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This paper proposes a real-time sensing and on-site spotting scheme for multi-type wireless LAN based spy-cameras. The proposed scheme has the goal of overcoming the restrictions of previous studies on location-dependent training data per target location for spycam traffic classification and learning-based positioning, and the overhead requiring additional equipment to gather specific data for spycam location such as channel state information (CSI). In addition, some ways to send video data via spycams exploit data encryption, so it leads to erasing feature of video data which should be trained to the models for camera existence detection. In other words, there is a lack of comprehensive research that integrates the detection and localization of these surveillance video devices without reliance on data preparation per location and special devices. Recognizing the need to detect video devices used for such malicious purposes in real-time, this paper introduces a method that utilizes the Nilsimsa hashing algorithm and the Log-Distance Path Loss (LDPL) model for similarity checks to detect and localize various video devices transmitting wirelessly. We capture the wireless data transmitted and use the Nilsimsa hashing algorithm, which generates similar hash results for similar inputs, to detect video devices through similarity checks. Furthermore, we rely only on the recent signal strength indication (RSSI) values received to quickly estimate the location of the device that is not very accurate but detectable with eyes at the site, regardless of its type. Our approach shows determining the presence of a device within 3 seconds and estimating its approximate location with an average accuracy of approximately $0.08~{m}$ within a $1~{m}$ range and $0.45~{m}$ within a $3~{m}$ range, regardless of the device type.

In many disaster situations, the location of victims is the most important information for saving them. To provide such information, most studies simply use indoor positioning technologies of the victim’s mobile device. Nevertheless, their schemes have a large lack to point out the exact location of a victim because people sometimes drop off their mobile device or put it down some places, such as a desk and a table. In other words, it merely provides the location of mobile devices and not of victims. Hence, this paper proposes a novel virtual sensing mechanism, named the USING sensor (User-Smartphone-Decoupling Detection). The USING sensor relies on the user activity sensing and co-existence monitoring of users and smart mobile devices. It could track the continuous coupling state of the user-mobile device. However, continuous user activity detection is not trivial, since some actions, such as sitting, dropping, and so on, intermittently occur while some actions like walking are frequent and continuous. So, data acquisition methods from a mobile device for the detection of user actions should be designed and managed differently. In addition, for high sensing reliability, the USING sensor is based on machine learning (ML) analytics with multiple algorithms and the ensemble technique. Thus, it could achieve 95.0% of user activity recognition accuracy and 86.8% of sensing accuracy.

This paper introduces a novel method to detect WiFi-enabled spy cameras potentially invading user privacy via wireless streaming within a user’s vicinity. So far, there have been social initiatives to conduct spy camera inspections to address this issue, and academic societies have been working on various detection methods. However, such studies typically rely on additional specific efforts, such as using specialized equipment to gather channel state information (CSI) or collecting extensive long-term data per location for machine learning approaches. In addition, some ideas could not recognize features of spy camera data due to data encryption. Hence, this paper comes up with a novel scheme for detecting wireless spy cameras based only on off-the-shelf devices. To achieve these functionalities, the proposed scheme consists of three phases: 1) classification, 2) detection, and 3) localization. First, in the classification phase, the collected packet headers are analyzed to identify and classify the upstream streaming protocols. In addition, the proposed scheme fulfills the detection phase that exploits the Nilsimsa algorithm to analyze packet similarity, identifying potential spy cameras by detecting consistent high similarity patterns in the classified data traffic. Finally, the proposed approach provides the localization phase that captures and analyzes Received Signal Strength Indication (RSSI) values at the target location and spots the camera position. Unlike existing methods, the proposed scheme does not rely on extensive data collection per location and complex mechanisms to interpret encrypted data. Moreover, compared to machine learning (ML)-based positioning means, which require significant data sets and long-term computation time, the scheme achieves real-time detection with minimal computational demands.

