Flexible ultraviolet and ambient light sensor based on nanomaterial network fabricated by using selective and localized wet-chemical reactions

We report ZnO nanowire- and TiO_2 nanotube-based light sensors on flexible polymer substrates fabricated by localized hydrothermal synthesis and liquid phase deposition (LPD). This method realized simple and cost-effective in situ synthesis and integration of one-dimensional ZnO and TiO_2 nanomaterials. The fabricated sensor devices with ZnO nanowires and TiO_2 nanotubes show very high sensitivity and quick response to the ultraviolet (UV) and ambient light, respectively. In addition, our direct synthesis and integration method result in mechanical robustness under external loading such as static and cyclic bending because of the strong bonding between the nanomaterial and the electrode. By controlling the reaction time of the LPD process, the Ti/Zn ratio could be simply modulated and the spectral sensitivity to the light in the UV to visible range could be controlled

Localized Liquid-Phase Synthesis of Porous SnO_2 Nanotubes on MEMS Platform for Low Power, High Performance Gas Sensors

We have developed highly sensitive, low-power gas sensors through the novel integration method of porous SnO_2 nanotubes (NTs) on a micro-electro-mechanical-systems (MEMS) platform. As a template material, ZnO nanowires (NWs) were directly synthesized on beam-shaped, suspended microheaters through an in situ localized hydrothermal reaction induced by local thermal energy around the Joule-heated area. Also, the liquid-phase deposition process enabled the formation of a porous SnO_2 thin film on the surface of ZnO NWs and simultaneous etching of the ZnO core, eventually to generate porous SnO_2 NTs. Because of the localized synthesis of SnO_2 NTs on the suspended microheater, very low power for the gas sensor operation (<6 mW) has been realized. Moreover, the sensing performance (e.g., sensitivity and response time) of synthesized SnO_2 NTs was dramatically enhanced compared to that of ZnO NWs. In addition, the sensing performance was further improved by forming SnO_2–ZnO hybrid nanostructures due to the heterojunction effect

On Localized Countermeasure Against Reactive Jamming Attacks in Smart Grid Wireless Mesh Networks

Reactive jamming attacks have been considered as one of the most lethal and disruptive threats to subvert or disrupt wireless networks since they attack the broadcast nature of transmission mediums by injecting interfering signals. Existing countermeasures for the Internet against reactive jamming attacks, i.e., channel surfing or frequency hopping, demands excessive computing resources, which are infeasible on the low cost resource constraint of the electrical devices in the Smart Grid wireless mesh networks. Even these are inadequate protect approaches to the control systems where the availability is the major security priority to achieve. To overcome the problems for normal lower computation power electrical devices in the Smart Grid wireless mesh networks with difference security triad from the Internet, we propose an efficient localized jamming-resistant countermeasure against the jamming attacks by the identification of trigger nodes whose wireless signal invokes the jammer in the grid. By constraining the trigger nodes to be receivers only, we can avoid the activation of the jammers and completely nullify the reactive jamming attack. The triggers identification approach utilizes a hexagon tiling coloring and sequential Group Testing (GT), which does not demand any sophisticated hardware. Theoretical analyses and simulation results endorse the suitability of our localized algorithm in terms of message and time complexity

NMN-VD: A Neural Module Network for Visual Dialog

Visual dialog demonstrates several important aspects of multimodal artificial intelligence; however, it is hindered by visual grounding and visual coreference resolution problems. To overcome these problems, we propose the novel neural module network for visual dialog (NMN-VD). NMN-VD is an efficient question-customized modular network model that combines only the modules required for deciding answers after analyzing input questions. In particular, the model includes a Refer module that effectively finds the visual area indicated by a pronoun using a reference pool to solve a visual coreference resolution problem, which is an important challenge in visual dialog. In addition, the proposed NMN-VD model includes a method for distinguishing and handling impersonal pronouns that do not require visual coreference resolution from general pronouns. Furthermore, a new Compare module that effectively handles comparison questions found in visual dialogs is included in the model, as well as a Find module that applies a triple-attention mechanism to solve visual grounding problems between the question and the image. The results of various experiments conducted using a set of large-scale benchmark data verify the efficacy and high performance of our proposed NMN-VD model

NMN-VD: A Neural Module Network for Visual Dialog

Visual dialog demonstrates several important aspects of multimodal artificial intelligence; however, it is hindered by visual grounding and visual coreference resolution problems. To overcome these problems, we propose the novel neural module network for visual dialog (NMN-VD). NMN-VD is an efficient question-customized modular network model that combines only the modules required for deciding answers after analyzing input questions. In particular, the model includes a Refer module that effectively finds the visual area indicated by a pronoun using a reference pool to solve a visual coreference resolution problem, which is an important challenge in visual dialog. In addition, the proposed NMN-VD model includes a method for distinguishing and handling impersonal pronouns that do not require visual coreference resolution from general pronouns. Furthermore, a new Compare module that effectively handles comparison questions found in visual dialogs is included in the model, as well as a Find module that applies a triple-attention mechanism to solve visual grounding problems between the question and the image. The results of various experiments conducted using a set of large-scale benchmark data verify the efficacy and high performance of our proposed NMN-VD model

Design and Implementation of Embedded-Based Vein Image Processing System with Enhanced Denoising Capabilities

