Accurate Device Temperature Forecasting using Recurrent Neural Network for Smartphone Thermal Management

Jaiswal, Aditi; Amjad, Zeeshan; Jha, Suraj; Sahni, Nikhil; B, Vaisakh Punnekkattu Chirayil S; Nair, Renju C

doi:10.1109/ijcnn52387.2021.9533732

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…In the field of non-invasive horse monitoring, there is progress in the use of biometric devices, including inertial measurement units (IMU) to monitor motion speed [24], motion caption [28], and breath-jockey to assess the respiratory rate and kinematic parameters of performance horses [29], single inertial sensor (SIS) for the evaluation of the neuromusculoskeletal system of performance horses [30,31], and infrared thermography (IRT) for quantification of the radiant energy emitted by the body surface, which is proportional to the horse's effort intensity [32,33]. Considering the advances made in recent years in the use of IRT to assess the rider:horse bodyweight ratio [20,21,34] and the rapidly increasing availability of smartphone thermal camera software and medical applications [35][36][37][38], the IRT-based assessment of rider-horse fit may be shortly integrated into everyday equine practice.…”

Section: Introductionmentioning

confidence: 99%

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding

Domino

Borowska

Zdrojkowski

et al. 2022

Sensors

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As obesity is a serious problem in the human population, overloading of the horse’s thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse’s back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features.

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Section: Introductionmentioning

confidence: 99%

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding

Domino

Borowska

Zdrojkowski

et al. 2022

Sensors

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Simulation of Heat Dissipation of CPU Chip in Smart Phone

Zeng,

Zhong,

Zeng

et al. 2024

2024 25th International Conference on Electronic Packaging Technology (ICEPT)

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The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing

Domino

Borowska

Trojakowska

et al. 2022

Animals

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Appropriate matching of rider–horse sizes is becoming an increasingly important issue of riding horses’ care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body’s surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10–12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.

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Accurate Device Temperature Forecasting using Recurrent Neural Network for Smartphone Thermal Management

Cited by 4 publications

References 14 publications

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding

Simulation of Heat Dissipation of CPU Chip in Smart Phone

The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse’s Thoracolumbar Region Evaluated by Advanced Thermal Image Processing

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