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The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human–computer interaction. Achieving this goal using loose-fit garments instrumented with sensors is particularly challenging, due to the complex interaction between garments and human body. Herein we present a method to detect and recognize human posture with casual loose-fitting smart garments integrated with highly sensitive, stretchable, optical transparent, and low-cost strain sensors. By attaching these sensors to an off-the-shelf casual jacket, we developed a smart loose-fitting sensing garment that enables posture recognition using a deep learning model, domain-adaptive Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM). This deep learning model overcame the noise and variation due to the complex interaction between loose-fitting garments and human body. Considering that users’ labeled data are usually not available in the training stage, an additional domain discriminator path on the conventional CNN-LSTM model has been introduced to further improve the adaptability. To evaluate the potential of this loose-fitting smart garment, three case studies were conducted under realistic conditions: recognitions of human activities, stationary postures with random hand movements and slouch. Our results demonstrate the potential of the proposed smart garment system for practical applications. [ABSTRACT FROM AUTHOR]

The demand for energy has increased tremendously around the whole world due to rapid urbanization and booming industrialization. Energy is the major key to achieving an improved social life, but energy production and utilization processes are the main contributors to environmental pollution and greenhouse gas emissions. Mitigation of the energy crisis and reduction in pollution (water and air) difficulties are the leading research topics nowadays. Carbonaceous materials offer some of the best solutions to minimize these problems in an easy and effective way. It is also advantageous that the sources of carbon-based materials are economical, the synthesis processes are comfortable, and the applications are environmentally friendly. Among carbonaceous materials, activated carbons, graphene, and carbon nanotubes have shown outstanding performance in mitigating the energy crisis and environmental pollution. These three carbonaceous materials exhibit unique adsorption properties for energy storage, water purification, and gas cleansing due to their outstanding electrical conductivity, large specific surface areas, and strong mechanical strength. This paper reviews the synthesis methods for activated carbons, carbon nanotubes, and graphene and their significant applications in energy storage, water treatment, and carbon dioxide gas capture to improve environmental sustainability. [ABSTRACT FROM AUTHOR]

Piezoelectric energy harvester (PEH), which generates electricity from stress or vibrations, is attracting tremendous attention as a viable solution to extend battery life of wearable devices. More interestingly, besides the energy harvesting capability, recent research has demonstrated the feasibility of leveraging PEH as an power-free sensor for gait recognition as its stress or vibration patters are significantly influenced by the gait. However, as PEHs are not designed for precise motion sensing, the gait recognition accuracy remains low with conventional classification algorithms. The accuracy deteriorates further when the generated electricity is stored simultaneously. In this work, to achieve high performance gait recognition and efficient energy harvesting at the same time, we make two distinct contributions. First, we propose a preprocessing algorithm to filter out the effect of energy storage on PEH electricity signals. Second, we propose long short-term memory (LSTM) network-based classifiers to accurately capture temporal information in gait-induced electricity generation. We prototype the proposed gait recognition architecture in the form factor of an insole and evaluate its gait recognition as well as energy harvesting performance with 20 subjects. Our results show that the proposed architecture detects human gait with 12 percent higher recall and harvests up to 127 percent more energy while consuming 38 percent less power compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

For UAV-aided wireless systems, online path planning attracts much attention recently. To better adapt to the real-time dynamic environment, for the first time, we propose a Monte Carlo Tree Search (MCTS)-based path planning scheme. In details, we consider a single UAV acts as a mobile server to provide computation tasks offloading services for a set of mobile users on the ground, where the movement of ground users follows a Random Way Point model. Our model aims at maximizing the average throughput under energy consumption and user fairness constraints, and the proposed time-saving MCTS algorithm can further improve the performance. Simulation results show that the proposed algorithm achieves a larger average throughput and a faster convergence performance compared with the baseline algorithms of Q-learning and Deep Q-Network. [ABSTRACT FROM AUTHOR]