Nowadays, mobile–mobile interaction is becoming a fundamental methodology for human–human networking services since mobile devices are the most common interfacing equipment for recent smart services such as food delivery, e-commerce, ride-hailing, etc. Unlike legacy ways of human interaction, on-site and in-person mutual recognition between a service provider and a client in mobile–mobile interaction is not trivial. This is because of not only the avoidance of face-to-face communication due to safety and health concerns but also the difficulty of matching up the online user using mobiles with the real person in the physical world. So, a novel mutual recognition scheme for mobile–mobile interaction is highly necessary. This paper comes up with a novel cyber-physical secure communication scheme relying on the digital twin paradigm. The proposed scheme designs the digital twin networking architecture on which real-world users form digital twins as their own online abstraction, and the digital twins authenticate each other for a smart service interaction. Thus, inter-twin communication (ITC) could support secure mutual recognition in mobile–mobile interaction. Such cyber-physical authentication (CPA) with the ITC is built on the dynamic BLE beaconing scheme with accurate proximity detection and dynamic identifier (ID) allocation. To achieve high accuracy in proximity detection, the proposed scheme is conducted using a wide variety of data pre-processing algorithms, machine learning technologies, and ensemble techniques. A location-dependent ID exploited in the CPA is dynamically generated by the physical user for their own digital twin per each mobile service.

Multisystem inflammatory syndrome in children (MIS-C) is a rare but life-threatening hyperinflammatory condition induced by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes pediatric COVID-19 (pCOVID-19). The relationship of the systemic tissue injury to the pathophysiology of MIS-C is poorly defined. We leveraged the high sensitivity of epigenomics analyses of plasma cell-free DNA (cfDNA) and plasma cytokine measurements to identify the spectrum of tissue injury and glean mechanistic insights. Compared with pediatric healthy controls (pHCs) and patients with pCOVID-19, patients with MIS-C had higher levels of cfDNA primarily derived from innate immune cells, megakaryocyte-erythroid precursor cells, and nonhematopoietic tissues such as hepatocytes, cardiac myocytes, and kidney cells. Nonhematopoietic tissue cfDNA levels demonstrated significant interindividual variability, consistent with the heterogenous clinical presentation of MIS-C. In contrast, adaptive immune cell–derived cfDNA levels were comparable in MIS-C and pCOVID-19 patients. Indeed, the cfDNA of innate immune cells in patients with MIS-C correlated with the levels of innate immune inflammatory cytokines and nonhematopoietic tissue–derived cfDNA, suggesting a primarily innate immunity–mediated response to account for the multisystem pathology. These data provide insight into the pathogenesis of MIS-C and support the value of cfDNA as a sensitive biomarker to map tissue injury in MIS-C and likely other multiorgan inflammatory conditions.

The proliferation of IoT comes with many challenges, among which security is one of the most serious issues. Since most IoT devices are designed based on bare-metal devices with low performance, low power, and small form factor, an attacker can easily perform firmware extraction and analysis. In addition, code reuse attacks, such as Return Oriented Programming (ROP) attacks, are possible based on the analyzed firmware information. Since most code reuse attacks are performed through a combination of gadgets that include return instructions, preventing an attacker from analyzing return instructions can be a fundamental solution for these attacks. To prevent code reuse attacks on bare-metal based IoT devices, this paper proposes Return Instruction Obfuscation (RIO). The proposed scheme encrypts all return instructions in the firmware and instruments the modules necessary to decrypt and execute the encrypted return instructions using a Low Level Virtual Machine (LLVM). Since all return instructions in the firmware are encrypted, the proposed scheme can prevent attackers from performing firmware analysis and gadget collection. The proposed scheme was implemented and evaluated on Nuvoton’s NuMaker-PFM-M2351 development board with an ARM Cortex-M23 based SoC.

Commonly, large π-conjugated systems facilitate low-energy electronic transitions. Here, the authors demonstrate that the relief of excited-state antiaromaticity of the benzene core leads to large Stokes shifts, and allows the construction of emitters covering the entire visible spectrum without the need of extending π-conjugation.

