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  1. AU="Hasib, Al"
  2. AU="Gao, Hongyang"
  3. AU="Bedrosian, P. A."
  4. AU="Hotujac, Ljubomir"
  5. AU="Falato, C"
  6. AU="Mayrink, Marcelo Oliveira"
  7. AU="Davidoff, Dahlia F"
  8. AU=Endo A
  9. AU="Chunlei Yu"
  10. AU="Nagao, Asuteka"
  11. AU="Derwael, Céline" AU="Derwael, Céline"
  12. AU="Yao, Yang"
  13. AU=Strutz Frank
  14. AU="Won-Sang Lee"
  15. AU="the ICHseq Investigators" AU="the ICHseq Investigators"
  16. AU="Green, J G"
  17. AU="Xinguang Yang"
  18. AU="Masamed, Rinat"
  19. AU="Flores-Estrada, Diana"
  20. AU="Castellanos, Jason A"
  21. AU="Kiberd, Bryce A"
  22. AU="Kaushal, Deepak"
  23. AU="Rouzaire, Paul"
  24. AU="Mohammed A. S. Abourehab"
  25. AU="Basa, S."
  26. AU="Rohner, Eliane"
  27. AU="Abu-Mahfouz, Adnan M"
  28. AU="Falanti, Andrea"
  29. AU="Yujing Dang"
  30. AU="Clare Duncan"
  31. AU="Calvo Soto, Andrea Patricia"
  32. AU="Joanna I. Olszewska"
  33. AU="Francesco Cavallieri"
  34. AU="Betaieb, Ehssen"
  35. AU="Fan, Xiaoyu"
  36. AU="Riveros-Magaña, Alma Rocío"
  37. AU="Zhang, Wei-Fen"
  38. AU="Ciuca, Catrinel"
  39. AU="Friend, James R"
  40. AU="Colin R. Jackson"
  41. AU="Messina, Claudia"
  42. AU="Faircloth, Chelsey"
  43. AU="Md. Zabirul Islam" AU="Md. Zabirul Islam"
  44. AU="Butcher, Xochitl"
  45. AU="Espay, Alberto J."

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  1. Artikel ; Online: TinyM

    Rashid, Hasib-Al / Ovi, Pretom Roy / Busart, Carl / Gangopadhyay, Aryya / Mohsenin, Tinoosh

    ArXiv

    2022  

    Abstract: With the emergence of Artificial Intelligence (AI), new attention has been given to implement AI algorithms on resource constrained tiny devices to expand the application domain of IoT. Multimodal Learning has recently become very popular with the ... ...

    Abstract With the emergence of Artificial Intelligence (AI), new attention has been given to implement AI algorithms on resource constrained tiny devices to expand the application domain of IoT. Multimodal Learning has recently become very popular with the classification task due to its impressive performance for both image and audio event classification. This paper presents
    Sprache Englisch
    Erscheinungsdatum 2022-02-09
    Erscheinungsland United States
    Dokumenttyp Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel: Design optimization and validation of UV-C illumination chamber for filtering facepiece respirators.

    Mohsin, Abu S M / Jamiruddin, Mohd Raeed / Peyal, Md Mahmudul Kabir / Sharmin, Shahana / Ahmed, Ashfaq / Puspita, Afrin Hossain / Sharfuddin, A A M / Malik, Afrida / Hasib, Al / Suchona, Sanjida Akter / Chowdhury, Arshad M / Kabir, Eva Rahman

    Heliyon

    2024  Band 10, Heft 5, Seite(n) e26348

    Abstract: In this study, we constructed an UV-C illumination chamber using commercially available germicidal lamps and other locally available low-cost components for general-purpose biological decontamination purposes. The illumination chamber provides uniform ... ...

