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1. Real-Time Source-Tracking Spherical Microphone Arrays for Immersive Environments

   Mathews, Jonathan; Braasch, Jonas (August 2018, Audio Engineering Society Conference on Audio for Virtual and Augmented Reality)

   Spherical microphone arrays have attained considerable interest in recent years for their ability to decompose three-dimensional soundfields. This paper details real-time capabilities of a source-tracking system composed of a beamforming array and multiple lavalier microphones. Using the lavalier microphones for source identification, a particle filter can be implemented to allow independent tracking of the orientation of multiple sources simultaneously. This source identification and tracking mechanism is utilized in an immersive lab space. In conjunction with networked audiovisual equipment, the system can generate a real-time virtual representation of sound sources for a more dynamic telematic experience. 

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2. A Blockchain-Based Secure and Transparent Global Radioactive Source Lifecycle Management System

   https://doi.org/10.1109/SoutheastCon56624.2025.10971666  

   Poudel, Susan; Kausar, Firdous; Das, Debashis; Chatterjee, Pushpita; Hussain, Sajid; Ghosh, Uttam (March 2025, IEEE)

   The management of radioactive sources is a criti- cal process that ensures the safe and responsible handling of radioactive materials throughout their lifecycle. These sources require careful management from production to disposal, such as real-time tracking and radiation monitoring to follow everyone’s safety rules and protect people and the environment. However, these sources present significant global challenges, especially regarding safety, security, and transparency. Current systems face limitations such as fragmented oversight, lack of accountability, and risks of unauthorized access. To address these limitations, this paper proposes a blockchain-based radioactive source lifecycle management system to manage the lifecycle of radioactive sources. Blockchain’s decentralized and tamper-proof ledger secures data throughout the entire lifecycle of radioactive materials. By using smart contracts and access controls, the system ensures that only authorized parties can monitor and verify transactions in real- time, which reduces human error and prevents unauthorized changes to the data. Users can perform key operations such as retrieving source details, transferring ownership, updating source locations, and adding observers to our proposed system. Our experiment in designing and testing a blockchain application has proven the potential for a secure and transparent system that enhances global cooperation in managing radioactive sources. Overall, the proposed system not only addresses current challenges in radioactive source management but also enhances the tracking and monitoring of radioactive materials. 

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3. Localizing Concurrent Sound Sources with Binaural Microphones: A Simulation Study

   Orr, Jakeh Ebel (September 2023, Hearing research)

   Canlon Barbara (Ed.)

   The human auditory system can localize multiple sound sources using time, intensity, and frequency cues in the sound received by the two ears. Being able to spatially segregate the sources helps perception in a challenging condition when multiple sounds coexist. This study used model simulations to explore an algorithm for localizing multiple sources in azimuth with binaural (i.e., two) microphones. The algorithm relies on the “sparseness” property of daily signals in the time-frequency domain, and sound coming from different locations carrying unique spatial features will form clusters. Based on an interaural normalization procedure, the model generated spiral patterns for sound sources in the frontal hemifield. The model itself was created using broadband noise for better accuracy, because speech typically has sporadic energy at high frequencies. The model at an arbitrary frequency can be used to predict locations of speech and music that occurred alone or concurrently, and a classification algorithm was applied to measure the localization error. Under anechoic conditions, averaged errors in azimuth increased from 4.5° to 19° with RMS errors ranging from 6.4° to 26.7° as model frequency increased from 300 to 3000 Hz. The low-frequency model performance using short speech sound was notably better than the generalized cross-correlation model. Two types of room reverberations were then introduced to simulate difficult listening conditions. Model performance under reverberation was more resilient at low frequencies than at high frequencies. Overall, our study presented a spiral model for rapidly predicting horizontal locations of concurrent sound that is suitable for real-world scenarios. 

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4. STENSL: Microbial Source Tracking with ENvironment SeLection

   Ulzee An, Liat Shenhav (January 2022, mSystems)

   Microbial source tracking analysis has emerged as a widespread technique for characterizing the properties of complex microbial communities. However, this analysis is currently limited to source environments sampled in a specific study. In order to expand the scope beyond one single study and allow the exploration of source environments using large databases and repositories, such as the Earth Microbiome Project, a source selection procedure is required. Such a procedure will allow differentiating between contributing environments and nuisance ones when the number of potential sources considered is high. Here, we introduce STENSL (microbial Source Tracking with ENvironment SeLection), a machine learning method that extends common microbial source tracking analysis by performing an unsupervised source selection and enabling sparse identification of latent source environments. By incorporating sparsity into the estimation of potential source environments, STENSL improves the accuracy of true source contribution, while significantly reducing the noise introduced by noncontributing ones. We therefore anticipate that source selection will augment microbial source tracking analyses, enabling exploration of multiple source environments from publicly available repositories while maintaining high accuracy of the statistical inference. 

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5. Low Cost Light Source Identification in Real World Settings

   https://doi.org/10.1109/SECON55815.2022.9918545  

   Routh, Tushar; Saoda, Nurani; Rabbi Masum Billah, Md Fazlay; Campbell, Brad (September 2022, 2022 19th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON))

   Recent studies have shown that, experiencing the appropriate lighting environment in our day-to-day life is paramount, as different types of light sources impact our mental and physical health in many ways. Researchers have intercon-nected daylong exposure of natural and artificial lights with circadian health, sleep and productivity. That is why having a generalized system to monitor human light exposure and recommending lighting adjustments can be instrumental for maintaining a healthy lifestyle. At present methods for collecting daylong light exposure information and source identification contain certain limitations. Sensing devices are expensive and power consuming and methods of classifications are either inac-curate or possesses certain limitations. In addition, identifying the source of exposure is challenging for a couple of reasons. For example, spectral based classification can be inaccurate, as different sources share common spectral bands or same source can exhibit variation in spectrum. Also irregularities of sensed information in real world makes scenario complex for source identification. In this work, we are presenting a Low Power BLE enabled Color Sensing Board (LPCSB) for sensing background light parameters. Later, utilizing Machine learning and Neural Network based architectures, we try to pinpoint the prime source in the surrounding among four dissimilar types: Incandescent, LED, CFL and Sunlight. Our experimentation includes 27 distinct bulbs and sunlight data in various weather/time of the day/spaces. After tuning classifiers, we have investigated best parameter settings for indoor deployment and also analyzed robustness of each classifier in several imperfect situations. As observed performance degraded significantly after real world deployment, we include synthetic time series examples and filtered data in the training set for boosting accuracy. Result shows that our best model can detect the primary light source type in the surroundings with accuracy up to 99.30% in familiar and up to 90.25% in unfamiliar real world settings with enlarged training set, which is much elevated than earlier endeavors. 

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