Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Efficiency Optimization in LTE-U Based Small Cell Networks

In this correspondence, an energy efficient (EE) optimization scheme is proposed for the LTE-unlicensed (LTE-U) based small cell networks. The small cell reuses the licensed bands with the macro base station and shares the unlicensed bands with multiple Wi-Fi access points. Therefore, there exists co-channel interference on licensed bands, and the collision on unlicensed bands. To maximize the system EE while guaranteeing the harmonious coexistence with macro cell and Wi-Fi networks, spectrum (both licensed and unlicensed) and power allocation are jointly considered to formulate a non-convex optimization problem. Accordingly, convex approximation and the classic Dinkelbach method are applied to derive the optimal scheme. Based on the proposed scheme, the interaction between the EE on licensed and unlicensed bands are revealed, and the performance of the scheme is verified via the simulation.
Resource Allocation by Submodular Optimization in Programmable Hierarchical C-RAN

In this paper, we consider a downlink cloud radio access network where a hierarchical structure of virtual controllers is in the center and massive remote radio heads (RRHs) in the coverage area are divided into multiple clusters. Each cluster has one local controller and a high-level controller coordinates control plane decisions among local controllers. Moreover, each local controller is equipped with one server for creating virtual machines (VMs) to execute users’ tasks. Then the output data is transmitted to their respective users through RRHs. This work aims to minimize the average delay including task execution delay and signal transmission delay by joint optimization of task scheduling and resource allocation under power constraint. Specifically, resource allocation consists of VM allocation and RRH assignment. Due to the NP-hardness of the joint optimization problem, we translate it into a matroid constrained submodular function maximization problem and propose a heuristic algorithm to find solutions with 0.5-approximation. Numerical results show that the proposed algorithm can achieve a better performance than two baseline algorithms, i.e., VM prioritized allocation algorithm and RRH prioritized assignment algorithm.
Native Language Identification in Very Short Utterances Using Bidirectional Long Short-Term Memory Network

Native language identification (NLI) is the task of identifying the first language of a user based on their speech or written text in a second language. In this paper, we propose the use of spectrogram- and cochleagram-based features extracted from very short speech utterances (0.8 s on average) to infer the native language of an Urdu speaker. The bidirectional long short-term memory (BLSTM) neural networks are adopted for the classification of utterances among the native languages. A set of experiments is carried out for the network architecture search and the system’s accuracy is evaluated on the validation data set. Overall accuracy of 74.81% and 71.61% is achieved using the Mel-frequency cepstral coefficients (MFCC) and Gammatone frequency cepstral coefficients (GFCC), respectively. Moreover, the optimized MFCC feature-based BLSTM network and GFCC feature-based BLSTM network are merged together to take advantage of both the feature sets. The experiments show that the performance of the merged network surpasses the individual BLSTM networks and accuracy of 75.69% is achieved on the evaluation data. The effect of test data duration is also analyzed (from 0.27 s to 1.5 s); in addition, it is observed that with very short duration as 0.4 s, an accuracy of over 50% can be achieved.
Deep View2View Mapping for View-Invariant Lipreading

Recently, visual-only and audio-visual speech recognition have made significant progress thanks to deep-learning based, trainable visual front-ends (VFEs), with most research focusing on frontal or near-frontal face videos. In this paper, we seek to expand the applicability of VFEs targeted on frontal face views to non-frontal ones, without making assumptions on the VFE type, and allowing systems trained on frontal-view data to be applied on mismatched, non-frontal videos. For this purpose, we adapt the “pix2pix” model, recently proposed for image translation tasks, to transform non-frontal speaker mouth regions to frontal, employing a convolutional neural network architecture, which we call “view2view”. We develop our approach on the OuluVS2 multiview lipreading dataset, allowing training of four such networks that map views at predefined non-frontal angles (up to profile) to frontal ones, which we subsequently feed to a frontal-view VFE. We compare the “view2view” network against a baseline that performs linear cross-view regression at the VFE space. Results on visual-only, as well as audio-visual automatic speech recognition over multiple acoustic noise conditions, demonstrate that the “view2view” significantly outperforms the baseline, narrowing the performance gap from an ideal, matched scenario of view-specific systems. Improvements are retained when the approach is coupled with an automatic view estimator.
An End-to-End Human Segmentation by Region Proposed Fully Convolutional Network