In general, it is very difficult to visually locate blood vessels for intravenous injection or surgery. In addition, if vein detection fails, physical and mental pain occurs to the patient and leads to financial loss in the hospital. In order to prevent this problem, NIR-based vein detection technology is developing. The proposed study combines vein detection and digital hair removal to eliminate body hair, a noise that hinders the accuracy of detection, improving the performance of the entire algorithm by about 10.38% over existing systems. In addition, as a result of performing venous detection of patients without body hair, 5.04% higher performance than the existing system was detected, and the proposed study results were verified. It is expected that the use of devices to which the proposed study is applied will provide more accurate vascular maps in general situations

Consent for withholding life-sustaining treatment in cancer patients: a retrospective comparative analysis before and after the enforcement of the Life Extension Medical Decision law

Abstract Background The Life Extension Medical Decision law enacted on February 4, 2018 in South Korea was the first to consider the suspension of futile life-sustaining treatment, and its enactment caused a big controversy in Korean society. However, no study has evaluated whether the actual implementation of life-sustaining treatment has decreased after the enforcement of this law. This study aimed to compare the provision of patient consent before and after the enforcement of this law among cancer patients who visited a tertiary university hospital's emergency room to understand the effects of this law on the clinical care of cancer patients. Methods This retrospective single cohort study included advanced cancer patients aged over 19 years who visited the emergency room of a tertiary university hospital. The two study periods were as follows: from February 2017 to January 2018 (before) and from May 2018 to April 2019 (after). The primary outcome was the length of hospital stay. The consent rates to perform cardiopulmonary resuscitation (CPR), intubation, continuous renal replacement therapy (CRRT), and intensive care unit (ICU) admission were the secondary outcomes. Results The length of hospital stay decreased after the law was enforced from 4 to 2 days (p = 0.001). The rates of direct transfers to secondary hospitals and nursing hospitals increased from 8.2 to 21.2% (p = 0.001) and from 1.0 to 9.7%, respectively (p < 0.001). The consent rate for admission to the ICU decreased from 6.7 to 2.3% (p = 0.032). For CPR and CRRT, the consent rates decreased from 1.0 to 0.0% and from 13.9 to 8.8%, respectively, but the differences were not significant (p = 0.226 and p = 0.109, respectively). Conclusion After the enforcement of the Life Extension Medical Decision law, the length of stay in the tertiary university hospital decreased in patients who established their life-sustaining treatment plans in the emergency room. Moreover, the rate of consent for ICU admission decreased

Micropatterning of metal oxide nanofibers by electrohydrodynamic (EHD) printing towards highly integrated and multiplexed gas sensor applications

Integration of heterogeneous sensing materials in microelectronic devices is essential to accomplish compact and highly integrated environmental sensors. For this purpose, a micro-patterning method of electrospun metal oxide nanofibers based on electrohydrodynamic (EHD) printing process was developed in this work. Several types of metal oxide (SnO_2, In_2O_3, WO_3 and NiO) nanofibers that were produced by electrospinning, fragmented into smaller pieces by ultrasonication, and dissolved in organic solvents were utilized as inks for the printing. Constant or pulsed wave bias consisting of base and jetting voltages were applied between a nozzle and a substrate to generate a jetting of nanofiber solutions. Several parameters for EHD printing such as pulse width, inner diameter of the nozzle, distance from the nozzle to the substrate, and stage speed, were optimized for accurate micro-patterning of electrospun nanofibers. By using optimized printing parameters, microscale patterns of electrospun nanofibers with a minimum diameter less than 50 μm could be realized. Gas sensors were fabricated by EHD printing on the microelectrodes and then used for the detection of toxic gases such as NO_2, CO and H_2S. Four kinds of metal oxides could detect down to 0.1 ppm of NO_2, 1 ppm of H_2S and 20 ppm of CO gases. Also, heterogeneous nanofiber gas sensor array was fabricated by the same printing method and could detect NO_2 using the sensor array platform with microheaters. Furthermore, microscale patterns of nanofibers by EHD printing could be applied to the suspended MEMS platform without any structural damage and this sensor array could detect NO_2 and H_2S gases with 20 mW power consumption

Localized Liquid-Phase Synthesis of Porous SnO₂ Nanotubes on MEMS Platform for Low-Power, High Performance Gas Sensors

We have developed highly sensitive, low-power gas sensors through the novel integration method of porous SnO₂ nanotubes (NTs) on a micro-electro-mechanical-systems (MEMS) platform. As a template material, ZnO nanowires (NWs) were directly synthesized on beam-shaped, suspended microheaters through an in situ localized hydrothermal reaction induced by local thermal energy around the Joule-heated area. Also, the liquid-phase deposition process enabled the formation of a porous SnO₂ thin film on the surface of ZnO NWs and simultaneous etching of the ZnO core, eventually to generate porous SnO₂ NTs. Because of the localized synthesis of SnO₂ NTs on the suspended microheater, very low power for the gas sensor operation (<6 mW) has been realized. Moreover, the sensing performance (e.g., sensitivity and response time) of synthesized SnO₂ NTs was dramatically enhanced compared to that of ZnO NWs. In addition, the sensing performance was further improved by forming SnO₂–ZnO hybrid nanostructures due to the heterojunction effect