Multifunctional 3D‐printed nanocomposites based on poly(lactic‐co‐glycolic acid), that is, PLGA (RESOMER® LG857S) were developed for simultaneous monitoring of cells and scaffold as a function of time and spectral responses. These were achieved by impregnating carbon quantum dots (CQDs) on PLGA using melt‐blending, plasticating extrusion, and 3D‐printing. The nanocomposites enabled enhanced bio‐affinity and cellular interactions for bone tissue engineering (TE). PLGA (control) and PLGA‐CQD scaffolds were used for growing human adipose‐derived‐stem‐cells (ADSCs) and tested for cell biocompatibility, cellular adhesion, growth, and osteogenesis. CQDs were found to enhance the hydrophilicity of nanocomposites and promote cellular nesting. MTS assays confirmed that CQDs on PLGA act as cell anchoring sites, thereby enhancing seeding efficiency and cell proliferation. Alkaline phosphate tests showed increased osteogenesis and Alizarin assays confirmed enhanced bone mineralization on PLGA‐CQD. The qPCR tests based on selected mRNA expressions showed that the incorporation of CQDs significantly enhanced osteogenesis of ADSCs during all three phases of cell differentiation. The intrinsic luminescence of the composites allowed label‐free monitoring of cell proliferation and bone mineralization on the scaffolds. Thus, the CQDs facilitated significant enhancements in composite processability with customized fabrication of 3D printed scaffolds, bone tissue osteoconductivity, and monitoring of cell‐scaffold activities, offering multifunctional benefits for bone TE. [ABSTRACT FROM AUTHOR]

Nanocomposites are promising for manufacturing small diameter vascular biodegradable polymer‐based stents (BDPSs) to obviate frequent ordeals of in‐stent restenosis and stent thrombosis by providing appropriate support for vasculature as well as tissue regeneration. Freeform (without any support), vascular‐scale, small diameter (2 mm) tubular nanocomposites were fabricated using 3D printing based on fused filament fabrication (FFF). The nanocomposites were constructed using bioresorbable polylactic acid (PLA) and carbon quantum dots (CQDs). The3D‐printing process of the PLA‐CQD nanocomposites was optimized to deliver improved structural integrity as well as mechanical and biological properties for cardiovascular systems. The PLA‐CQD composites demonstrated excellent processability, hydrophilicity, tensile strength, compressive strength, radial stability, and cell proliferation relative to pure PLA. The results show that the addition of CQDs enhances stent properties significantly, such as, composite hydrophilicity by about 25%, tensile strength by 24%, compressive strength by 66%, and cell proliferation by 50% compared to PLA alone. The results show that the combination of 3D printing with PLA‐CQD nanocomposites could be a viable method to produce bioresorbable nanocomposites for cardiovasculatures with noninvasive imaging for monitoring composite condition and cell growth. [ABSTRACT FROM AUTHOR]

In this paper, we show how the absorption and re-radiation energy from molecules in the air can influence the Multiple Input Multiple Output (MIMO) performance in high-frequency bands, e.g., millimeter wave (mmWave) and terahertz. In more detail, some common atmosphere molecules, such as oxygen and water, can absorb and re-radiate energy in their natural resonance frequencies, such as 60 GHz, 180 GHz and 320 GHz. Hence, when hit by electromagnetic waves, molecules will get excited and absorb energy, which leads to an extra path loss and is known as molecular attenuation. Meanwhile, the absorbed energy will be re-radiated towards a random direction with a random phase. These re-radiated waves also interfere with the signal transmission. Although, the molecular re-radiation was mostly considered as noise in literature, recent works show that it is correlated to the main signal and can be viewed as a composition of multiple delayed or scattered signals. Such a phenomenon can provide non-line-of-sight (NLoS) paths in an environment that lacks scatterers, which increases spatial multiplexing and thus greatly enhances the performance of MIMO systems. Therefore in this paper, we explore the scattering model and noise models of molecular re-radiation to characterize the channel transfer function of the NLoS channels created by atmosphere molecules. Our simulation results show that the re-radiation can increase MIMO capacity up to 3 folds in mmWave and 6 folds in terahertz for a set of realistic transmit power, distance, and antenna numbers. We also show that in the high SNR, the re-radiation makes the open-loop precoding viable, which is an alternative to beamforming to avoid beam alignment sensitivity in high mobility applications. [ABSTRACT FROM AUTHOR]

