Shereen S. Ismail, Mitchell Sueker, Sayed Asaduzzaman, Hassan Reza, F. Vasefi, Hossein Kashani Zadeh
The Fish Supply Chain (FSC) industry faces a significant challenge in efficiently and affordably preserving fish quality and detecting adulteration throughout the chain. Quality, Adulteration and Traceability (QAT) is a multi-mode spectroscopy and AI-based handheld device that is developed by our team to identify fish species and assess fish freshness that can be integrated into the FSC ecosystem. We conducted a survey interviewing professionals across the FSC, including harvesters, processors, distributors, and retailers and queried them about how they evaluate fish freshness and the major issues faced in freshness inspection and fraud detection. We learned that traditional sensory evaluation and electronic noses are the most common methods used for fish quality and freshness assessment. QAT technology will play a role as a substitute for current methods and will offer rapid results for fish species identification, quality assessment, and nutritional content analysis. Blockchain (BC), as a Distributed Ledger Technology (DLT), can be integrated with FSC to securely monitor and record fish quality and freshness values each step of the FSC. This helps in maintaining product integrity and provides stakeholders with access to the entire journey of the fish product. We extend our experiments to study the degradation of fish freshness throughout the FSC to trigger the system once the rate of decay exceeds a certain limit. These results should be used so BC integration with smart contracts be able to compare its freshness grade to the history of recorded values. If the degradation in freshness exceeds the expected range, then the smart contract should raise an alarm to alert the system. In this way, BC-based FSC incorporating QAT technology is able to detect any degradation and flag products that may have compromised freshness or quality. This integration of technologies not only promises to revolutionize the FSC but also addresses issues like fraud and illegal fishing activities, ultimately delivering higher-quality and more transparent fish products to consumers.
{"title":"Seafood quality, adulteration, and traceability technology integrated with blockchain supply chain","authors":"Shereen S. Ismail, Mitchell Sueker, Sayed Asaduzzaman, Hassan Reza, F. Vasefi, Hossein Kashani Zadeh","doi":"10.1117/12.3014185","DOIUrl":"https://doi.org/10.1117/12.3014185","url":null,"abstract":"The Fish Supply Chain (FSC) industry faces a significant challenge in efficiently and affordably preserving fish quality and detecting adulteration throughout the chain. Quality, Adulteration and Traceability (QAT) is a multi-mode spectroscopy and AI-based handheld device that is developed by our team to identify fish species and assess fish freshness that can be integrated into the FSC ecosystem. We conducted a survey interviewing professionals across the FSC, including harvesters, processors, distributors, and retailers and queried them about how they evaluate fish freshness and the major issues faced in freshness inspection and fraud detection. We learned that traditional sensory evaluation and electronic noses are the most common methods used for fish quality and freshness assessment. QAT technology will play a role as a substitute for current methods and will offer rapid results for fish species identification, quality assessment, and nutritional content analysis. Blockchain (BC), as a Distributed Ledger Technology (DLT), can be integrated with FSC to securely monitor and record fish quality and freshness values each step of the FSC. This helps in maintaining product integrity and provides stakeholders with access to the entire journey of the fish product. We extend our experiments to study the degradation of fish freshness throughout the FSC to trigger the system once the rate of decay exceeds a certain limit. These results should be used so BC integration with smart contracts be able to compare its freshness grade to the history of recorded values. If the degradation in freshness exceeds the expected range, then the smart contract should raise an alarm to alert the system. In this way, BC-based FSC incorporating QAT technology is able to detect any degradation and flag products that may have compromised freshness or quality. This integration of technologies not only promises to revolutionize the FSC but also addresses issues like fraud and illegal fishing activities, ultimately delivering higher-quality and more transparent fish products to consumers.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"64 3","pages":"130600F - 130600F-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In an era characterized by the prolific generation of digital imagery through advanced artificial intelligence, the need for reliable methods to authenticate actual photographs from AI-generated ones has become paramount. The ever-increasing ubiquity of AI-generated imagery, which seamlessly blends with authentic photographs, raises concerns about misinformation and trustworthiness. Authenticating these images has taken on critical significance in various domains, including journalism, forensic science, and social media. Traditional methods of image authentication often struggle to adapt to the increasingly sophisticated nature of AI-generated content. In this context, frequency domain analysis emerges as a promising avenue due to its effectiveness in uncovering subtle discrepancies and patterns that are less apparent in the spatial domain. Delving into the imperative task of imagery authentication, this paper introduces a novel Generative Adversarial Networks (GANs) based AI-generated Imagery Authentication (GANIA) method using frequency domain analysis. By exploiting the inherent differences in frequency spectra, GANIA uncovers unique signatures that are difficult to replicate, ensuring the integrity and authenticity of visual content. By training GANs on vast datasets of real images, we create AI-generated counterparts that closely mimic the characteristics of authentic photographs. This approach enables us to construct a challenging and realistic dataset, ideal for evaluating the efficacy of frequency domain analysis techniques in image authentication. Our work not only highlights the potential of frequency domain analysis for image authentication but also underscores the importance of adopting generative AI approaches in studying this critical topic. Through this innovative fusion of AI and frequency domain analysis, we contribute to advancing image forensics and preserving trust in visual information in an AI-driven world.