Person segmentation in images has various applications, for example, smart home, human–computer interaction, and scene perception for self-driving cars, which are a key feature of the Internet of Things. Due to limitations in performance, such as accuracy and runtime, most traditional methods do not fulfill the practical requirements. Deep learning-based modern segmentation systems become prevalent. Fully convolutional network (FCN), as a classic image semantic segmentation method, directly optimizes the semantic map from the original image in a pixel-wise manner without using pixel-correlations or global object information. In this paper, we propose an efficient end-to-end person segmentation network structure fusing the person detection network with the FCN. The person detection network estimates the region of interest of persons and enforces the segmentation network to focus on the optimization of person segmentation. The loss function of the proposed network considers both the segmentation error and the detection bias error. In addition, the lightweight design of the detection network that optimizes only person bounding-box coordinates enables real-time person detection. The experimental comparison and analysis of several different networks on several datasets show the effectiveness of the proposed fusion strategy. The approach shows a promising practical application potential by fast running time and high segmentation accuracy.
Trap-Aware Compact Modeling and Power-Performance Assessment of III-V Tunnel FET

We report, for the first time, on a SPICE simulation study of the circuit-level power-performance impact of device traps in a state-of-the-art III-V heterojunction tunnel FET (TFET). First, the individual parasitic effects of junction bulk traps and oxide interface traps are incorporated in a compact model and validated against measurement-calibrated TCAD data, where we propose an analytical formulation for trap-assisted tunneling at the heterojunction and account for the oxide interface charge with a look-up table. Then, the model is used in SPICE simulations on a ring oscillator test bench to predict the impact of traps on logic circuits. It is found that bulk and oxide traps in TFET together cause up to $\sim$5x iso-frequency energy penalty in the desired low-supply-voltage domain $(\leq 0.50$ V), of which oxide traps dominate at high switching activity while bulk and oxide traps contribute comparably when switching is less active. This study quantitatively suggests that trap reduction is the key to the enablement of the full benefit of TFET.
Assessment and training of visuospatial cognitive functions in virtual reality: proposal and perspective

Visuospatial functions play a crucial role in human cognition, which has elicited over years a great deal of research focusing on their assessment, training and restoration. Interestingly, although our visuospatial capacities allow us to understand and infer relationships of 3D objects in space, these 3D aspects of visuospatial processing are profoundly neglected in laboratory tests, and instead, 2D designs are commonly used. Aiming to increase the ecological validity of such tests, we propose an experiment to evaluate the capacity of a 3D virtual space to stimulate cognitive functions. The experiment involves cognitive testing, EEG measurements, and cognitively stimulating tasks in an immersive 3D virtual environment rendered by a unique CAVE system. This paper focuses primarily on two game prototypes that will serve as the virtual environment and describes a natural movement control using the Myo armband, incorporated into one of the games. We also briefly discuss cognitive testing design using the selected brain electrophysiological variables measured on human subjects before and after the virtual reality game use, in order to assess the potential effect of the game.
Effects of distance on detectability of Arctic waterfowl using double‐observer sampling during helicopter surveys

Abstract Aerial survey is an important, widely employed approach for estimating free‐ranging wildlife over large or inaccessible study areas. We studied how a distance covariate influenced probability of double‐observer detections for birds counted during a helicopter survey in Canada’s central Arctic. Two observers, one behind the other but visually obscured from each other, counted birds in an incompletely shared field of view to a distance of 200 m. Each observer assigned detections to one of five 40‐m distance bins, guided by semi‐transparent marks on aircraft windows. Detections were recorded with distance bin, taxonomic group, wing‐flapping behavior, and group size. We compared two general model‐based estimation approaches pertinent to sampling wildlife under such situations. One was based on double‐observer methods without distance information, that provide sampling analogous to that required for mark–recapture (MR) estimation of detection probability, p^, and group abundance, G^, along a fixed‐width strip transect. The other method incorporated double‐observer MR with a categorical distance covariate (MRD). A priori, we were concerned that estimators from MR models were compromised by heterogeneity in p^ due to un‐modeled distance information; that is, more distant birds are less likely to be detected by both observers, with the predicted effect that p^ would be biased high, and G^ biased low. We found that, despite increased complexity, MRD models (ΔAICc range: 0–16) fit data far better than MR models (ΔAICc range: 204–258). However, contrary to expectation, the more naïve MR estimators of p^ were biased low in all cases, but only by 2%–5% in most cases. We suspect that this apparently anomalous finding was the result of specific limitations to, and trade‐offs in, visibility by observers on the survey platform used. While MR models provided acceptable point estimates of group abundance, their far higher stranded errors (0%–40%) compared to MRD estimates would compromise ability to detect temporal or spatial differences in abundance. Given improved precision of MRD models relative to MR models, and the possibility of bias when using MR methods from other survey platforms, we recommend avian ecologists use MRD protocols and estimation procedures when surveying Arctic bird populations.
Improved-Sensitivity Resonant Electric-Field Probes Based on Planar Spiral Stripline and Rectangular Plate Structure

