Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-term cover cropping seasonally affects soil microbial carbon metabolism in an apple orchard

ABSTRACT Groundcover management can significantly affect soil microbial metabolic activities, especially carbon metabolism, in apple orchards. However, there have been few studies on the effects of groundcover on the seasonality of soil microbial carbon metabolism. We, therefore, studied soil microbial carbon metabolism in an apple orchard on China’s Loess Plateau under four single species cover crops (the grass Dactylis glomerata L., and the legumes Trifolium repens, Coronilla varia L., Lotus corniculatus L.) during spring, summer and fall. Cover cropping significantly, but differentially, promoted soil microbial carbon metabolism in spring and fall. However, cover cropping leads to a significant reduction of soil moisture in spring and summer due to the competition of soil moisture between the cover crops and apple trees, which probably lead to the changes in types of carbon substances metabolizing by soil microbes in summer. Besides, cover crop significantly enhanced soil organic carbon contents between three seasons while clean cultivation had slight, non-significant effects. The promotion of soil microbial metabolic activities was probably an important mechanism for the carbon accumulation, and we postulate that leguminous cover plants may have significantly different effects, mediated through their root exudates, from grasses on soil carbon contents. Graphical abstract
Research on Distributed Parallel Dimensionality Reduction Algorithm Based on PCA Algorithm

PCA algorithm is a typical data dimensionality reduction method, which projects high-dimensional data to a lower-dimensional space to obtain a low-dimensional data set that can maximally represent these characteristics of the original data set. The PCA algorithm can effectively achieve dimensionality reduction for high-dimensional data and is widely used in various fields. Aimed at the tedious calculation process of PCA algorithm and the time-consuming of processing massive stream data, this paper proposes a distributed parallel dimensionality reduction algorithm that called DP-PCA by improving the PCA algorithm. Based on the theory of PCA algorithm, DP-PCA algorithm includes three parts of improvement research. Firstly, the original data set is preprocessed by using the “mean” method. Secondly, the solution process of correlation coefficient matrix is improved. Thirdly, this paper designs a distributed parallel dimensionality reduction scheme for DP-PCA algorithm. In addition, this paper deploys DP-PCA algorithm on Storm platform to realize parallelization of the algorithm, and tests the DP-PCA algorithm. Experiments show that DP-PCA algorithm improves computational efficiency and reduces the dimensionality reduction time, and improves the speedup ratio.
Hydrobatics: A Review of Trends, Challenges and Opportunities for Efficient and Agile Underactuated AUVs

Hydrobatics refers to agile maneuvering of under-water vehicles just like aerobatics represents agile maneuvering of aerial vehicles. Performance trade-offs between flight and hover style autonomous underwater vehicles (AUVs) means that either maneuverability or range is compromised. Hydrobatic capabilities in flight style AUVs can bridge this gap and lead to more efficient and agile vehicles; thereby encouraging disruptive designs. As this is a relatively new area of research with very limited published research work, the focus of this paper is to present a multidisciplinary literature review to provide a path forward for further research. Relevant impact areas in ocean production, environmental sensing and security are discussed. Technical challenges are described in underactuated control and flight dynamics modeling. Synergies and opportunities are explored with aerospace engineering, robotics and artificial intelligence. As a part of this study, a simulation and verification framework is suggested and ongoing work is presented.
Area-Efficient On-Chip Transient Detection Circuit for System-Level ESD Protection Against Transient-Induced Malfunction

A new on-chip transient detection circuit with superior area efficiency is proposed against the system malfunction resulting from system-level electrostatic discharge (ESD) events. With dual-latched structure, a better area efficiency can be achieved by the reduced time constant inquiry. The proposed transient detection circuit with a silicon area of 40 <inline-formula> <tex-math notation="LaTeX">${\mu }\text{m}\,\,{\times }\,\,60\,\,{\mu }\text{m}$ </tex-math></inline-formula> has been fabricated in a 0.18-<inline-formula> <tex-math notation="LaTeX">${\mu }\text{m}$ </tex-math></inline-formula> CMOS process with 1.8-V devices. The detection sensitivity has been successfully verified under ±200 V system-level ESD tests. To achieve the “Class B” specification of IEC 61000-4-2 standard, the proposed transient detection circuit serves as a safety guard for the system. Through the hardware/firmware co-design, the auto-recovery procedure can be activated by the proposed transient detection circuit sending out a warning signal. With the proposed transient detection circuit co-works with the system program, the immunity level of microelectronic products against the electromagnetic compatibility (EMC) of ESD events can be effectively improved.
Hole Mobility Model for 6H-SiC Thermoresistive Sensors Simulation