{"title":"Generative adversarial networks-based AI-generated imagery authentication using frequency domain analysis","authors":"Nihal Poredi, Monica Sudarsan, Enoch Solomon, Deeraj Nagothu, Yu Chen","doi":"10.1117/12.3013240","DOIUrl":"https://doi.org/10.1117/12.3013240","url":null,"abstract":"In an era characterized by the prolific generation of digital imagery through advanced artificial intelligence, the need for reliable methods to authenticate actual photographs from AI-generated ones has become paramount. The ever-increasing ubiquity of AI-generated imagery, which seamlessly blends with authentic photographs, raises concerns about misinformation and trustworthiness. Authenticating these images has taken on critical significance in various domains, including journalism, forensic science, and social media. Traditional methods of image authentication often struggle to adapt to the increasingly sophisticated nature of AI-generated content. In this context, frequency domain analysis emerges as a promising avenue due to its effectiveness in uncovering subtle discrepancies and patterns that are less apparent in the spatial domain. Delving into the imperative task of imagery authentication, this paper introduces a novel Generative Adversarial Networks (GANs) based AI-generated Imagery Authentication (GANIA) method using frequency domain analysis. By exploiting the inherent differences in frequency spectra, GANIA uncovers unique signatures that are difficult to replicate, ensuring the integrity and authenticity of visual content. By training GANs on vast datasets of real images, we create AI-generated counterparts that closely mimic the characteristics of authentic photographs. This approach enables us to construct a challenging and realistic dataset, ideal for evaluating the efficacy of frequency domain analysis techniques in image authentication. Our work not only highlights the potential of frequency domain analysis for image authentication but also underscores the importance of adopting generative AI approaches in studying this critical topic. Through this innovative fusion of AI and frequency domain analysis, we contribute to advancing image forensics and preserving trust in visual information in an AI-driven world.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"13 10","pages":"1305812 - 1305812-15"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a pioneering first-generation dive-mask integrated eye tracking system for underwater health and cognition monitoring. Building on this foundation, we're exploring its potential for enhancing human-machine teaming in low-visibility, low-communication scenarios. By harnessing eye metrics to inform decision field theory, our aim is to revolutionize task allocation in extreme environments, prioritizing safety and efficiency.
{"title":"Eye tracking in extreme environments: from invention to new frontiers of human-machine teaming","authors":"Connor Tate, Jeff Phillips, Dawn Kernagis","doi":"10.1117/12.3013589","DOIUrl":"https://doi.org/10.1117/12.3013589","url":null,"abstract":"This paper presents a pioneering first-generation dive-mask integrated eye tracking system for underwater health and cognition monitoring. Building on this foundation, we're exploring its potential for enhancing human-machine teaming in low-visibility, low-communication scenarios. By harnessing eye metrics to inform decision field theory, our aim is to revolutionize task allocation in extreme environments, prioritizing safety and efficiency.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"74 s316","pages":"1305806 - 1305806-15"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Min, Hansel A. Mina, Sungho Shin, Iyll-Joon Doh, J. P. Robinson, Bartek Rajwa, Amanda J. Deering, E. Bae
Salmonella ser. Typhimurium is notorious for causing serious foodborne illnesses and presenting considerable public health risks. The study introduces an innovative system based on a quartz crystal microbalance, designed to detect the target pathogen by integrating the system around a smartphone. The system operates through a dual-mode approach, relying on two distinct mechanisms: measuring frequency changes due to variations in bacterial mass and quantifying fluorescence intensities resulting from bacteria captured by FITC-labeled antibodies. Incorporating FITC-labeled antibodies not only enhances the resonance frequency shift but also offers visual confirmation through the fluorescence signal. The integration of the quartz crystal microbalance system with a smartphone enables real-time monitoring. This system displays both frequency and temperature data, while also capturing fluorescence intensities to estimate the concentration of the target analyte. The smartphone-based system successfully detected Salmonella Typhimurium within a concentration range of 105 CFU/mL after the application of FITC-labeled antibodies. This portable QCM system represents a promising advancement in pathogen detection, holding significant potential to improve food safety protocols and strengthen public health safeguards.