The designs of two resonant electric-field probes with improved sensitivity for near-field measurements are proposed and measurements in weak radiated level are conducted. One probe is the resonant monopole probe and the other is named the resonant T-shaped probe with further improved sensitivity. Each of the two probes is fabricated on four-layer printed circuit board using high-performance and low-loss Rogers material (<inline-formula> <tex-math notation="LaTeX">$\varepsilon _{ {r}} = 3.48$ </tex-math></inline-formula> and tan<inline-formula> <tex-math notation="LaTeX">$\delta = 0.0037$ </tex-math></inline-formula>), which consists of an <italic>LC</italic> resonator made up of a planar spiral stripline (inductor) and a rectangular plate (capacitor), a single-stub transformer, and a SubMiniature version A connector. The <italic>LC</italic> resonator can achieve a specific resonant frequency for a specific narrowband of interest with the pure input resistance. The single-stub transformer is used to transform the pure input resistance to the input impedance of measurement instruments to acquire maximum transmission power. Compared with the referenced broadband probe with the same physical size of tip, the measured <inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{ {12}}\vert $ </tex-math></inline-formula> of the resonant monopole probe is enhanced by 9 dB at the resonant frequency. The resonant T-shaped probe proposed has 3.3-dB sensitivity higher than the resonant monopole probe in terms of <inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{{12}}\vert $ </tex-math></inline-formula>. In order to validate the improved sensitivity of the resonant probes, comparisons on the improvement of sensitivity with other probes are also introduced.
Stability Analysis of Switched Positive Nonlinear Systems by Mode-Dependent Average Dwell Time Method

The stability problem of switched positive nonlinear systems is presented in this paper. Both continuous-time and discrete-time systems are considered. Compared with the average dwell time (ADT) switching, the switching law designed here is more general in which each mode possesses its own ADT. Unlike most of the existing results, based on the approach in which the Lyapunov function method is not involved, several stability criteria are derived. In the end, a numerical example is presented to illustrate the main results. The simulation results demonstrate that the proposed MDADT switching signal can achieve better performance, which shows that the state trajectories converge faster than the ADT switching.
A pictorial key to differentiate the recently detected exotic Haemaphysalislongicornis Neumann, 1901 (Acari, Ixodidae) from native congeners in North America

Abstract Until recently, only two haemaphysaline species, Haemaphysalischordeilis (Packard, 1869) and Haemaphysalisleporispalustris (Packard, 1869), were known to occur in the United States, and neither was considered to be of significant medical or veterinary importance. In 2017–2018 established populations of the Asian longhorned tick, Haemaphysalislongicornis Neumann, 1901, were detected in the eastern US for the first time. Haemaphysalislongicornis has the potential to be a significant threat to human and animal health, and the urgent need to determine the full extent of its distribution and host range requires availability of a straightforward and practical guide to differentiate it from native species. We created a pictorial dichotomous key to all stages of Haemaphysalis spp. known to occur in North America with scanning electron photomicrographs of all H.longicornis life stages, including rarely seen males, to aid researchers in differentiating these species. The largely Neotropical species Haemaphysalisjuxtakochi Cooley, 1946, with established populations in Mexico and sporadic detections in the US on migrating birds is also included.
Image fusion and influence function for performance improvement of ATM vandalism action recognition

Rising rate of vandalism against Automatic Teller Machines (ATMs) is a serious issue within banking industries, prompting needs of a technology to autonomously recognize such events. A vision based fusion method proposed here for classifying these incidents is rooted on visually recognizing heavy or sharp objects potentially used for detecting vandalism actions inferred from optical flow. The recognition performance has been improved chiefly by a novel employment of influence functions in selecting data points of each class useful in learning. We show that the tool recognition performance can be improved when the training data is selected from the ImageNet data set as guided by the influence function.
Iterative Randomized Algorithms for Low Rank Approximation of Tera-scale Matrices with Small Spectral Gaps

Randomized approaches for low rank matrix approximations have become popular in recent years and often offer significant advantages over classical algorithms because of their scalability and numerical robustness on distributed memory platforms. We present a distributed implementation of randomized block iterative methods to compute low rank matrix approximations for dense tera-scale matrices. We are particularly interested in the behavior of randomized block iterative methods on matrices with small spectral gaps. Our distributed implementation is based on four iterative algorithms: block subspace iteration, the block Lanczos method, the block Lanczos method with explicit restarts, and the thick-restarted block Lanczos method. We analyze the scalability and numerical stability of the four block iterative methods and demonstrate the performance of these methods for various choices of the spectral gap. Performance studies demonstrate superior runtimes of the block Lanczos algorithms over the subspace power iteration approach on (up to) 16,384 cores of AMOS, Rensselaer's IBM Blue Gene/Q supercomputer.
Mining Big Data: Its Current Status and Future