    Abstract In this study, we constructed an UV-C illumination chamber using commercially available germicidal lamps and other locally available low-cost components for general-purpose biological decontamination purposes. The illumination chamber provides uniform illumination of around 1 J/cm
    Sprache Englisch
    Erscheinungsdatum 2024-02-28
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2835763-2
    ISSN 2405-8440
    ISSN 2405-8440
    DOI 10.1016/j.heliyon.2024.e26348
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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    Über subito bestellen

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  3. Buch ; Online: TinyM$^2$Net

    Rashid, Hasib-Al / Ovi, Pretom Roy / Busart, Carl / Gangopadhyay, Aryya / Mohsenin, Tinoosh

    A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devices

    2022  

    Abstract: With the emergence of Artificial Intelligence (AI), new attention has been given to implement AI algorithms on resource constrained tiny devices to expand the application domain of IoT. Multimodal Learning has recently become very popular with the ... ...

    Abstract With the emergence of Artificial Intelligence (AI), new attention has been given to implement AI algorithms on resource constrained tiny devices to expand the application domain of IoT. Multimodal Learning has recently become very popular with the classification task due to its impressive performance for both image and audio event classification. This paper presents TinyM$^2$Net -- a flexible system algorithm co-designed multimodal learning framework for resource constrained tiny devices. The framework was designed to be evaluated on two different case-studies: COVID-19 detection from multimodal audio recordings and battle field object detection from multimodal images and audios. In order to compress the model to implement on tiny devices, substantial network architecture optimization and mixed precision quantization were performed (mixed 8-bit and 4-bit). TinyM$^2$Net shows that even a tiny multimodal learning model can improve the classification performance than that of any unimodal frameworks. The most compressed TinyM$^2$Net achieves 88.4% COVID-19 detection accuracy (14.5% improvement from unimodal base model) and 96.8% battle field object detection accuracy (3.9% improvement from unimodal base model). Finally, we test our TinyM$^2$Net models on a Raspberry Pi 4 to see how they perform when deployed to a resource constrained tiny device.

    Comment: tinyML Research Symposium 2022
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Sound ; Electrical Engineering and Systems Science - Audio and Speech Processing
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-02-09
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  4. Buch ; Online: Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information

    Hosseini, Morteza / Ren, Haoran / Rashid, Hasib-Al / Mazumder, Arnab Neelim / Prakash, Bharat / Mohsenin, Tinoosh

    2020  

    Abstract: Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the attention of the research community to the machine-aided diagnosis of ... ...

    Abstract Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the attention of the research community to the machine-aided diagnosis of respiratory problems. This paper is thus an effort to exploit machine learning for classification of respiratory problems and proposes a framework that employs as much correlated information (auditory and demographic information in this work) as a dataset provides to increase the sensitivity and specificity of a diagnosing system. First, we use deep convolutional neural networks (DCNNs) to process and classify a publicly released pulmonary auditory dataset, and then we take advantage of the existing demographic information within the dataset and show that the accuracy of the pulmonary classification increases by 5% when trained on the auditory information in conjunction with the demographic information. Since the demographic data can be extracted using computer vision, we suggest using another parallel DCNN to estimate the demographic information of the subject under test visioned by the processing computer. Lastly, as a proposition to bring the healthcare system to users' fingertips, we measure deployment characteristics of the auditory DCNN model onto processing components of an NVIDIA TX2 development board.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Human-Computer Interaction
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2020-11-26
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  5. Buch: Kitāb ʾanbāṭ al-mīyāh al-ḫaffīya

    Karḥī, Abū-Bakr Muḥammad Ibn al-Ḥasan al-Ḥāsib al-

    1940  

    Verfasserangabe Taṣnīf Abū-Bakr Muḥammad Ibn al-Ḥasan al-Ḥāsib al- Karḥī
    Sprache Arabisch
    Umfang 74 S
    Ausgabenhinweis Ṭabʿa 1
    Verlag Maṭbaʿat Dāʾirat al-Maʿārif al-ʿUṯmānīya
    Erscheinungsort Ḥaidarābād
    Dokumenttyp Buch
    Anmerkung In arab. Schr.
    Datenquelle Ehemaliges Sondersammelgebiet Küsten- und Hochseefischerei

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