More Like this

1. Enhanced Piezoelectric, Ferroelectric, and Electrostrictive Properties of Lead‐Free (1‐ x )BCZT‐( x )BCST Electroceramics with Energy Harvesting Capability

   https://doi.org/10.1002/smll.202300549  

   Baraskar, Bharat G. ; Kolekar, Yesappa D. ; Thombare, Balu. R. ; James, Ajit. R. ; Kambale, Rahul C. ; Ramana, C. V. ( May 2023 , Small)

   Next‐generation electronics and energy technologies can now be developed as a result of the design, discovery, and development of novel, environmental friendly lead (Pb)‐free ferroelectric materials with improved characteristics and performance. However, there have only been a few reports of such complex materials’ design with multi‐phase interfacial chemistry, which can facilitate enhanced properties and performance. In this context, herein, novel lead‐free piezoelectric materials (1‐x)Ba_0.95Ca_0.05Ti_0.95Zr_0.05O₃‐(x)Ba_0.95Ca_0.05Ti_0.95Sn_0.05O₃, are reported, which are represented as (1‐x)BCZT‐(x)BCST, with demonstrated excellent properties and energy harvesting performance. The (1‐x)BCZT‐(x)BCST materials are synthesized by high‐temperature solid‐state ceramic reaction method by varyingxin the full range (x= 0.00–1.00). In‐depth exploration research is performed on the structural, dielectric, ferroelectric, and electro‐mechanical properties of (1‐x)BCZT‐(x)BCST ceramics. The formation of perovskite structure for all ceramics without the presence of any impurity phases is confirmed by X‐ray diffraction (XRD) analyses, which also reveals that the Ca²⁺, Zr⁴⁺, and Sn⁴⁺are well dispersed within the BaTiO₃lattice. For all (1‐x)BCZT‐(x)BCST ceramics, thorough investigation of phase formation and phase‐stability using XRD, Rietveld refinement, Raman spectroscopy, high‐resolution transmission electron microscopy (HRTEM), and temperature‐dependent dielectric measurements provide conclusive evidence for the coexistence of orthorhombic + tetragonal (Amm2+P4mm) phases at room temperature. The steady transition ofAmm2crystal symmetry toP4mmcrystal symmetry with increasingxcontent is also demonstrated by Rietveld refinement data and related analyses. The phase transition temperatures, rhombohedral‐orthorhombic (T_R‐O), orthorhombic‐ tetragonal (T_O‐T), and tetragonal‐cubic (T_C), gradually shift toward lower temperature with increasingxcontent. For (1‐x)BCZT‐(x)BCST ceramics, significantly improved dielectric and ferroelectric properties are observed, including relatively high dielectric constantε_r≈ 1900–3300 (near room temperature),ε_r≈ 8800–12 900 (near Curie temperature), dielectric loss, tanδ≈ 0.01–0.02, remanent polarizationP_r≈ 9.4–14 µC cm⁻², coercive electric fieldE_c≈ 2.5–3.6 kV cm⁻¹. Further, high electric field‐induced strainS≈ 0.12–0.175%, piezoelectric charge coefficientd₃₃≈ 296–360 pC N⁻¹, converse piezoelectric coefficient ≈ 240–340 pm V⁻¹, planar electromechanical coupling coefficientk_p≈ 0.34–0.45, and electrostrictive coefficient (Q₃₃)_avg≈ 0.026–0.038 m⁴C⁻²are attained. Output performance with respect to mechanical energy demonstrates that the (0.6)BCZT‐(0.4)BCST composition (x= 0.4) displays better efficiency for generating electrical energy and, thus, the synthesized lead‐free piezoelectric (1‐x)BCZT‐(x)BCST samples are suitable for energy harvesting applications. The results and analyses point to the outcome that the (1‐x)BCZT‐(x)BCST ceramics as a potentially strong contender within the family of Pb‐free piezoelectric materials for future electronics and energy harvesting device technologies.

