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Joint Beamforming Optimization for the Intelligent Reflecting Surface Assisted Wireless Surveillance System
Issue:
Volume 6, Issue 1, January 2021
Pages:
1-7
Received:
27 December 2020
Accepted:
7 January 2021
Published:
18 January 2021
Abstract: The intelligent reflecting surface (IRS), which consists of a large number of reflecting units, can adjust the phase shifts of its reflecting units to strengthen the desired signal and/or suppress the undesired signal. In this paper, we consider an IRS-assisted wireless surveillance system where an IRS is deployed to assist the legal surveillance receiver E to monitor the information transmission of the suspicious link from AP to the suspicious receiver B. Two communication scenarios assuming whether the suspicious link is aware of the existence of the monitor are considered. The optimization problem under the constraint that the achievable rate at the monitor E is larger than that at the suspicious receiver B is proposed to jointly optimize the beamforming vector at the AP and the phase shift matrix at the IRS to maximize the achievable eavesdropping rate. To solve this non-convex problem, we introduce the semi-definite relaxation (SDR) approach and the alternating optimization (AO) method to convert the non-convex optimization problem to a series of semi-definite programs (SDPs) and solve the SDPs iteratively. Simulation results show that the assistance of IRS can greatly improve the performance of the surveillance, and achieves significant advantages over the traditional relay-assisted wireless surveillance system.
Abstract: The intelligent reflecting surface (IRS), which consists of a large number of reflecting units, can adjust the phase shifts of its reflecting units to strengthen the desired signal and/or suppress the undesired signal. In this paper, we consider an IRS-assisted wireless surveillance system where an IRS is deployed to assist the legal surveillance r...
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Patterns in Hotel Revenue Management Forecasting Systems: Improved Sample Sizes, Frozen Intervals, Horizon Lengths, and Accuracy Measures
Victor Pimentel,
Aysajan Eziz,
Tim Baker
Issue:
Volume 6, Issue 1, January 2021
Pages:
8-15
Received:
6 January 2021
Accepted:
20 January 2021
Published:
28 January 2021
Abstract: Research in hotel revenue management system design has not paid much attention to the demand forecasting side of the system. And the research that has examined forecasting has tended to focus on the comparison of specific forecaster methodologies, as opposed to prioritizing how a total system should be parameterized: how far in the future should projections be, how much data to use to update each specific parameter, which measure of forecast error to use, and how long to freeze each parameter/forecast before updating. This paper fills this prioritization void by utilizing a full-functionality hotel reservation system simulation validated by the revenue management staff of a major hotel chain as the basis for running screening experiments on an exhaustive set of forecaster parameters with regards to their impact on bottom-line system performance (average nightly net revenue, where net revenue refers to total room rate receipts minus an overbooking per person penalty that estimates the discounted lost sales of future revenues). A screening experiment is run for each general type of operating environment (demand intensity, degree of market segment differentiation) that a property might face. We find that only two parameters are significant: the final combined forecast horizon length and how long that final forecast is frozen before updating. We find that these two factors interact in a negative fashion to influence net revenue.
Abstract: Research in hotel revenue management system design has not paid much attention to the demand forecasting side of the system. And the research that has examined forecasting has tended to focus on the comparison of specific forecaster methodologies, as opposed to prioritizing how a total system should be parameterized: how far in the future should pr...
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Knowing Ahead Mathematical Determinant of Bank Customers Credit Worthiness: A Safe Strategy for Funding Loan in a Critical Economy
Issue:
Volume 6, Issue 1, January 2021
Pages:
16-23
Received:
29 January 2020
Accepted:
11 March 2020
Published:
10 March 2021
Abstract: The study was carried out to identify relevant attributes that signals the capacity of borrower to pay back the loan and determine the fit of mathematical scoring model to evaluate credit worthiness of a potential borrower. The data was taken from primary and secondary sources which was through the use of questionnaires (primary source) while the secondary source was collection of data from all the financial statements of selected business owners in Ekpoma, Edo State credits history of these business owners as well. The descriptive research and the explanatory research designs were employed in this study. Two research questions were raised while one hypothesis was formulated to guide the study. Thirty five (35) business owners were randomly selected from Ekpoma metropolis of Edo state for this study based on loan applications and business capacity. The data collected were analyzed using Altman Z-scores, frequencies and percentages while the Pearson Product Moment Correlation Co-efficient was used to determine the relationship between Mathematical Scoring model and credits worthiness. The result showed that credit scores developed from borrower financial and non-financial records and history such as turnover, assets, previous loan repayment rate and trading capital perfectly classified them into five risk classes of A (Worthy and very able to payback), B (worthy and less able to pay back) and D (not worthy at all). The result revealed that credit score can safe award banks and creditors against credit risk default and loss of money. It was therefore recommended among others, that banks and credit facilities handlers should adopt mathematical credit scoring techniques to avoid loss of their money.
Abstract: The study was carried out to identify relevant attributes that signals the capacity of borrower to pay back the loan and determine the fit of mathematical scoring model to evaluate credit worthiness of a potential borrower. The data was taken from primary and secondary sources which was through the use of questionnaires (primary source) while the s...
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The Utilization of the Radon Transform for the Extraction of the Orientation of Linear Features in Binary Images
Issue:
Volume 6, Issue 1, January 2021
Pages:
24-29
Received:
9 January 2021
Accepted:
1 March 2021
Published:
1 April 2021
Abstract: This paper considers the idea of using the Radon transform to extract the orientation features of lines in binary images. The Radon transform sends a line at a particular orientation in image space to a point in feature space. The ensuing set of points in feature space is called a sinogram. This computation is usually performed for a large group of angles (over the interval [0,179] degrees taken in integer increments in this paper). Therefore, linear features at specific orientations will be mapped to points having maximum value at particular angles. For angular spacing of at least 5 degrees, the peaks of the sinogram at the angles corresponding to the orientations of the lines will be clearly visible (in a bar plot of sinogram peaks) above sinogram values at other angles. The mapping from image space to feature space accomplished by the Radon transform which maps rectangular coordinates (x,y) to coordinates (range, angle) provides for the garnering of the orientation of the linear features in binary images. In particular, the coordinates (range, angle) in the sinogram allow for distinguishing between lines oriented at one angle versus lines oriented at another angle or angles. This particular property of the sinogram allows for the extraction of the orientation features of lines in an image.
Abstract: This paper considers the idea of using the Radon transform to extract the orientation features of lines in binary images. The Radon transform sends a line at a particular orientation in image space to a point in feature space. The ensuing set of points in feature space is called a sinogram. This computation is usually performed for a large group of...
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