{"title":"Detection and confirmation of Salmonella Typhimurium by smartphone-enabled optomechanical platform","authors":"H. Min, Hansel A. Mina, Sungho Shin, Iyll-Joon Doh, J. P. Robinson, Bartek Rajwa, Amanda J. Deering, E. Bae","doi":"10.1117/12.3016099","DOIUrl":"https://doi.org/10.1117/12.3016099","url":null,"abstract":"Salmonella ser. Typhimurium is notorious for causing serious foodborne illnesses and presenting considerable public health risks. The study introduces an innovative system based on a quartz crystal microbalance, designed to detect the target pathogen by integrating the system around a smartphone. The system operates through a dual-mode approach, relying on two distinct mechanisms: measuring frequency changes due to variations in bacterial mass and quantifying fluorescence intensities resulting from bacteria captured by FITC-labeled antibodies. Incorporating FITC-labeled antibodies not only enhances the resonance frequency shift but also offers visual confirmation through the fluorescence signal. The integration of the quartz crystal microbalance system with a smartphone enables real-time monitoring. This system displays both frequency and temperature data, while also capturing fluorescence intensities to estimate the concentration of the target analyte. The smartphone-based system successfully detected Salmonella Typhimurium within a concentration range of 105 CFU/mL after the application of FITC-labeled antibodies. This portable QCM system represents a promising advancement in pathogen detection, holding significant potential to improve food safety protocols and strengthen public health safeguards.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"1 5","pages":"130600H - 130600H-7"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Narasimha S. Prasad, Gabrielle Amalthea Trobare, Aria Tauraso, C. Su, Bradley Arnold, Fow-Sen Choa, Brian Cullum, Kamdeo D. Mandal, N. Singh
This paper explores the development of innovative materials for the dielectric energy storage for space components. The CaCu3Ti4O12 or CCTO belonging to perovskite family is of interest due to its colossal dielectric constant. It was demonstrated that materials synthesized at low temperature show nonequilibrium state and exhibit differences in the dielectric and resistivity values. The goal is to obtain high dielectric constant along with high resistivity values for achieving enhanced breakdown voltage. By using other members of the perovskite structures, it was demonstrated that similar colossal dielectric constant is observed and is dependent on processing methods. We have used heterovalent and dissimilar sized atom to replace Ca+2 ion. Accordingly, we replaced Ca+2 ion with heavy Ga+3 ion and developed gallium-based material system, Ga2/3 Cu3Ti4O12. Following successful synthesis, we measured its dielectric constant and resistivity and compared with CCTO material system. Results of five sets of samples showed that lower temperature processing demonstrated mechanism of grain growth, but due to copper flow in high temperature processed samples dielectric constant and resistivity values were different.
