Fruit and vegetable (F&V) harvested from plants trigger a series of stress-related physiological processes, potentially resulting in quality deterioration and considerable losses. Cold chain acting as abiotic stressors and activation of specific pathways to maintains metabolic activities is an effective way to reduce postharvest F&V loss. To this end, real-time monitoring of the micro-environment of the cold chain is an important approach. While temperature and humidity are routinely monitored nowadays, gas is much less explored despite it deeply interacts with the product quality of cold chain. This article analyzes the requirement for quality sensing via gas signal, reviews existing and emerging gas sensor technology and gas signal processing method for F&V cold chain. Furthermore, mathematical models, which interpret sensed gas data and predict product quality, are systematically analyzed and discussed. Gas sensor technology and associated modelling method is an effective approach to improve transparency and product quality for F&V cold chain. The results illustrate that the gas sensor for quality sensing of F&V cold chain should have characteristic with high precise resolution and full scale, low power consumption, low cost and smaller size, existed gas sensors have been gradually developed from a single unit to a plurality of components, specially rigid and flexible structural materials and manufacturing process. Existing mathematical models still have limited prediction accuracy that gas signal interfere product quality. Then, the model need improve the performances to explain the complex interaction relationship between gas and quality.
The constant increase in global energy demand and stricter environmental standards are calling for advanced energy storage technologies that can store electricity from intermittent renewable sources such as wind, solar, and tidal power, to allow the broader implementation of the renewables. The grid-oriented sodium-ion batteries, potassium ion batteries and multivalent ion batteries are cheaper and more sustainable alternatives to Li-ion, although they are still in the early stages of development. Additional optimisation of these battery systems is required, to improve the energy and power density, and to solve the safety issues caused by dendrites growth in anodes. Electrolyte, one of the most critical components in these batteries, could significantly influence the electrochemical performances and operations of batteries. In this review, the definitions and influences of three critical components (salts, solvents, and additives) in electrolytes are discussed. The significant advantages, challenges, recent progress and future optimisation directions of various electrolytes for monovalent and multivalent ions batteries (i.e. organic, ionic liquid and aqueous liquid electrolytes, polymer and inorganic solid electrolytes) are summarised to guide the practical application for grid-oriented batteries.
The statistical monitoring of batch manufacturing processes is considered. It is known that conventional monitoring approaches, e.g. principal component analysis (PCA), are not applicable when the normal operating conditions of the process cannot be sufficiently represented by a Gaussian distribution. To address this issue, Gaussian mixture model (GMM) has been proposed to estimate the probability density function of the process nominal data, with improved monitoring results having been reported for continuous processes. This paper extends the application of GMM to on-line monitoring of batch processes, and the proposed method is demonstrated through its application to a batch semiconductor etch process.
We propose a novel turbo detection scheme based on the factor graph serial-schedule belief propagation equalization algorithm with low complexity for single-carrier faster-than-Nyquist (FTN) and multicarrier FTN signaling. In this work, the additive white Gaussian noise channel and multi-path fading channels are both considered. The iterative factor graph-based equalization algorithm can deal with severe intersymbol interference and intercarrier interference introduced by the generation of single-carrier and multi-carrier FTN signals, as well as the effect of multi-path fading. With the application of Gaussian approximation, the complexity of the proposed equalization algorithm is significantly reduced. In the turbo detection, Low density parity check code is employed. The simulation results demonstrate that the factor graph-based turbo detection method can achieve satisfactory performance with low complexity.
Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge. net)