset, the proposed approach is demonstrated to be with good performance in non-intrusive load monitoring. In addition to the accuracy enhancement, the proposed approach is also with good scalability, which is efficient in recognizing the newly added appliance.

The Value and Cost of Crop Minimum Support Price: Farmer and Consumer Welfare and Implementation Cost

In many developing countries, crop minimum support price (MSP) is a subsidy scheme to (i) improve farmer welfare by safeguarding farmers’ incomes against vagaries in crop price and (ii) improve consumer surplus by ensuring sufficient crop production. Among different mechanisms to operationalize an MSP scheme, we focus on credit-based MSPs under which the government credits farmers should the prevailing market price be below the prespecified MSP. By accounting for the implementation cost of the MSP, we examine the effectiveness of the MSP in terms of net benefit (i.e., farmer’s surplus minus the implementation cost) and net social value (i.e., sum of farmer’s and consumer’s surpluses minus the implementation cost) in a market that consists of risk-averse farmers with heterogeneous production costs. Also, farmers face two types of uncertainties: (1) market and (2) production yield uncertainty. We find that a credit-based MSP can induce crop production, which is intuitive. However, we find some more interesting results: (i) offering a higher MSP may not improve farmer’s surplus, (ii) the net benefit of an MSP can be negative—the cost of offering an MSP can exceed the farmer’s surplus, and (iii) there exists an MSP that maximizes the net social value. We extend our single-crop model to the case of two crops to capture the intercrop MSP interaction. We show that when one crop is more rewarding but riskier than the other crop, then it is sufficient to offer an appropriate MSP for one of the two crops while offering no MSP to the other crop. This paper was accepted by Vishal Gaur, operations management.

A Survey of Wireless Battery Management System: Topology, Emerging Trends, and Challenges

An effective battery management system (BMS) is indispensable for any lithium-ion battery (LIB) powered systems such as electric vehicles (EVs) and stationary grid-tied energy storage systems. Massive wire harness, scalability issue, physical failure of wiring, and high implementation cost and weight are some of the major issues in conventional wired-BMS. One of the promising solutions researchers have come up with is the wireless BMS (WBMS) architecture. Despite research and development on WBMS getting momentum more than a decade ago, it is still in a preliminary stage. Significant further upgradation is required towards developing an industry-ready WBMS, especially for high-power LIB packs. Therefore, an in-depth survey exclusively on WBMS architectures is presented in this article. The aim is to provide a summary of the existing developments as well as to present an informative guide to the research community for future developments by highlighting the issues, emerging trends, and challenges. In-depth analysis of the existing WBMS topologies will not only help the researchers to understand the existing challenges and future research scopes clearly but at the same time enthuse them to focus their research inclination in the domain of WBMS.

A Survey of Wireless Battery Management System: Topology, Emerging Trends, and Challenges

An effective battery management system (BMS) is indispensable for any lithium-ion battery (LIB) powered systems such as electric vehicles (EVs) and stationary grid-tied energy storage systems. Massive wire harness, scalability issue, physical failure of wiring, and high implementation cost and weight are some of the major issues in conventional wired-BMS. One of the promising solutions researchers have come up with is the wireless BMS (WBMS) architecture. Despite research and development on WBMS got momentum more than a decade ago, it is still in a preliminary stage. Significant further upgradation is still required towards developing an industry-ready WBMS, especially for high-power LIB packs. Therefore, an in-depth survey exclusively on WBMS architectures is presented in this article. The aim is to provide a summary of the existing developments as well as to present an informative guide to the research community for future development by highlighting the issues, emerging trends, and challenges. In-depth analysis of the existing WBMS topologies will not only help the researchers to understand the existing challenges and future research scopes clearly while at the same time enthuse them to focus their research inclination in the domain of WBMS.

Nonintrusive Monitoring for Electric Vehicles Based on Zero-Shot Learning

Monitoring the charging behavior of electric vehicle clusters will contribute to developing more effective energy management strategies for grid operators. A low implementation cost leads to a wide application prospect in nonintrusive monitoring for EVs. Aiming at the problem that traditional nonintrusive monitoring methods cannot identify unknown devices accurately due to the lack of classes, a nonintrusive monitoring method based on zero-shot learning (ZSL) is proposed in this article, one which can monitor the unknown types of EVs connected to charging piles. First, the charging characteristics of known EVs and unknown EVs are extracted by dictionary learning. Then EVs are classified by ZSL based on sparse coding. Furthermore, EVs are decomposed based on the proposed multimode factorial hidden Markov model (FHMM). Finally, the EV dataset of Pecan Street is used to verify the effectiveness and accuracy of the proposed method.

Advantages and Disadvantages of Different Coupling Methods of Plasma Antennas

One of the important challenges in plasma antennas, is the coupling of RF signal to the plasma column. RF signal coupling has a significant effect on antenna efficiency, antenna implementation cost, structure implementation complexity, antenna pattern shape, and final structure weight and volume. In this article, firstly the various methods of coupling were introduced. Then capacitive coupling, direct coupling and sleeve coupling were presented and their advantages and disadvantages were mentioned. As a sample, a plasma folded monopole antenna with sleeve coupling was fabricated and measured. By comparison of the different coupling methods and as a result, one can conclude that the sleeve coupling method is the most suitable method. This method has the least sensitivity to change the dimensions. It is also easy and cheap to implement. In this type of coupling, the efficiency of the Nesta antenna is suitable and the coupling structure adds small weight and volume to the antenna structure.