News & Press Releases

Topic: Experimental Demonstration of Sound Source Bearing Estimation at a Single Receiver Using Spectral Cues Location: Lester W. Cory Conference Room Science & Engineering Building (SENG), Room 213A Abstract: Traditionally, bearing estimation of a sound source is accomplished by exploiting relative time differences of arrival across an array of sensors. For scenarios where arrays of sensors cannot be effectively utilized due to size or cost, an alternative method for source localization must be considered. Yovel et al. [Science, 2010] discovered one such method when observing that bats steer the main response axis (MRA) of their echolocation beams askew of a target. While this strategy reduces the SNR of a received echo, it paradoxically improves the Fisher information about a target's bearing angle. This trade-off suggests there are spectral cues which improve target localization despite the lost signal power. For a single sensor with far-field frequency-dependent directivity, and a known broadband waveform of finite temporal support and sufficient SNR, it is possible to locate a sound source by the angle-dependent lowpass filtering of the signal at the receiver. Furthermore, there exists an angle which minimizes the bearing angle estimate that is near to, but not coincident with the main response angle of the receiver. This thesis presents experiments demonstrating a man-made system estimating a source's bearing from signal spectral information using a single directional sensor with frequency dependence over the bandwidth of the received signal. A linear FM chirp from a source in the far field is recorded, measuring the beampattern of the receiver. Maximum likelihood estimates (MLE) of source bearing are calculated in a Monte Carlo algorithm, comparing noise-corrupted recordings to a randomized dictionary of template recordings. Mean-squared error (MSE) is computed as a function of source angle and compared to the Cramer-Rao lower bound (CRLB). Experimental results support that the MSE is not proportional to angle-dependent SNR, rather there are local variance minima away from the receiver MRA where the received signal power is attenuated. The MSE local minima are consistent with optimal angles observed in previous studies simulating the exploitation of spectral cues on target localization [Kloepper et al., JASA-EL 2018] [Tidwell & Buck, IEEE SSPD 2019]. Advisor(s): Dr. John R. Buck, Chancellor Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Dayalan P. Kasilingam, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Paul J. Gendron, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. John R. Buck email at jbuck@umassd.edu Zoom Conference Link: https://umassd.zoom.us/j/91744864902 Meeting ID: 917 4486 4902 Passcode: 844492

Science & Engineering Building, Lester W. Cory Conference Room: Room 213A

Interested in studying abroad? Do you have a quick question about the opportunities that are available or the overall process? Stop by the International Programs Office (IPO)! Students will be seen on a first come, first served basis.

International Programs Office LARTS 016

Financial Aid Services wants to remind all students to file their FAFSA! Join Financial Aid Services for FAFSA Help Labs in LARTS 203 on Wednesdays and Fridays from 3-4pm for help filing your FAFSA and learning more about financial aid. Contact Mark Yanni myanni@umassd.edu

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Department of Fisheries Oceanography "Diverse uses for Species Distribution Models (SDMs) in New England fisheries management" Michelle Bachman Lead Fishery Analyst, NEFMC Wednesday, November 27, 2024 3pm-4pm SMAST E 101-102 and via Zoom Abstract: Species Distribution Models (SDMs) combine presence / absence or relative abundance data from fishery-independent surveys with environmental data to predict the probability of marine fish and shellfish species occurrence through space and time. Using Community Basis Function Modeling techniques (Hui et al. 2023), offshore and inshore fish survey data, and a diverse suite of environmental predictors, we are estimating distributions for New England Council and Mid-Atlantic Council managed species and other abundant species in the Northeast U.S. Shelf Ecosystem. A solid understanding of current species distributions and the factors that influence them is essential to fisheries management decision-making in an era of climate change. We envision diverse applications for model outputs that aim to improve the responsiveness and resilience of fisheries management. The initial application for these model outputs is revising essential fish habitat designation maps. The Council's essential fish habitat designations support fisheries management decisions as well as consultations on non-fishing projects that are likely to impact fish habitats, and, by extension, fishery resources and fisheries. The three climate-resilience applications are: (1) identifying considerations for designating ecosystem component species in our fishery management plans, (2) developing revisions to governance approaches to account for current vs. historic species distributions, and (3) evaluating the results of portfolio analyses that will be used to identify opportunities and gaps in our management system, for example how fishing permits are structured. This talk will briefly describe our modeling approach and share how the results will be applied to each of these four projects. Potential future updates to these SDMs will also be noted. Join the Zoom Note: Meeting passcode required, email contact below to receive To request the Zoom passcode, or for any other questions, please email Callie Rumbut at c.rumbut@umassd.edu

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FAFSA Lab Closed today Due to Holiday. Contact Mark Yanni, myanni@umassd.edu

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Mechanical Engineering / Industrial & Systems Engineering (ISE) MS Project Presentation by Mr. WASEEMAKRAM MOHAMMED DATE: December 2, 2024 TIME: 2pm-4pm LOCATION: Join Zoom Meeting https://umassd.zoom.us/j/96829665890?pwd=XnPY9KQmOnsyiEqQYR1o9RP5byJsQX.1 Meeting ID: 968 2966 5890 Passcode: 977641 TOPIC: CATSED OIL SEALER BLOCK USED IN MINING EQUIPMENT WITH CAM PROFILE ABSTRACT: Casted oil sealer blocks function by creating a tight seal between moving components, preventing the leakage of lubricants. This ensures that the machinery operates efficiently by reducing friction and wear. Additionally, they help to protect internal components from contaminants like dust and debris, extending the lifespan of the equipment, which can cause costly damage to the machine. The casted oil sealer block is designed to ensure a tight seal in mining equipment, preventing oil leaks and maintaining optimal performance. Its cam profile enhances the sealing efficiency by providing a precise fit and improved durability in harsh mining conditions. Cam sealers are used to prevent dust from entering mining equipment, and over time, the frictional wear of the cam sealer can lead to a decrease in its effectiveness. It is important to monitor cam sealers for signs of frictional wear and replace them, when necessary, to maximize the effectiveness of the mining equipment. The present work focused on structural stability of cam sealer with different materials made of gravitational casting. SS316 and H13 are materials considered noncorrosive metals for many applications of mining. As we know, the lubrication can control the heat fluxes and internal temperatures of the seal body, most of the literature concentrates on the top load digging forces on the entire body. Rock digging is a different scenario, and the forces act in opposite direction. Considering these factors an increment load on the cam surface analyzed for deformed stability in profile. To avoid damage directly on base block oil injected hose the maximum load applied as 120 kg on sealer block cam profile. The materials were compared after analysis and the cost evaluation done in industrial production criteria for both the materials. ADVISOR: Dr. Wenzhen Huang, Professor, Department of Mechanical Engineering, UMass Dartmouth COMMITTEE MEMBERS: Dr. Vijaya Chalivendra, Assistant Professor, Department of Mechanical Engineering, UMass Dartmouth Dr. Md Habibor Rahman, Assistant Professor, Department of Mechanical Engineering, UMass Dartmouth Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Wenzhen Huang (whuang@umassd.edu) or Sue Cunha (scunha@umassd.edu).

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