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, and providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 h of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches. [ABSTRACT FROM AUTHOR]

IoT enables a new set of novel applications that promise to transform the physical world as we know it today. Current IoT applications have demonstrated the feasibility and promise of this technology, but there is still a large space open for innovation. We present our vision for the next generation of IoT devices: IoT devices that are autonomous, in terms of energy and communication, as well as cost-efficient to deploy. We present enabling-technologies to realize our vision and open research challenges. [ABSTRACT FROM AUTHOR]

With the rapid development of sensor networks and embedded computing technologies, miniaturized wearable healthcare monitoring devices have become practically feasible. For many of these devices, accelerometer-based user authentication systems by gait analysis are becoming a hot research topic. However, a major bottleneck of such system is it requires continuous sampling of accelerometer, which reduces battery life of wearable sensors. In this paper, we presentKEH-Gait, which advocates use of output voltage signal from kinetic energy harvester (KEH) as the source for gait recognition.KEH-Gaitis motivated by the prospect of significant power saving by not having to sample the accelerometer at all. Indeed, our measurements show that, compared to conventional accelerometer-based gait detection,KEH-Gaitcan reduce energy consumption by 82.15 percent. The feasibility ofKEH-Gaitis based on the fact that human gait has distinctive movement patterns for different individuals, which is expected to leave distinctive patterns for KEH as well. We evaluate the performance ofKEH-Gaitusing two different types of KEH hardware on a data set of 20 subjects. Our experiments demonstrate that, althoughKEH-Gaityields slightly lower accuracy than accelerometer-based gait detection when single step is used, the accuracy problem can be overcome by the proposed Probability-based Multi-Step Sparse Representation Classification (PMSSRC). Moreover, the security analysis shows that the EER of KEH-Gait against an active spoofing attacker is 11.2 and 14.1 percent using two different types of KEH hardware, respectively. [ABSTRACT FROM AUTHOR]

On-body health monitoring involves continuous sensing and wireless data transmissions, which severely impacts the battery life of wearable devices. However, rapid advances in kinetic energy harvesting (KEH) soon might obviate the need for battery recharging or replacement by harnessing human motion energy. The authors discuss the challenges and opportunities of KEH technology to help realize the ultimate vision of fully autonomous IoT-based healthcare. [ABSTRACT FROM AUTHOR]

Kinetic energy harvesting (KEH) may help combat battery issues in wearable devices. While the primary objective of KEH is to generate energy from human activities, the harvested energy itself contains information about human activities that most wearable devices try to detect using motion sensors. In principle, it is therefore possible to use KEH both as a power generator and a sensor for human activity recognition (HAR), saving sensor-related power consumption. Our aim is to quantify the potential of human activity recognition from kinetic energy harvesting (HARKE). We evaluate the performance of HARKE using two independent datasets: (i) a public accelerometer dataset converted into KEH data through theoretical modeling; and (ii) a real KEH dataset collected from volunteers performing activities of daily living while wearing a data-logger that we built of a piezoelectric energy harvester. Our results show that HARKE achieves an accuracy of 80 to 95 percent, depending on the dataset and the placement of the device on the human body. We conduct detailed power consumption measurements to understand and quantify the power saving opportunity of HARKE. The results demonstrate that HARKE can save 79 percent of the overall system power consumption of conventional accelerometer-based HAR. [ABSTRACT FROM AUTHOR]