Recently, research has been actively conducted to overcome various challenges observed in wireless power transfer (WPT) technology. However, as additional techniques are implemented, the complexity and cost of WPT systems increased. Therefore, there is a need for a new WPT method that can overcome the shortcomings of existing technology. This paper presents a metal surface guided-wireless power transfer (MSG-WPT) system for applications with multiple receivers (Rxs). First, a plate-wire propagation enhancer (PWPE) is shown to improve the power transfer efficiency (PTE). However, it is not suitable for portable receiving systems. Based on our analysis of the PWPE characteristics, applying an L-section lumped impedance-matching scheme instead of a PWPE can improve the portability of the Rx. A coil-based propagation enhancer (CPE), or a coil-based inductor (CI), can increase the power received if it replaces the lumped inductor in the L-section scheme. The MSG-WPT system does not experience the co-alignment issues that exist between a pair of transceivers (TRxs) in conventional WPT systems. It can also support multiple Rxs while ensuring that the PTE of each Rx is relatively unaffected. An electromagnetic simulation of the proposed MSG-WPT system is performed using a high-frequency structure simulator (HFSS), and measurements are conducted in a corresponding experimental environment. The system power efficiency is measured at −12 dB in the 4 MHz frequency band. The proposed MSG-WPT system with a CPE is adequately efficient and portable, while also allowing a more liberal arrangement of Rxs compared to other conventional WPT systems.

Recently, the interest in the lower-limb exoskeleton robot is increasing within rehabilitation practice. While research on advancing the gait rehabilitation exoskeleton robot (ER) is being actively conducted from a technical point of view, studies evaluating usability and user value of ERs are insufficient. In this study, an evaluation tool was developed and verified, and an evaluation of an ER-based rehabilitation training program was conducted using the evaluation tool. A total of 102 rehabilitation patients (51 using ER and 51 under the existing training program [ETP] without robots) and 38 therapists participated in this experiment. The ER used in this study was Exowalk (HR-02, HMH Co. Ltd, South Korea). As a result of the study, the usability of the ERs consists of trust and overall satisfaction. The patient group using the ER showed statistically higher trust and overall satisfaction scores than the patient group with the ETP. On the other hand, the user value of the ER includes self-direction and security. The self-direction score of the patients using the ER was significantly higher than that of the patients with the ETP, but the security score showed the opposite tendency. This study is unique in that measures were developed to evaluate the usability and user value of the gait rehabilitation ER, especially for the Exowalk, verified by various user groups, and statistically analyzed to be used as a reference for the design of usability and user value in other gait rehabilitation ERs.

The prevalence of dry eye syndrome (DES) has rapidly increased in recent years, negatively affecting the eye health of many office workers worldwide. Although low eye blink rate (EBR) has been pointed out as one of the main risk factors for DES, it is difficult for office workers to continuously monitor and increase their own involuntary blinking, especially when they are focused on the primary work task. Thus, as an effort to help office workers correct their low EBR, the current study developed a real-time EBR level classification system utilizing sitting postural behavior data. A total of twenty participants performed typical computer tasks on a sensor-embedded chair. The participants’ eye blinking and postural behavior data were collected to develop the EBR level classification system with a random forest algorithm. After evaluating the system performance, the relationships between EBR and postural behaviors were empirically examined to help understand how the system worked for EBR level classification. As a result, the developed system showed high classification performance overall; and compared with high EBR condition, low EBR condition was related to less overall postural variability and greater extent of forward bending posture. The real-time EBR level classification system is expected to contribute to preventing/relieving DES and thereby enhancing the eye health of office workers.

Malate dehydrogenase (MDH) is one of the key enzymes for succinic acid (SA) bioproduction. Here, the authors report biochemical and structural analyses of various MDHs to reveal amino acids influencing the specific activity and susceptibility to substrate inhibition, and achieve industrial-level SA production.

While multiple previous studies have proposed utilizing an ambient display for providing real-time posture feedback to seated computer workers, it is not well understood how effective ambient feedback is in supporting the users' dual task of computer work and posture monitoring/rectification. The objective of the current study was to evaluate an ambient display for real-time posture feedback in comparison with typical conditions, an on-screen display and no display condition, in terms of the dual task performance. The ambient and the on-screen display were also compared in terms of perceived detection efficiency and user acceptance measures. A total of 24 participants performed the dual task in each of the three display conditions (the ambient, on-screen and no display conditions). The dependent measures for the dual task performance were the number of typed answers and the occurrence rate of high-risk postures; and, for the subjective evaluation, two detection efficiency measures (visibility and understandability) and four user acceptance measures (performance expectancy, effort expectancy, attitude toward using technology and social influence) were employed. The study demonstrated that both the ambient and the on-screen displays enhanced the dual task performance with respect to the no-display condition and the ambient display was superior to the on-screen display in the subjective experience measures.