    

   more » « less
2. Enhancing 360 Video Streaming through Salient Content in Head-Mounted Displays

   https://doi.org/10.3390/s23084016  

   Nguyen, Anh ; Yan, Zhisheng ( April 2023 , Sensors)

   Predicting where users will look inside head-mounted displays (HMDs) and fetching only the relevant content is an effective approach for streaming bulky 360 videos over bandwidth-constrained networks. Despite previous efforts, anticipating users’ fast and sudden head movements is still difficult because there is a lack of clear understanding of the unique visual attention in 360 videos that dictates the users’ head movement in HMDs. This in turn reduces the effectiveness of streaming systems and degrades the users’ Quality of Experience. To address this issue, we propose to extract salient cues unique in the 360 video content to capture the attentive behavior of HMD users. Empowered by the newly discovered saliency features, we devise a head-movement prediction algorithm to accurately predict users’ head orientations in the near future. A 360 video streaming framework that takes full advantage of the head movement predictor is proposed to enhance the quality of delivered 360 videos. Practical trace-driven results show that the proposed saliency-based 360 video streaming system reduces the stall duration by 65% and the stall count by 46%, while saving 31% more bandwidth than state-of-the-art approaches. 

   more » « less
3. Evaluating how extended reality delivery device and preservice teacher major impact presence in immersive learning environments

   https://doi.org/10.1111/bjet.13446  

   Gandolfi, Enrico ; Ferdig, Richard E. ; Kosko, Karl W. ( February 2024 , British Journal of Educational Technology)

   Teacher education has begun to embrace the use of 360 videos to improve preservice teachers' (PSTs) engagement and immersion. While recent research on such use is promising overall, there are specific questions that have been left unanswered about the construct of presence in 360 videos. More specifically, research has yet to fully explore how video delivery devices and PST characteristics may impact presence. The purpose of this study was to respond to this gap in the literature by examining PST major, delivery device (ie, head mounted display vs. flat screen), and the interaction between the two in informing presence. A total of 93 PSTs watched 360 videos and then completed a questionnaire. Findings indicated that presence in head‐mounted displays was related to emotions and agency. Conversely, presence with flat screens meant familiarity and control. Moreover, PST major was a predictor of presence, suggesting that 360 videos should be personalized according to PST career and professional goals. Finally, the interaction effect of preparing to teach K‐12 mathematics and wearing a head‐mounted display had a negative and statistically significant effect on participants' perceived presence, thus indicating a non‐cumulative effect.

   What is already known about this topic

   360 videos are increasingly used to support preservice teacher training.

   360 videos for teacher education foster immersion, presence, and noticing skills.

   What this paper adds

   Empirical evidence that 360 videos experienced with head‐mounted display facilitate agency and emotional involvement.

   Empirical evidence that 360 videos experienced with flat screens facilitate feelings of control.

   Data showed that preservice teachers' content area (math) was a positive predictor of presence in 360 videos.

   Implications for practice and/or policy

   Head‐mounted displays show promise as a delivery mode for 360 videos in teacher education.

   360 videos should be personalized according to PST career and professional goals.

   The eXtended Reality Presence Scale is confirmed as a robust instrument to measure presence in immersive environments.

   More research is needed to evaluate the scalability of this approach in other contexts.

    

   more » « less
4. Navigation Graph for Tiled Media Streaming

   https://doi.org/10.1145/3343031.3351021  

   Park, Jounsup ; Nahrstedt, Klara ( October 2019 , ACM International Conference on Multimedia)

   After the emergence of video streaming services, more creative and diverse multimedia content has become available, and now the capability of streaming 360-degree videos will open a new era of multimedia experiences. However, streaming these videos requires larger bandwidth and less latency than what is found in conventional video streaming systems. Rate adaptation of tiled videos and view prediction techniques are used to solve this problem. In this paper, we introduce the Navigation Graph, which models viewing behaviors in the temporal (segments) and the spatial (tiles) domains to perform the rate adaptation of tiled media associated with the view prediction. The Navigation Graph allows clients to perform view prediction more easily by sharing the viewing model in the same way in which media description information is shared in DASH. It is also useful for encoding the trajectory information in the media description file, which could also allow for more efficient navigation of 360-degree videos. This paper provides information about the creation of the Navigation Graph and its uses. The performance evaluation shows that the Navigation Graph based view prediction and rate adaptation outperform other existing tiled media streaming solutions. Navigation Graph is not limited to 360-degree video streaming applications, but it can also be applied to other tiled media streaming systems, such as volumetric media streaming for augmented reality applications. 

   more » « less
5. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 

   more » « less