The modeling of low-field hole mobility in 6H silicon carbide (SiC) was carried out. Fitting coefficients for the temperature-dependent Caughey-Thomas equation and the Arora model were obtained. Experimental mobility data reported in the literature served as the basis for the model development. It was shown that Arora model is much more suitable for modeling of hole mobility in 6H-SiC in a wide range of doping concentrations and temperatures than widely used temperature-dependent Caughey-Thomas model. As far as we know, the fitting coefficients of the Arora model for hole mobility in 6H-SiC has not been reported until now. The developed mobility model was then applied to simulate a number of 6H-SiC material properties, which are critical for the design of thermoresistive sensors. Doping and temperature dependencies of resistivity and temperature coefficient of resistivity were obtained. The presented mobility model can be used in device simulators to design and optimize p-type 6H-SiC high-temperature thermoresistive sensors and other 6H-SiC devices.
An Evaluation System for Cognitive Impairment based on Brain Functional Network Links

This paper presents an evaluation system for cognitive impairment patients based on the strength of brain functional network links. Through the combination of artificial neural network (ANN) and scoring function, the system can obtain the score which represents the cognitive status of the subject. The data set is divided into three groups according to the degree of cognitive impairment, named as normal people (NC), people with mild cognitive impairment (MCI) and Alzheimer's (AD). The features of sample are selected by Kendall correlation coefficients between samples’ labels and link strength of brain functional network. Three kinds of data are classified by ANN and the accuracy of classification is 0.82, 0.69 and 0.78 for 2 categories. The system’s score is compared with the MMSE of the samples and it’s found that they have an approximate distribution. This indicates that the evaluation system proposed in this paper has significance in clinical diagnosis for cognitive impairment.
From Smart to Personal Environment: Integrating Emotion Recognition into Smart Houses

Recent advances in computational and sensing technologies allowed to incorporate different devices into a smart systems, making the ubiquitous or pervasive computing a hot topic for research and commercial projects. One technology, that can help the user to interact with invisible system representing smart environment is spoken dialogue system. Following the success in research on automatic speech recognition and natural language understanding, spoken dialogue systems have significantly improved themselves during the past decade and now bringing the communication between human and machine closer to natural level. Having user as a main subject, both system may benefit from explicit information about his current state and mood, adjusting their behaviour to the certain extent. In this paper we consider the combination of ubiquitous computing, spoken dialogue systems, and emotion recognition technologies, suggest possible ways of information flow, discuss future applications and potential problems. We find, that these technologies can be complementary to each other, increasing their flexibility, robustness and intelligibility when combined. We present the usage of such approach in a smart house environment, continuously tracking the state of the user, interacting with them in real time and reacting to mood changes.
Non-negative Matrix Factorization Speech Enhancement Method Based on Constraints of Temporal Continuity

The constrained low-rank and sparse matrix decomposition (CLSMD) method ignores the temporal continuity between adjacent speech frames in the process of speech enhancement, resulting in a sparse matrix generated by decomposition with isolated discrete points. Therefore, in order to improve the noise suppression ability of the speech system and improve the enhanced speech quality and intelligibility, this paper proposes a speech enhancement method based on Temporal continuity Constraint for Non-negative Low-rank and Sparse Matrix Decomposition (TCNLSMD). In this method, in addition to adding low -rank and sparse constraints, temporal continuity constraints are added. The proposed method based on the sparse matrix obtained by eigenvalue decomposition of non-negative matrices and hard-threshold function estimation, the discrete sparse matrix is reduced by adding temporal continuity constraints to reduce discrete isolated points, retaining more speech information and reducing the enhanced speech distortion. The experimental results show that under various types of noise test conditions, compared with the current mainstream speech enhancement methods, especially with NLSMD, the proposed method improve the noise suppression capability, make the residual noise less, and improve the quality of the enhanced speech.
Improving Boundary Level Calculation in Quantized Iterative Learning Control With Encoding and Decoding Mechanism

This paper investigates an iterative learning control for single-input, single-output, and linear time-invariant discrete system. The special design of the learning gain matrix is introduced, where a finite uniform quantizer is incorporated with an encoding and decoding mechanism to realize the zero-error convergence of a tracking problem. Furthermore, the boundary-level calculation is considerably improved using lifting technique and infinity-norm of vectors under this mechanism. Some illustrations of the simulations verify the theoretical results.
Sparse Subspace Clustering for Hyperspectral Images with Missing Pixels

Spectral image clustering discovers groups and identifies distributions from spectral signatures without requiring a previous training stage. The sparse subspace clustering-based methods group spectral signatures in different subspaces, finding the sparsest representation for each pixel, guaranteeing that they belong to the same class. Although these methods have shown good accuracy, as the number of pixels increases, computational complexity becomes intractable. This paper proposes to reduce the number of pixels to be classified in the spectral image by half through a sub-sampling procedure that eliminate every two contiguous pixels, preserving the spatial structure of the image. Then, the clustering result is obtained using its spatial information. The performance of the proposed spectral image clustering framework is evaluated in three datasets showing that similar accuracy is obtained up to 7.9 times faster compared to the classification of the data sets without subsampling.
Real Time Bus Tracking and Location Updation System