Carbon quantum dots (CQDs) have emerged as potential alternatives to classical metal-based semiconductor quantum dots (QDs) due to the abundance of their precursors, their ease of synthesis, high biocompatibility, low cost, and particularly their strong photoresponsiveness, tunability, and stability. Light is a versatile, tunable stimulus that can provide spatiotemporal control. Its interaction with CQDs elicits interesting responses such as wavelength-dependent optical emissions, charge/electron transfer, and heat generation, processes that are suitable for a range of photomediated bioapplications. The carbogenic core and surface characteristics of CQDs can be tuned through versatile engineering strategies to endow specific optical and physicochemical properties, while conjugation with specific moieties can enable the design of targeted probes. Fundamental approaches to tune the responses of CQDs to photo-interactions and the design of bionanoprobes are presented, which enable biomedical applications involving diagnostics and therapeutics. These strategies represent comprehensive platforms for engineering multifunctional probes for nanomedicine, and the design of QD probes with a range of metal-free and emerging 2D materials. [ABSTRACT FROM AUTHOR]

Advances in nanotechnology are paving the way for a new generation of sensor networks at the nanoscale, promising radically new applications in medical, biological, and chemical fields. At the nanoscale, it is difficult to use large batteries for prolonged operations, necessitating energy harvesting as the most viable solution for these nanosensor networks. Unfortunately, the rate at which energy can be harvested at the nanoscale may not be sufficient to simultaneously power all of the conventional device components, including the microprocessor, memory, sensor, and the communication radio. We propose a simplified architecture for energy-harvesting nanosensor networks that uses the energy signatures of monitored events for efficient event recognition at the sink. Using the instantaneous harvested energy to transmit a short pulse of proportional amplitude, the proposed architecture obviates the need for several conventional sensor node elements, including the microprocessor, memory, and sensor. In this article, we validate the feasibility of the architecture using a novel application of nanosensor networks for microscopic monitoring of chemical reactors, and we demonstrate that different types of reactions can be detected accurately at a remote sink. [ABSTRACT FROM PUBLISHER]

Doping of graphene has emerged as a key strategy to improve the electrocatalytic performance of the oxygen reduction reaction (ORR). Activated graphene co-doped with iodine and nitrogen atoms (NIG) was developed in this work using a facile scalable approach. The onset potential, current density, and four-electron reduction pathway of the newly developed catalyst were significantly improved. The charge-transfer resistance of co-doped NIG was found to be much lower than nitrogen-doped graphene (NG); furthermore, the stability of NIG and its resistance to methanol crossover were also improved. The synergistically enhanced ORR performance of NIG was found to be a result of a high strain and size advantage of the larger iodine atom clusters (compared to nitrogen), which facilitate the simultaneous enrichment of anode electrons and O2 and H2O molecule transport at catalytic sites, inducing four-electron transfer in a single step. These results are promising for application in alkaline fuel cells. [ABSTRACT FROM AUTHOR]

Hypertext transfer protocol (HTTP) is the fundamental mechanics supporting web browsing on the Internet. An HTTP server stores large volumes of contents and delivers specific pieces to the clients when requested. There is a recent move to use HTTP for video streaming as well, which promises seamless integration of video delivery to existing HTTP-based server platforms. This is achieved by segmenting the video into many small chunks and storing these chunks as separate files on the server. For adaptive streaming, the server stores different quality versions of the same chunk in different files to allow real-time quality adaptation of the video due to network bandwidth variation experienced by a client. For each chunk of the video, which quality version to download, therefore, becomes a major decision-making challenge for the streaming client, especially in vehicular environment with significant uncertainty in mobile bandwidth. In this paper, we demonstrate that for such decision making, the Markov decision process (MDP) is superior to previously proposed non-MDP solutions. Using publicly available video and bandwidth datasets, we show that the MDP achieves up to a 15x reduction in playback deadline miss compared to a well-known non-MDP solution when the MDP has the prior knowledge of the bandwidth model. We also consider a model-free MDP implementation that uses Q-learning to gradually learn the optimal decisions by continuously observing the outcome of its decision making. We find that the MDP with Q-learning significantly outperforms the MDP that uses bandwidth models. [ABSTRACT FROM PUBLISHER]