{"title":"Dielectric energy storage materials for space sensors: effect of processing on the performance","authors":"Narasimha S. Prasad, Gabrielle Amalthea Trobare, Aria Tauraso, C. Su, Bradley Arnold, Fow-Sen Choa, Brian Cullum, Kamdeo D. Mandal, N. Singh","doi":"10.1117/12.3013177","DOIUrl":"https://doi.org/10.1117/12.3013177","url":null,"abstract":"This paper explores the development of innovative materials for the dielectric energy storage for space components. The CaCu3Ti4O12 or CCTO belonging to perovskite family is of interest due to its colossal dielectric constant. It was demonstrated that materials synthesized at low temperature show nonequilibrium state and exhibit differences in the dielectric and resistivity values. The goal is to obtain high dielectric constant along with high resistivity values for achieving enhanced breakdown voltage. By using other members of the perovskite structures, it was demonstrated that similar colossal dielectric constant is observed and is dependent on processing methods. We have used heterovalent and dissimilar sized atom to replace Ca+2 ion. Accordingly, we replaced Ca+2 ion with heavy Ga+3 ion and developed gallium-based material system, Ga2/3 Cu3Ti4O12. Following successful synthesis, we measured its dielectric constant and resistivity and compared with CCTO material system. Results of five sets of samples showed that lower temperature processing demonstrated mechanism of grain growth, but due to copper flow in high temperature processed samples dielectric constant and resistivity values were different.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"47 1","pages":"1305903 - 1305903-6"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
NIWC Pacific will present a novel, cost-effective method for in situ measurement and characterization of atmospheric turbulence, as quantified by the atmospheric seeing parameter, r0. The technique will leverage spatially encoded QR codes that are imaged using normal imaging optics. The presentation will cover the theory of the technique along with simulation and experimental results compared to a commercial turbulence measurement system.
北印度洋委员会太平洋分会将介绍一种成本效益高的新方法，用于现场测量和描述大气湍流，并通过大气视场参数r0进行量化。该技术将利用空间编码的 QR 代码，使用普通成像光学器件进行成像。报告将介绍该技术的理论以及与商用湍流测量系统相比较的模拟和实验结果。
{"title":"Measuring turbulence using spatially encoded QR codes","authors":"Kyle R. Drexler, B. Neuner, Skylar D. Lilledahl","doi":"10.1117/12.3013556","DOIUrl":"https://doi.org/10.1117/12.3013556","url":null,"abstract":"NIWC Pacific will present a novel, cost-effective method for in situ measurement and characterization of atmospheric turbulence, as quantified by the atmospheric seeing parameter, r0. The technique will leverage spatially encoded QR codes that are imaged using normal imaging optics. The presentation will cover the theory of the technique along with simulation and experimental results compared to a commercial turbulence measurement system.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"9 3‐4","pages":"130610I - 130610I-12"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid and nondestructive identification of pork spoilage holds significant importance due to the inherent richness of nutrients and the conducive environment for bacterial proliferation within pork. This study focused on the non-destructive assessment of the total viable count in pork utilizing visible/near-infrared spectroscopy. By employing this technique, pork samples were subjected to analysis across the visible/near-infrared spectra range (400-1000 nm), with the total viable count determined through the plate counting method. The principal component analysis technique was used to consider whether there was variability in the spectra of pork with different total viable counts. Three different preprocessing methods in visible near-infrared spectroscopy for the prediction of total viable count in pork were compared, the preprocessing methods used were standard normal variate, multiplicative scatter correction and Savitzky-Golay smoothing. The results of the study show that, divisibility of pork with different total viable count in the low-dimensional space of the first and second principal components of principal component analysis. Among these preprocessing techniques, the study highlighted the superiority of the partial least squares regression model combined with standard normal variate preprocessing. This optimized model exhibited remarkable efficiency in predicting total viable count in pork. The best total viable count prediction model showed the RP, RMSEP, and RPD of 0.864, 0.826%, and 1.887, respectively. This study highlights the importance of rapid and non-destructive techniques for pork spoilage detection, contributing to improved food safety and quality assurance practices within the pork industry.