Public transportation system plays a major role in every aspect of life. It has a greater impact on economic development of the country. Tracking, monitoring, scheduling, alert services are the major challenges faced by this system. Currently, major services of this system are manually operated which are approximate and ease of access is denied to the people. This project aims in automating the services of the system that can provide the real time tracking experience of the public transport buses. The buses will be having RFID tags within them and RFID readers will be placed in every bus stops. Arduino serves as the central controller for this system. GSM module will be used to send the tracking messages to the authorized persons for continuous monitoring. GPS is used for getting the location of the buses. Users will be getting the bus tracking details as notifications in their mobiles through IoT. The inputs from RFID readers are continuously updated to Arduino for processing the data. The processed data is sent to the cloud which serves as the interface between the user and the system.
Energy Efficient Hierarchical Key Management Protocol

A wireless sensor network (WSN) is a group of resource-constrained, inexpensive, tiny, and homogeneous or heterogeneous sensor nodes. The inherent nature of WSNs such that it makes them deployable in a variety of circumstances, which increases the interest towards them but at the same time poses tremendous challenges such as resource-constrained nodes, unattended operations, unknown topology and wireless communication links. Security in WSNs can be achieved with the help of various cryptographic operations. The strength of cryptographic system depends on the secrecy of the key it uses. So, a solid strong key management frame work is the prerequisite for the cryptographic primitive upon which other security primitives are built.To improve the energy efficiency and increase the resilience more effectively, an Energy Efficient Hierarchical Key management Protocol (EEHKMP) for hierarchical homogeneous WSN is proposed. In this protocol, a Differentiated random KPD (DKPD) process is employed for randomly deployed distributed WSN. Its main objective is to distribute different number of keys which are chosen randomly to different sensors in order to enhance the resilience of certain links such that the nodes can route through those links with higher resilience. This DKPD process divides the sensor nodes into different classes and pre-distributes the keys according to each class. Nodes with maximum residual energy and minimum distance are elected as cluster heads (CHs). The CH sets up the intra-cluster and inter-cluster routes with nodes having more shared keys. CH generates multiple random key shares to generate pair-wise key and transmits each key share to source and destination on each hop route, which is selected based on the cost function. Key shares are hop-by-hop encrypted / decrypted by a combination of all shared pre-distributed keys on that hop. Finally, a key update mechanism is presented to update the keys.
Modeling of Surface Effects in Massive Rotors of Electromagnetic Brakes

A numerical 2-D electromagnetic brake model is obtained in the Comsol Multiphysics software package. Due to this, the process of changing the magnetic field in the rotating brake rotor is investigated, the reasons for the displacement of currents and the magnetic field to the rotor surface are shown, the graphs of current and magnetic field distribution along the rotor depth are given, it is shown that the electromagnetic field in the rotating brake rotor differs from the classical surface effect (varies not according to the exponential law), although there is an outward similarity.
Spectrum Trading for E ffi cient Spectrum Utilization

The conventional command and control based spectrum management has led to substantial underutilization of some spectrum bands while severely crowding others due to the uneven and dynamic needs that vary over time and at different locations. Spectrum trading has emerged as a promising management approach to substantially improve spectrum utilization and user experience in wireless communications by taking advantage of market-based mechanisms. This article presents an overview of spectrum trading, including the fundamental characteristics of spectrum trading markets, the state-of-the-art techniques for modeling and resolving various spectrum trading issues, and trading based dynamic spectrum sharing and access. Moreover, some open issues in spectrum trading are identified for future research in this area. Received on 20 September 2013; accepted on 29 January 2014; published on 11 April 2014
Trends and Challenges of Multimodal User Interfaces

The development of information technologies brings new possibilities for its users. The development is noticeable in user interface design as well – new data input and output methods and facilities are proposed, increasing resource capabilities allow more realistic user interfaces. However together with the growth of user interface possibilities and technologies new challenges rise too. Therefore it is important to understand current challenges in order to understand future trends. In this paper we review the peculiarities of multimodal user interfaces and propose multimodal user interface taxonomy. This taxonomy defines possible types of data input and output and is used for input/output combination matrix generation. We summarize existing solutions and challenges based on the matrix and evaluate each combination. The evaluated combinations in the matrix highlight the main tendencies in natural user interface design area.
Adaptive Compression Algorithm Selection Using LSTM Network in Column-oriented Database

