18 research outputs found
Cold-Induced Arousal: A Theoretical Framework on Ambient Cold Air as an Antithesis to Caffeine (By Seonyeong Heo)
No full textThis theoretical model, developed by Seonyeong Heo (a.k.a. Nayami), explores the idea that cold ambient air—especially in temperatures below 5°C—can induce a physiological and psychological state of arousal comparable to that of caffeine.
The framework contrasts the mechanisms of cold exposure (via the sympathetic nervous system) with those of caffeine (via adenosine receptor blockade), proposing a dual-pathway model of cognitive awakening.
Originally conceived through personal observation and reflection during winter conditions in 2025, this document presents an informal but structured hypothesis for further exploration in behavioral science, neurophysiology, and environmental psychology.
Keywords: cognitive arousal, cold exposure, caffeine, antithesis model, winter stimulus, self-observation, environmental cognitio
RTScale: Sensitivity-Aware Adaptive Image Scaling for Real-Time Object Detection
Get PDFReal-time object detection is crucial in autonomous driving. To avoid catastrophic accidents, an autonomous car should detect objects with multiple cameras and make decisions within a certain time limit. Object detection systems can meet the real-time constraint by dynamically downsampling input images to proper scales according to their time budget. However, simply applying the same scale to all the images from multiple cameras can cause unnecessary accuracy loss because downsampling can incur a significant accuracy loss for some images.
To reduce the accuracy loss while meeting the real-time constraint, this work proposes RTScale, a new adaptive real-time image scaling scheme that applies different scales to different images reflecting their sensitivities to the scaling and time budget. RTScale infers the sensitivities of multiple images from multiple cameras and determines an appropriate image scale for each image considering the real-time constraint. This work evaluates object detection accuracy and latency with RTScale for two driving datasets. The evaluation results show that RTScale can meet real-time constraints with minimal accuracy loss
RT-IFTTT: Real-Time IoT Framework with Trigger Condition-aware Flexible Polling Intervals
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Rapid prototyping of IoT applications with Esperanto compiler
No full textIntegrating various networked devices, the Internet of Things (IoT) enables various new services like home automation, making its market larger and more competitive. Although rapid development of an IoT application is crucial to keep up with the highly competitive IoT market, developing an IoT application is challenging for programmers because the programmers should integrate multiple programmable devices and heterogeneous third-party devices. Some IoT frameworks integrate programming environments of multiple devices, but they either require device-specific implementation for third- party devices without any device abstraction, or abstract all the devices to the standard interfaces requiring unnecessary abstraction of programmable devices. This work introduces the Esperanto framework that integrates IoT devices with selective abstraction, allowing rapid prototyping of an IoT application. Exploiting the correspondence between an object and a thing in the object oriented programming (OOP) model, the Esperanto framework allows programmers to write only one OOP program instead of multiple programs for each device, and to manipulate third-party devices with their common ancestor classes. Compared to an existing approach on the integrated IoT programming, Esperanto requires 33.3% fewer lines of code to implement 5 IoT services, and reduces their response time by 44.8% on average. Moreover, with an empirical study, this work shows that the Esperanto framework reduces the development time by 52.7%.1
Integrated IoT Programming with Selective Abstraction
No full textThe explosion of networked devices has driven a new computing environment called the Internet of Things (IoT), enabling various services such as home automation and health monitoring. Despite the promising applicability of the IoT, developing an IoT service is challenging for programmers, because the programmers should integrate multiple programmable devices and heterogeneous third-party devices. Recent works have proposed integrated programming platforms, but they either require device-specific implementation for third-party devices without any device abstraction, or abstract all the devices to the standard interfaces requiring unnecessary abstraction of programmable devices. To integrate IoT devices with selective abstraction, this work revisits the object oriented programming (OOP) model, and proposes a new language extension and its compiler-runtime framework, called Esperanto. With three annotations that map each object to its corresponding IoT device, the Esperanto language allows programmers to integrate multiple programmable devices into one OOP program and to abstract similar third-party devices into their common ancestor classes. Given the annotations, the Esperanto compiler automatically partitions the integrated program into multiple sub-programs for each programmable IoT device, and inserts communication and synchronization code. Moreover, for the ancestor classes, the Esperanto runtime dynamically identifies connected third-party devices, and links their corresponding descendent objects. Compared to an existing approach on the integrated IoT programming, Esperanto requires 33.3% fewer lines of code to implement 5 IoT services, and reduces their response time by 44.8% on average.1
Heterogeneous Distributed Shared Memory for Lightweight Internet-of-Things Devices
No full textIot-hdsm is a compiler-runtime cooperative heterogeneous distributed shared memory (hdsm) framework in which the compiler unifies heterogeneous memory layouts of different iot devices and the runtime system provides a shared memory view of the devices. This article describes an implementation of three iot services with iot-hdsm and shows that iot-hdsm simplifies shared data management without harming quality of service.1112sciescopu
Varicose Veins and the Diagnosis of Chronic Venous Disease in the Lower Extremities
No full textVaricose veins usually present in the superficial veins of the lower extremities and are one
of the main clinical presentations of chronic venous disease (CVD). Patients’ symptoms
may vary according to the pathophysiology, location, and severity of CVD. The prevalence
of CVD in Korea has been increasing gradually. However, due to its broad clinical spectrum
and the subjective nature of its diagnosis using ultrasound, discrepancies in diagnostic and
treatment quality may exist among treating physicians. There have been recent efforts to
improve the quality of the diagnosis and treatment of varicose veins in Korea by standardizing
the diagnostic criteria and the indications for treatment. This study is a comprehensive
review of the clinical manifestations and diagnostic criteria of CVD based on the most
recent international and domestic guidelines and reports
TinySeg: Model Optimizing Framework for Image Segmentation on Tiny Embedded Systems
Get PDFImage segmentation is one of the major computer vision tasks, which is
applicable in a variety of domains, such as autonomous navigation of an
unmanned aerial vehicle. However, image segmentation cannot easily materialize
on tiny embedded systems because image segmentation models generally have high
peak memory usage due to their architectural characteristics. This work finds
that image segmentation models unnecessarily require large memory space with an
existing tiny machine learning framework. That is, the existing framework
cannot effectively manage the memory space for the image segmentation models.
This work proposes TinySeg, a new model optimizing framework that enables
memory-efficient image segmentation for tiny embedded systems. TinySeg analyzes
the lifetimes of tensors in the target model and identifies long-living
tensors. Then, TinySeg optimizes the memory usage of the target model mainly
with two methods: (i) tensor spilling into local or remote storage and (ii)
fused fetching of spilled tensors. This work implements TinySeg on top of the
existing tiny machine learning framework and demonstrates that TinySeg can
reduce the peak memory usage of an image segmentation model by 39.3% for tiny
embedded systems.Comment: LCTES 202