{"title":"Non-destructive detection of TVC in pork by machine learning techniques based on spectral information","authors":"Jiewen Zuo, Yankun Peng, Yong-yu Li, Yahui Chen, Tianzhen Yin","doi":"10.1117/12.3013154","DOIUrl":"https://doi.org/10.1117/12.3013154","url":null,"abstract":"The rapid and nondestructive identification of pork spoilage holds significant importance due to the inherent richness of nutrients and the conducive environment for bacterial proliferation within pork. This study focused on the non-destructive assessment of the total viable count in pork utilizing visible/near-infrared spectroscopy. By employing this technique, pork samples were subjected to analysis across the visible/near-infrared spectra range (400-1000 nm), with the total viable count determined through the plate counting method. The principal component analysis technique was used to consider whether there was variability in the spectra of pork with different total viable counts. Three different preprocessing methods in visible near-infrared spectroscopy for the prediction of total viable count in pork were compared, the preprocessing methods used were standard normal variate, multiplicative scatter correction and Savitzky-Golay smoothing. The results of the study show that, divisibility of pork with different total viable count in the low-dimensional space of the first and second principal components of principal component analysis. Among these preprocessing techniques, the study highlighted the superiority of the partial least squares regression model combined with standard normal variate preprocessing. This optimized model exhibited remarkable efficiency in predicting total viable count in pork. The best total viable count prediction model showed the RP, RMSEP, and RPD of 0.864, 0.826%, and 1.887, respectively. This study highlights the importance of rapid and non-destructive techniques for pork spoilage detection, contributing to improved food safety and quality assurance practices within the pork industry.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"78 6","pages":"130600B - 130600B-7"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Douglas B. Ruyle, David Curtis, Peter N. McMahon-Crabtree
Shadow imaging has been used for decades in astronomical observation of distant space objects. Synthetic Aperture Silhouette Imaging applies this technology to space domain awareness to enable fine resolution silhouette images of satellites in the Geosynchronous (GEO) belt to be collected with a linear array of hobby telescopes. As a satellite passes between a star and the observer on the ground, a North-South telescope array can detect the reduced stellar intensity as the shadow of the satellite passes over from West to East. This paper discusses the resolution advantages of collecting and stacking shadow images at multiple wavelengths to arrive at a multispectral improvement factor. A laboratory model is scaled to GEO according to the Fresnel diffraction integral before the silhouette is recovered through a phase retrieval algorithm. The recovered silhouettes are stacked and evaluated against the image of the original laboratory target to determine how closely the images match. The best Percent Difference (PD) between the reconstructed silhouette and the target silhouette is found by scaling the intensity of the diffraction pattern using a look up table to the fourth power. The best PD from a stacked image is using five layers between 475 nm and 675 nm. The five layers produce a resolution of approximately 50 cm. Each additional layer improves resolution from the expected value by approximately 4.23 cm from two layers to six layers.
{"title":"Shadow imagery resolution advantages from multispectral image stacking","authors":"Douglas B. Ruyle, David Curtis, Peter N. McMahon-Crabtree","doi":"10.1117/12.3012879","DOIUrl":"https://doi.org/10.1117/12.3012879","url":null,"abstract":"Shadow imaging has been used for decades in astronomical observation of distant space objects. Synthetic Aperture Silhouette Imaging applies this technology to space domain awareness to enable fine resolution silhouette images of satellites in the Geosynchronous (GEO) belt to be collected with a linear array of hobby telescopes. As a satellite passes between a star and the observer on the ground, a North-South telescope array can detect the reduced stellar intensity as the shadow of the satellite passes over from West to East. This paper discusses the resolution advantages of collecting and stacking shadow images at multiple wavelengths to arrive at a multispectral improvement factor. A laboratory model is scaled to GEO according to the Fresnel diffraction integral before the silhouette is recovered through a phase retrieval algorithm. The recovered silhouettes are stacked and evaluated against the image of the original laboratory target to determine how closely the images match. The best Percent Difference (PD) between the reconstructed silhouette and the target silhouette is found by scaling the intensity of the diffraction pattern using a look up table to the fourth power. The best PD from a stacked image is using five layers between 475 nm and 675 nm. The five layers produce a resolution of approximately 50 cm. Each additional layer improves resolution from the expected value by approximately 4.23 cm from two layers to six layers.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"5 6","pages":"130620A - 130620A-12"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Auditory hallucinations are a hallmark symptom of mental disorders such as schizophrenia, psychosis, and bipolar disorder. The biological basis for auditory perceptions and hallucinations, however, is not well understood. Understanding hallucinations may broadly reflect how our brains work — namely, by making predictions about stimuli and the environments that we navigate. In this work, we would like to use a recently developed language model to help the understanding of auditory hallucinations. Bio-inspired Large Language Models (LLMs) such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) can generate next words based on previously generated words from the embedded space and their pre-trained library with or without inputs. The generative nature of neural networks in GPT (like self-attention) can be analogously associated with the neurophysiological sources of hallucinations. Functional imaging studies have revealed that the hyperactivity of the auditory cortex and the disruption between auditory and verbal network activity may underlie auditory hallucinations’ etiology. Key areas involved in auditory processing suggest that regions involved in verbal working memory and language processing are also associated with hallucinations. Auditory hallucinations reflect decreased activity in verbal working memory and language processing regions, including the superior temporal and inferior parietal regions. Parallels between auditory processing and LLM transformer architecture may help to decode brain functions on meaning assignment, contextual embedding, and hallucination mechanisms. Furthermore, an improved understanding of neurophysiological functions and brain architecture would bring us one step closer to creating human-like intelligence.