Data compression is a key part of database management systems for storage saving and performance enhancement. In column-oriented databases, records belong to the same attribute are stored nearby, and the similarity between these records increases the compressibility of data and expands the range of compression algorithms to choose. Since different data compression algorithms process data in different manners, the achieved compression ratio varies significantly. This makes it worth studying the choice of compression algorithms depending on features of data to be compressed. As Recurrent Neural Networks is good at processing and making predictions based on series of data, we propose a Long-Short Term Memory network based model to select compression algorithm for input data blocks adaptively. Given a typical database benchmark, we implemented our model to formulate compression strategies for each data block and managed to reduce at most 15% storage size than using a single compression algorithm scheme.
Energy Harvesting-Based Smart Transportation Mode Detection System via Attention-Based LSTM

Detecting the transportation mode of an individual’s everyday travel provides useful information in urban design, real-time journey planning, and activity monitoring. In existing systems, the accelerometer and GPS are predominant signal sources which quickly drain the limited battery life of the wearable devices. In this paper, we present an alternative approach for fine-grained transportation mode detection using kinetic energy harvester (KEH). We demonstrate the feasibility of using the output signal from the KEH device as the information source to achieve transportation mode detection. The proposed system is motivated by the fact that different transportation modalities produce distinctive motion patterns which are expected to leave distinctive patterns for context detection. To achieve fine-grained transportation mode detection, we design a transportation detection framework based on attention-based Long Short Term Memory (LSTM). We evaluate our approach using 38.6 hours of transportation data, which is collected from a total of six volunteers in three months’ time using our prototype. The evaluation results show that our approach is able to reach an overall accuracy of over 97% to detect fine-grained transportation modalities. In addition, our measurements show that the power consumption of the sampling KEH signal is only 460uW which significantly outperforms the existing transportation mode detection systems.
Channels with Action Dependent States and Common Reconstructions

In channels with action dependent states, a common message is conveyed using two encoders operating sequentially, viz. an action encoder and a channel encoder. The actions drive the output of a discrete-memoryless channel (DMC), which in turn forms the state process for the DMC between the channel encoder and receiver. Assuming non-causal knowledge of the state-process at the channel encoder, a single letter characterization of the capacity is known in the discrete memoryless case. We consider the action dependent state channel with a common message and an additional private message at the channel encoder, along with common reconstructions (CR) of the state process at the channel encoder and the decoder. Capacity characterizations for the discrete memoryless and Gaussian versions are presented. As a consequence, we settle the capacity characterization of the Gaussian action dependent channel with only a common message and CR. Moreover, by identifying a connection to degraded message sets multiple access channel (deg-MAC) models studied in literature, we establish the capacity regions for the discrete and Gaussian versions of deg-MAC with CR constraints.
Review of vision-based Simultaneous Localization and Mapping

Vision-based simultaneous localization and mapping (VSLAM) which uses visual sensor to make a robot locate itself in an unknown environment while simultaneously construct a map of the environment. With the continuous development of computer vision and robotics, VSLAM has become a supporting technology for popular fields such as unmanned aerial vehicle, virtual reality and unmanned driving. In this paper, the classical framework of visual SLAM is introduced briefly. On this basis, the key technologies and latest research progress of VSLAM from indirect and direct methods are surveyed. Then the research progress of deep learning techniques applied to VSLAM is reviewed. Finally, the development tendency of VSLAM is discussed.
Pervasive Persuasion for Stress Self-Regulation

This article reports on coupled smartwatch and smartphone pervasive apps enabling stress self-regulation. Stress, the physiological responses of an organism to demanding conditions, can be both beneficial and harmful. Beneficial stress, or eustress, enhances physical or mental abilities. Harmful stress, or distress, can result in reduced abilities, anxiety, or depression. The potential of pervasive computing to enable stress self-regulation, that is, the ability to benefit from eustress while avoiding or limiting distress, is explored in this article. It first reports on Stila Computed Stress, a stress estimate computed after an original model from pulse rates delivered by smartwatches. This article then describes how Stila Computed Stress is combined with users' activity reports and pervasively delivered on their smartwatches and smartphones. It further reports on a real life evaluation pointing to the pervasive apps' persuasiveness, that is, the apps' capacity to increase subjective stress awareness so as to enhance stress self-regulation.
An Analytical Model of Organic Light Emitting Diodes Including the Temperature Effects

An analytical model describing the current-voltage characteristics of modern organic light emitting diodes (OLED) including the temperature effects is reported. OLED operations in different regions are analyzed with emphasis on temperature effects. The increasing carrier concentrations and mobility with temperature mainly contribute to the higher current. The region-wise formulas are connected to form a full-region model. With all parameters originating from the OLED physics, the developed model agree well with experimental OLED data.