Abstract Diverse artificial synapse structures and materials are widely proposed for neuromorphic hardware systems beyond von Neumann architecture owing to their capability to mimic complex information processing tasks such as image recognition, natural language processing, and learning. Nevertheless, temporal and spatial randomness in the movement of ion and electron particles that exist in materials usually prevents the solid‐state‐based synaptic devices from enabling the reliable modulation of synaptic plasticity. An aluminum nanoparticle (Al NP)‐embedded indium gallium zinc oxide (IGZO) synaptic transistor whose spike peak level and conductance change can be precisely modulated by the density of Al NPs within the IGZO channel is demonstrated. Essential synaptic functions including excitatory or inhibitory postsynaptic current, paired pulse facilitation, and short‐term potentiation or depression are also thoroughly emulated in the synaptic transistor device with the most optimized Al NP density: IGZO:Al NPs (6 nm). Moreover, controllable switching from short‐term to long‐term memory regimes essential for a learning task is demonstrated. Simulation results prove that this transistor can provide a decent recognition accuracy for neuromorphic computing. Indeed, the integrated IGZO:Al NP synaptic circuit with the effective synaptic plasticity will facilitate the implementation of a reconfigurable neuromorphic computing system.

The transition metal dichalcogenides (TMDCs) have been extensively investigated for various applications such as logic, memory and optical devices, and sensors. The pressing challenge in the research of TMDCs is the electrical performance limited by the high contact resistance. We report more than 5000 times reduction in the contact resistance of WS2 field-effect transistor (FET) with Ti contact (81 MΩ μm to 14.6 kΩ μm) by high-pressure hydrogen annealing. Schottky barrier height reduction appears to play a major role in the reduction of the contact resistance. This process can be used to reduce the contact resistance even further by combining with a doping technique for TMDCs and a contact metal optimization for TMDC FETs.

A new architecture of tunnel field effect transistor (TFET) with in-line (vertical) tunneling area is introduced. By adding the vertical tunneling area, the in-line TFET architecture outperformed the normal TFET in terms of the drive current, the subthreshold swing, and the intrinsic time delay, etc. The drive current of the in-line TFET is enhanced nearly 7× compared to the conventional TFET. It also shows a significantly reduced subthreshold swing of 37.2 mV/dec.

Despite the long history of automotive head-up displays (HUD) systems and their increasing importance in the human-vehicle interface design, a question still remains about what they should display to best serve the driver. As an effort to contribute to the ergonomics design of automotive HUD systems, this paper determined the perceived importance of different HUD information items-fifty-one drivers with significant prior HUD use experience subjectively evaluated twenty-two information items in their importance. The information items were those displayed by existing commercial HUD systems. The results indicated that the information items varied greatly in perceived importance, and current speed, speed limit, turn-by-turn navigation instructions, maintenance warning, cruise control status, and low fuel warning were of highest importance. Also, participants' prior experience of using an information item was found to greatly impact the average importance rating, suggesting that information items' importance must be evaluated by those with sufficient information item use experience.

The fifth generation (5G) of mobile communication envisions ultralow latency less than 1 ms for radio interface. To this end, frameless asynchronous multiple access may be needed to allow users to transmit instantly without waiting for the next frame start. In this paper, generalized frequency division multiple-access (GFDMA), one of the promising multiple-access candidates for 5G mobile, is compared with the conventional single-carrier FDMA (SC-FDMA) in terms of the uplink sum rate when both techniques are adapted for the asynchronous scenario. In particular, a waveform windowing technique is applied to both schemes to mitigate the inter-user interference due to non-zero out-of-band emission.

Highly dispersive molybdenum disulfide nanoflakes (MoS2 NFs), without any phase transition during the exfoliation process, are desirable for full utilization of their semiconductor properties in practical applications. Here, we demonstrate an innovate approach for fabricating MoS2 NFs by using hydrazine-assisted ball milling via the synergetic effect of chemical intercalation and mechanical exfoliation. The NFs obtained have a lateral size of 600–800 nm, a thickness less than 3 nm, and high crystallinity in the 2H semiconducting phase. They form a stable dispersion in various solvents, which will be helpful for many applications, due to the oxygen functional group. To investigate production of a two-dimensional (2D) photodetector, 2D semiconducting MoS2, MoS2–p-Si vertical devices were fabricated, and their optical properties were characterized. The photodiode exhibited consistent responses with excellent photo-switching characteristics with wavelengths of 850, 530, and 400 nm.