{"title":"Exploring connections between auditory hallucinations and language model structures and functions","authors":"Janerra Allen, Luke Xia, L. E. Hong, Fow-Sen Choa","doi":"10.1117/12.3013964","DOIUrl":"https://doi.org/10.1117/12.3013964","url":null,"abstract":"Auditory hallucinations are a hallmark symptom of mental disorders such as schizophrenia, psychosis, and bipolar disorder. The biological basis for auditory perceptions and hallucinations, however, is not well understood. Understanding hallucinations may broadly reflect how our brains work — namely, by making predictions about stimuli and the environments that we navigate. In this work, we would like to use a recently developed language model to help the understanding of auditory hallucinations. Bio-inspired Large Language Models (LLMs) such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT) can generate next words based on previously generated words from the embedded space and their pre-trained library with or without inputs. The generative nature of neural networks in GPT (like self-attention) can be analogously associated with the neurophysiological sources of hallucinations. Functional imaging studies have revealed that the hyperactivity of the auditory cortex and the disruption between auditory and verbal network activity may underlie auditory hallucinations’ etiology. Key areas involved in auditory processing suggest that regions involved in verbal working memory and language processing are also associated with hallucinations. Auditory hallucinations reflect decreased activity in verbal working memory and language processing regions, including the superior temporal and inferior parietal regions. Parallels between auditory processing and LLM transformer architecture may help to decode brain functions on meaning assignment, contextual embedding, and hallucination mechanisms. Furthermore, an improved understanding of neurophysiological functions and brain architecture would bring us one step closer to creating human-like intelligence.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"79 3","pages":"130590A - 130590A-11"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joe Pappas, Venkateswara Dasari, Billy E. Geerhart, David M. Alexander, Peng Wang, S. Chaterji
We optimized and deployed the adaptive framework Virtuoso that can maintain real-time object detection even when experiencing high contention scenarios. The original Virtuoso framework uses an adaptive algorithm for the detection frame followed by a low-cost algorithm for the tracker frame which uses down-sampled images to reduce computation. One of our optimizations include detaching the single synchronous thread for detection and tracking into two parallel threads. This multi-threaded implementation allows for computationally high-cost detection algorithms to be used while still maintaining real-time output from the tracker thread. Another optimization we developed uses multiple down-sampled images to initialize each tracker based on the size of the input box; the multiple down-sampled images allow each tracker to choose the optimal image size for the box that it is tracking rather than a single down-sampled image being used for all trackers.
{"title":"Adaptive object detection algorithms for resource constrained autonomous robotic systems","authors":"Joe Pappas, Venkateswara Dasari, Billy E. Geerhart, David M. Alexander, Peng Wang, S. Chaterji","doi":"10.1117/12.3013781","DOIUrl":"https://doi.org/10.1117/12.3013781","url":null,"abstract":"We optimized and deployed the adaptive framework Virtuoso that can maintain real-time object detection even when experiencing high contention scenarios. The original Virtuoso framework uses an adaptive algorithm for the detection frame followed by a low-cost algorithm for the tracker frame which uses down-sampled images to reduce computation. One of our optimizations include detaching the single synchronous thread for detection and tracking into two parallel threads. This multi-threaded implementation allows for computationally high-cost detection algorithms to be used while still maintaining real-time output from the tracker thread. Another optimization we developed uses multiple down-sampled images to initialize each tracker based on the size of the input box; the multiple down-sampled images allow each tracker to choose the optimal image size for the box that it is tracking rather than a single down-sampled image being used for all trackers.","PeriodicalId":178341,"journal":{"name":"Defense + Commercial Sensing","volume":"194 1","pages":"130580C - 130580C-8"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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