Anticipating Daily Intention using On-Wrist Motion Triggered Sensing

Anticipating human intention by observing one's actions has many applications. For instance, picking up a cellphone, then a charger (actions) implies that one wants to charge the cellphone (intention). By anticipating the intention, an intelligent system can guide the user to the closest power outlet. We propose an on-wrist motion triggered sensing system for anticipating daily intentions, where the on-wrist sensors help us to persistently observe one's actions. The core of the system is a novel Recurrent Neural Network (RNN) and Policy Network (PN), where the RNN encodes visual and motion observation to anticipate intention, and the PN parsimoniously triggers the process of visual observation to reduce computation requirement. We jointly trained the whole network using policy gradient and cross-entropy loss. To evaluate, we collect the first daily "intention" dataset consisting of 2379 videos with 34 intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%, 97.56% accuracy on three users while processing only 29% of the visual observation on average

Risk-based maintenance strategy for deteriorating bridges using a hybrid computational intelligence technique: a case study

The current bridge inspection and maintenance protocol that is used in most countries focuses primarily on the visible aspects of bridge fitness and underestimates the invisible aspects, such as resistance to scouring and earthquake hazards. To help transportation authorities to better consider both aspects, the present study developed a new computational intelligence system, the so-called risk- based evaluation model for bridge life-cycle maintenance strategy (REMBMS). This model considers the three main risk factors of component deterioration, scouring and earthquakes in order to minimise the expected life-cycle cost of bridge maintenance. Monte Carlo simulation is used to estimate the probability of bridge maintenance. The evolutionary support vector machine inference model (ESIM) was applied to estimate the risk-related maintenance cost using historical data from the Taiwan Bridge Management System (TBMS) database. The time-influenced expected costs were obtained by multiply- ing each maintenance probability with its associated cost. Finally, the symbiotic organisms search (SOS) algorithm is used to identify the bridge maintenance schedule that optimises the life-cycle main- tenance cost. The present study provides to bridge management authorities an effective approach for determining the optimal timing and budget for maintaining transportation bridges

The heterojunction effects of TiO2 nanotubes fabricated by atomic layer deposition on photocarrier transportation direction

The heterojunction effects of TiO2 nanotubes on photoconductive characteristics were investigated. For ITO/TiO2/Si diodes, the photocurrent is controlled either by the TiO2/Si heterojunction (p-n junction) or the ITO-TiO2 heterojunction (Schottky contact). In the short circuit (approximately 0 V) condition, the TiO2-Si heterojunction dominates the photocarrier transportation direction due to its larger space-charge region and potential gradient. The detailed transition process of the photocarrier direction was investigated with a time-dependent photoresponse study. The results showed that the diode transitioned from TiO2-Si heterojunction-controlled to ITO-TiO2 heterojunction-controlled as we applied biases from approximately 0 to -1 V on the ITO electrode

Paper-based tuberculosis diagnostic devices with colorimetric gold nanoparticles

A colorimetric sensing strategy employing gold nanoparticles and a paper assay platform has been developed for tuberculosis diagnosis. Unmodified gold nanoparticles and single-stranded detection oligonucleotides are used to achieve rapid diagnosis without complicated and time-consuming thiolated or other surface-modified probe preparation processes. To eliminate the use of sophisticated equipment for data analysis, the color variance for multiple detection results was simultaneously collected and concentrated on cellulose paper with the data readout transmitted for cloud computing via a smartphone. The results show that the 2.6 nM tuberculosis mycobacterium target sequences extracted from patients can easily be detected, and the turnaround time after the human DNA is extracted from clinical samples was approximately 1 h

KINEMATICAL ANALYSIS OF TWO DIFFERENT FOREHAND BADMINTON DROP SHOTS TECHNIQUES

The purpose of this study was to compare the kinematics variables between badminton forehand regular and reverse slice drop shots. The participants were eight elite male players. Eight Vicon Motion T20s System cameras (300Hz) were used to record the 3D kinematic data, which were computed by Visual 3D software. All the variables were tested by Wilcoxon rank analysis of variance nonparametric statistical test with the significant level at a = .05. The results showed that there was significant difference between the two forehand drop shots in the racket pan angle. The strategy of two drop shots seems different. That might because the reverse slice drop was with greater shoulder abduction movement than the regular drop shot. The players performed reverse slice drop shot might because that the abduction movement was similar with the smash

Charge-Trapping Devices Using Multilayered Dielectrics for Nonvolatile Memory Applications

Charge-trapping devices using multilayered dielectrics were studied for nonvolatile memory applications. The device structure is Al/Y2O3/Ta2O5/SiO2/Si (MYTOS). The MYTOS field effect transistors were fabricated using Ta2O5 as the charge storage layer and Y2O3 as the blocking layer. The electrical characteristics of memory window, program/erase characteristics, and data retention were examined. The memory window is about 1.6 V. Using a pulse voltage of 6 V, a threshold voltage shift of ~1 V can be achieved within 10 ns. The MYTOS transistors can retain a memory window of 0.81 V for 10 years

A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans

Liver cancer has high morbidity and mortality rates in the world. Multi-phase CT is a main medical imaging modality for detecting/identifying and diagnosing liver tumors. Automatically detecting and classifying liver lesions in CT images have the potential to improve the clinical workflow. This task remains challenging due to liver lesions' large variations in size, appearance, image contrast, and the complexities of tumor types or subtypes. In this work, we customize a multi-object labeling tool for multi-phase CT images, which is used to curate a large-scale dataset containing 1,631 patients with four-phase CT images, multi-organ masks, and multi-lesion (six major types of liver lesions confirmed by pathology) masks. We develop a two-stage liver lesion detection pipeline, where the high-sensitivity detecting algorithms in the first stage discover as many lesion proposals as possible, and the lesion-reclassification algorithms in the second stage remove as many false alarms as possible. The multi-sensitivity lesion detection algorithm maximizes the information utilization of the individual probability maps of segmentation, and the lesion-shuffle augmentation effectively explores the texture contrast between lesions and the liver. Independently tested on 331 patient cases, the proposed model achieves high sensitivity and specificity for malignancy classification in the multi-phase contrast-enhanced CT (99.2%, 97.1%, diagnosis setting) and in the noncontrast CT (97.3%, 95.7%, screening setting)