Despite extensive investigations of a wide variety of artificial synapse devices aimed at realizing a neuromorphic hardware system, the identification of a physical parameter that modulates synaptic plasticity is still required. In this context, a novel two-dimensional architecture consisting of a NbSe2/WSe2/Nb2O5 heterostructure placed on an SiO2/p+ Si substrate was designed to overcome the limitations of the conventional silicon-based complementary metal-oxide semiconductor technology. NbSe2, WSe2, and Nb2O5 were used as the metal electrode, active channel, and conductance-modulating layer, respectively. Interestingly, it was found that the post-synaptic current was successfully modulated by the thickness of the interlayer Nb2O5, with a thicker interlayer inducing a higher synapse spike current and a stronger interaction in the sequential pulse mode. Introduction of the Nb2O5 interlayer can facilitate the realization of reliable and controllable synaptic devices for brain-inspired integrated neuromorphic systems.

Bluetooth Low Energy (BLE) is a short-range wireless communication technology aiming at low-cost and low-power communication. The performance evaluation of classical Bluetooth device discovery have been intensively studied using analytical modeling and simulative methods, but these techniques are not applicable to BLE, since BLE has a fundamental change in the design of the discovery mechanism, including the usage of three advertising channels. Recently, there several works have analyzed the topic of BLE device discovery, but these studies are still far from thorough. It is thus necessary to develop a new, accurate model for the BLE discovery process. In particular, the wide range settings of the parameters introduce lots of potential for BLE devices to customize their discovery performance. This motivates our study of modeling the BLE discovery process and performing intensive simulation. This paper is focused on building an analytical model to investigate the discovery probability, as well as the expected discovery latency, which are then validated via extensive experiments. Our analysis considers both continuous and discontinuous scanning modes. We analyze the sensitivity of these performance metrics to parameter settings to quantitatively examine to what extent parameters influence the performance metric of the discovery processes.

Evaluating physical discomfort of designed gestures is important for creating safe and usable gesture-based interaction systems; yet, gestural discomfort evaluation has not been extensively studied in HCI, and few evaluation methods seem currently available whose utility has been experimentally confirmed. To address this, this study empirically demonstrated the utility of the subjective rating method after a small number of gesture repetitions (a maximum of four repetitions) in evaluating designed gestures in terms of physical discomfort resulting from prolonged, repetitive gesture use. The subjective rating method has been widely used in previous gesture studies but without empirical evidence on its utility. This study also proposed a gesture discomfort evaluation method based on an existing ergonomics posture evaluation tool (Rapid Upper Limb Assessment) and demonstrated its utility in evaluating designed gestures in terms of physical discomfort resulting from prolonged, repetitive gesture use. Rapid Upper Limb Assessment is an ergonomics postural analysis tool that quantifies the work-related musculoskeletal disorders risks for manual tasks, and has been hypothesized to be capable of correctly determining discomfort resulting from prolonged, repetitive gesture use. The two methods were evaluated through comparisons against a baseline method involving discomfort rating after actual prolonged, repetitive gesture use. Correlation analyses indicated that both methods were in good agreement with the baseline. The methods proposed in this study seem useful for predicting discomfort resulting from prolonged, repetitive gesture use, and are expected to help interaction designers create safe and usable gesture-based interaction systems.

The fermi-level pinning phenomenon, which occurs at the metal−semiconductor interface, not only obstructs the achievement of high-performance field effect transistors (FETs) but also results in poor long-term stability. This paper reports on the improvement in gate-bias stress stability in two-dimensional (2D) transition metal dichalcogenide (TMD) FETs with a titanium dioxide (TiO2) interfacial layer inserted between the 2D TMDs (MoS2 or WS2) and metal electrodes. Compared to the control MoS2, the device without the TiO2 layer, the TiO2 interfacial layer deposited on 2D TMDs could lead to more effective carrier modulation by simply changing the contact metal, thereby improving the performance of the Schottky-barrier-modulated FET device. The TiO2 layer could also suppress the Fermi-level pinning phenomenon usually fixed to the metal−semiconductor interface, resulting in an improvement in transistor performance. Especially, the introduction of the TiO2 layer contributed to achieving stable device performance. Threshold voltage variation of MoS2 and WS2 FETs with the TiO2 interfacial layer was ~2 V and ~3.6 V, respectively. The theoretical result of the density function theory validated that mid-gap energy states created within the bandgap of 2D MoS2 can cause a doping effect. The simple approach of introducing a thin interfacial oxide layer offers a promising way toward the implementation of high-performance 2D TMD-based logic circuits.

