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Though innovation is essential to achieve competitiveness in the medical device industry, it is difficult for most firms in developing countries to invest sufficient resources in research and development (R&D) activities due to their small firm size. Therefore, it is necessary to evaluate a company’s R&D performance based on the R&D efficiency, which is R&D output to input, rather than the output itself. Although medical device development (MDD) process in the medical device industry is divided into several stages, moreover, the impact of R&D activities in each MDD stage on R&D performance is still unanswered. This study verifies the difference in R&D efficiency according to three business types: both manufacturing and import, manufacturing only, and import only. The effect of the R&D activities in each MDD phase on R&D efficiency is also verified. The results prove that import-only companies tend to achieve higher level of R&D efficiency than firms engaging in manufacturing-only or both manufacturing and import. However, the difference in R&D efficiency between manufacturing-only companies and both manufacturing and import companies has not been verified. Furthermore, it is also verified that the impact of investment in each MDD stage on R&D efficiency varies depending on business types.

For next-generation Internet-of-Things (IoT) networks, asynchronous instant transmission has attracted increasing research interest with the expectation of achieving near-zero latency without excessive initiation procedure. However, in an asynchronous multiple-access scenario, there exist significant inter-carrier interference between sub-carriers allocated to different users. To suppress out-of-band emission (OOBE) of each sub-carrier, a new generalized frequency division multiplexing (GFDM) has been proposed, which has lower OOBE than the conventional orthogonal frequency division multiplexing (OFDM). In this paper, by using GFDM, two types of receivers are proposed with the aim of reducing latency and improving throughput: a GFDM-based minimum mean square error (MMSE) receiver and a GFDM-based MMSE-successive interference cancellation (SIC) receiver. Then, we develop a lightweight scheme using an $\epsilon $ -conservative rate control with GFDM-based MMSE receivers and also invent a performance-focused scheme using an advanced rate control with GFDM-based MMSE-SIC receivers. In particular, the latter scheme provides higher throughput with limited increase in computational load of user equipments. Numerical results show that with a high successful transmission probability higher than 99 %, the performance-focused scheme and the lightweight scheme achieve up to 85 % and up to 70 % higher throughput compared to the conventional OFDM-based asynchronous multiple-access scheme, respectively. Furthermore, since our proposal does not require any centralized user scheduling or initiation procedure, it presents a significant reduction in latency compared to the existing low-latency technologies.

In this paper, we develop a privacy-preserving UAV system that does not infringe on the privacy of people in the videos taken by UAVs. Instead of blurring or masking the face parts of the videos, we want to exquisitely modify only the face parts so that the people in the modified videos still look like humans, but they become anonymous. Doing so, the semantic information of the videos can be preserved even with the anonymization. Specifically, based on the latest generative adversarial network architecture, we propose a deep learning-based face-anonymization scheme so that each modified face part looks like the face of a person who does not actually exist. The trained face-anonymizer is then mounted on the UAV system we have implemented. Through experiments, we confirm that the developed privacy-preserving UAV system anonymizes UAV’s first-person videos so that the people in the video are not recognized as anyone in the dataset used. In addition, we show that even with such anonymized videos, the perception performance required for performing UAV’s essential functions such as simultaneous localization and mapping is not degraded.

This study aims to improve the sensitivity of sensors using porous structures and cone-shaped patterns in the composition of a capacitive pressure sensor. A simple and rapid fabrication method for the production of porous structures and cone-shaped patterns using microwave irradiation of emulsions containing polydimethylsiloxane (PDMS) and a sacrificial solvent is introduced in this study. Through this method, a porous PDMS dielectric layer could be simply fabricated within a few minutes. The sensitivity was greatly improved by both enhancing deformability and increasing the dielectric constant under the external pressure. The effect of pattern distance was investigated, and the sensor with the pattern distance of 600 μm showed a remarkably high sensitivity of approximately 5 kPa−1. Moreover, the effect of aspect ratio and sharpness of the patterns were also studied through a finite element analysis. Finally, we demonstrated the performance of the sensor using a sensor array and finger attached sensors, and they showed sufficient performance for use in wearable devices applicable to artificial skin, surgical robots, and pressure monitoring systems, among others.