This paper proposes a method to detect corresponding vertex pairs between planar tessellation datasets. Applying an agglomerative hierarchical co-clustering, the method finds geometrically corresponding cell-set pairs from which corresponding vertex pairs are detected. Then, the map transformation is performed with the vertex pairs. Since these pairs are independently detected for each corresponding cell-set pairs, the method presents improved matching performance regardless of locally uneven positional discrepancies between dataset. The proposed method was applied to complicated synthetic cell datasets assumed as a cadastral map and a topographical map, and showed an improved result with the F-measures of 0.84 comparing to a previous matching method with the F-measure of 0.48.

This paper presents a simple total power radiometer to noninvasively measure the temperature of the human body. The proposed 3-GHz radiometer consists of an antenna collecting the noise power generated by a target, a low-noise and high-gain receiver amplifying the noise power, and a detector converting the noise power to voltage. A single-pole-triple-throw (SP3T) switch is placed between the antenna and the receiver, while a personal computer is used to control the SP3T switch, collect and process the data such as detector output voltages and physical temperatures of the reference noise sources and the target. The fabricated radiometer shows a good performance agreement with a thermometer in the temperature measurement of water from 25.0 to 43.1 °C. For the accurate prediction of the target temperature, the radiometer is calibrated adaptively to the environment and radiometer variations. For this purpose, two reference noise sources (hot and cold) are proposed using matched and mismatched resistors at room temperature. These resistor-based noise sources offer a reliable performance without complex temperature control systems. Furthermore, they can be easily calibrated in real time by periodically measuring the physical temperatures of the resistors. In addition, the logarithmic detector with wide dynamic range is adopted and logarithmically-fitted based on the measurement results instead of linear approximation, which reduces the error caused by the limited dynamic range of resistor-based noise sources. In order to further increase the accuracy, the performance imbalances between ports in the SP3T switch are also taken into account by employing offsets in the radiometer output voltages.

Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should be represented by clusters following a grid-based clustering method, in which an appropriate grid size should be determined. Currently, there are no criteria for determining the proper grid size, and the modifiable areal unit problem has been formulated for the purpose of addressing this issue. The method proposed in this paper is applies a hexagonal grid to geotagged Twitter point data, considering the grid size in terms of both quantity and quality to minimize the limitations associated with the modifiable areal unit problem. Quantitatively, we reduced the original Twitter point data by an appropriate amount using Töpfer’s radical law. Qualitatively, we maintained the original distribution characteristics using Moran’s I. Finally, we determined the appropriate sizes of clusters from zoom levels 9–13 by analyzing the distribution of data on the graphs. Based on the visualized clustering results, we confirm that the original distribution pattern is effectively maintained using the proposed method.

The indium-gallium-zinc-oxide (IGZO) transistor has consistently encountered reliability issues and has intrinsic material limitations that limit its electrical performance. In this study, we demonstrate a device concept containing a buried back gate in the IGZO transistor that offers a practical solution, enhancing device reliability and significantly improving transistor performance. Buried-gate transistors that apply a uniform gating field effect to the effective channel region outperform control-gate transistor devices with respect to the on-current, hysteresis window, subthreshold swing, and mFE. The device yield is also substantially increased (~81% even higher than ~54% of the control device) by mitigating the nonuniform gating field effect arising from the protruding gate structure of the control device. Furthermore, the reduced series resistance and decreased interface trap density of the buried-gate transistor substantiate the geometrical optimization of the original transistor device with intrinsically bending stacked-layers (IGZO/A2O3). These results provide a feasible approach for enhancing the reliability and electrical properties of oxide thin film transistors, potentially paving a way toward the development of a highly reliable transistor for commercial gadgets. [ABSTRACT FROM AUTHOR]