In a disaster site, terrestrial communication infrastructures are often destroyed or malfunctioning, and hence it is very difficult to detect the existence of survivors in the site. At such sites, UAVs are rapidly emerging as an alternative to mobile base stations to establish temporary infrastructure. In this paper, a novel deep-learning-based multi-source detection scheme is proposed for the scenario in which an UAV wants to estimate the number of survivors sending rescue signals within its coverage in a disaster site. For practicality, survivors are assumed to use off-the-shelf smartphones to send rescue signals, and hence the transmitted signals are orthogonal frequency division multiplexing (OFDM)-modulated. Since the line of sight between the UAV and survivors cannot be generally secured, the sensing performance of existing radar techniques significantly deteriorates. Furthermore, we discover that transmitted signals of survivors are unavoidably aysnchronized to each other, and thus existing frequency-domain multi-source classification approaches cannot work. To overcome the limitations of these existing technologies, we propose a lightweight deep-learning-based multi-source detection scheme by carefully designing neural network architecture, input and output signals, and a training method. Extensive numerical simulations show that the proposed scheme outperforms existing methods for various SNRs under the scenario where synchronous and asynchronous transmission is mixed in a received signal. For almost all cases, the precision and recall of the proposed scheme is nearly one, even when users’ signal-to-noise ratios (SNRs) are randomly changing within a certain range. The precision and recall are improved up to 100% compared to existing methods, confirming that the proposal overcomes the limitation of the existing works due to the asynchronicity. Moreover, for Intel(R) Core(TM) i7-6900K CPU, the processing time of our proposal for a case is 31.8 milliseconds. As a result, the proposed scheme provides a robust and reliable detection performance with fast processing time. This proposal can also be applied to any field that needs to detect the number of wireless signals in a scenario where synchronization between wireless signals is not guaranteed.

In recent years, innovative research has been widely conducted on flexible devices for wearable electronics applications. Many examples of wearable electronics, such as smartwatches and glasses, are already available to consumers. However, strictly speaking, the sensors used in these devices are not flexible. Many studies are underway to address a wider range of wearable electronics and the development of related fields is progressing very rapidly. In particular, there is intense interest in the research field of flexible pressure sensors because they can collect and use information regarding a wide variety of sources. Through the combination of novel materials and fabrication methods, human-machine interfaces, biomedical sensors, and motion detection techniques, it is now possible to produce sensors with a superior level of performance to meet the demands of wearable electronics. In addition, more compact and human-friendly sensors have been invented in recent years, as biodegradable and self-powered sensor systems have been studied. In this review, a comprehensive description of flexible pressure sensors will be covered, and design strategies that meet the needs for applications in wearable electronics will be presented. Moreover, we will cover several fabrication methods to implement these technologies and the corresponding real-world applications.

Highly sensitive capacitive pressure sensors with poly(vinylidenefluoride-co-trifluoroethylene) (P(VDF-TrFE)) dielectric layers were prepared. The dielectric layers were directly produced by electrospinning P(VDF-TrFE) nanofibers for various spinning times. A longer spinning time enhanced the deformability of the electrospun P(VDF-TrFE) layers, resulting in higher sensitivity owing to larger changes in the deformation of the dielectric layer. One of the capacitive pressure sensors showed a high sensitivity of 2.81 kPa-1 at a pressure ≤0.12 kPa, a good response time of 42 ms, and small hysteresis. The sensitivity of the sensor was five times higher than that of a typical capacitive pressure sensor. The fabricated pressure sensor could detect a tiny water droplet as light as 7mg. It is expected that the electrospun P(VDF-TrFE) nanofibers can be used as sensing materials for highly sensitive pressure sensors in wearable electronics applications. [ABSTRACT FROM AUTHOR]

In this study, a honeycomb-like nanofiber based triboelectric nanogenerator (HN-TENG) is presented. In order to fabricate the honeycomb-like nanofiber, we utilized self-assembly of electrospun poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) nanofibers. The honeycomb-like P(VDF-TrFE) nanofiber network was directly produced via electrospinning without any additional processing. The HN-TENG showed a maximum voltage, current, and power density of 160 V, 17 µA, and 1.6W/m2, respectively. The power density was enhanced more than twofold as compared with a typical flat nanofiber network based TENG due to the large surface area and high surface roughness of the honeycomb structure. Finally, we verified that HN-TENG has the potential to be used for practical applications by driving 100 light emitting diodes and charging capacitors. [ABSTRACT FROM AUTHOR]

The effect of the needle shape on electrohydrodynamic (EHD) printing was investigated by comparing flat outlet needles and hypodermic needles. Line fabrication was performed to confirm the tendency of jetting stability and the printed line width with various driving voltage and stage speed by using Ag nanoparticle ink as a jetting solution on a hydrophobic surface. We verified that the hypodermic needle greatly improves the resolution in EHD printing. The ink slips down the inner wall of the hypodermic needle, and a very small meniscus is generated at the tip of the needle. Due to this phenomenon, high-resolution printing can be accomplished. The narrowest line that was fabricated using a hypodermic needle has a line width of 0.7 im, and it is smaller than 1% of the needle inner diameter. [ABSTRACT FROM AUTHOR]