Organizations create 2.5 Quintilian bytes of data. So much that 90% of the data in the world today has been set up in the last two years alone. What is Big Data? Big Data is large volumes of structured and unstructured data. This data is what organizations collect on a daily basis. The amount of data is not the important part, but the information gathered from that data is the key. Collecting and analyzing Big Data gives organizations enhanced insight, decision making, and process automation. Approximately each one can agree that big data has taken the business world by storm, but what’s next? Will data continue to grow? What technologies will develop around it? Or will big data become a relic as quickly as the next trend — cognitive technology? Fast data? - appears on the horizon. I believe, am that big data is only going to get bigger and those companies that ignore it will be left further and further behind. This paper studies about what is big data, how does it helps organizations to extract information, its tools and technologies and its future.
Equivalent RLC Ladder Circuit for Scattering by Graphene-Coated Nanospheres

In this paper, an equivalent RLC ladder circuit is derived for a graphene-coated nanosphere. Using this model, it is feasible to tune the resonance frequency of graphene surface plasmons by means of closed-form formulas. To this end, the spherical Bessel and Hankel functions of the modified Mie–Lorenz coefficients are expanded in terms of polynomials and then they are expressed as simple continued fraction forms from which the parameters of the equivalent circuit are derived. It is observed that due to the presence of graphene complex surface conductivity, the impedance of each circuit element is complex, and the real part represents a positive- or negative-valued resistor and the imaginary part modeling an inductor or capacitor. The emergence of the negative resistor which results in the field enhancement is discussed in detail.
Transformer Health Management Based on Self-Powered RFID Sensor and Multiple Kernel RVM

Health management for transformer in service is of great significance to the reliable operation of power system. This paper presents a cost-efficient transformer condition prognosis methodology. First, a power management circuit is designed to guarantee the stable performance of self-powered RFID sensor. Moreover, the sensor data storage mechanism is modified by embedding the sensor data into the ID information of RFID sensor to realize less response time and lower power consumption compared with the traditional approach. Second, a denoising method based on band restricted empirical mode decomposition and a fuzzy threshold is introduced to extract noise-free vibration signal from the measured raw signal. Third, the multiple kernel relevance vector machine (MKRVM) is imported to predict the future condition of transformer. The kernels of MKRVM keep diversity for higher prediction accuracy by using the heterogeneous kernel learning approach. Moreover, the double chain quantum genetic algorithm is employed as optimization tool to find the optimal sparse weights of basic kernel functions in MKRVM. The experimental results indicate that the modified RFID sensor can stably operate even when the load of the transformer is abruptly decreased to zero. The response time and power consumption of RFID sensor are markedly decreased by using the improved sensor data storage mechanism. Furthermore, the prediction experiment results demonstrate that the proposed approach is effective in term of health monitoring for transformer.
A Square-Root-Based Memory Polynomial Model for Concurrent Dual-Band Digital Predistortion

In this letter, a novel 2-D square-root-based memory polynomial behavioral model is proposed. A new set of square-root-based basic functions is adopted to describe the characteristic of the predistorter. Since the proposed model has only two nested summations, the number of coefficients of the proposed model is greatly reduced compared with models which have three nested summations. The experimental results show that the proposed model can reduce the coefficients by more than 66.7% compared with the 2-D digital predistortion (2D-DPD) model. Moreover, the proposed model can achieve better adjacent channel power ratio (ACPR) performance. Compared with the 2D-DPD model and the simple online coefficient update model, the proposed model can improve the normalized mean-square error by 11 dB. Compared with the 2-D modified memory polynomial model, the proposed model can obtain similar ACPR performance with a shorter running time.
Beamforming Design for Low-Power In-Band Wireless Backhaul Systems: Centralized and Distributed Approaches

In this paper, we develop beamforming (BF) schemes to minimize the total transmitted power of both uplink (UL) and downlink (DL) in two-tier network with in-band wireless backhaul (WB) while the target rates of user equipment (UE) are satisfied under the backhaul constraints. We propose a centralized BF scheme with an alternate update algorithm optimizing the BF matrices and power allocation vectors. Then the UL-DL feasible target rate region of the proposed scheme is analyzed, which can be enlarged in both UL and DL by increasing the number of antennas of small cell base stations (SBSs). Simulation results show that the proposed in-band WB system for two-tier network can save the power consumption by 33.5% and 82.1% compared with the out-of-band WB system and conventional one-tier network, respectively, at the UE target rates of 80 Mb/s in DL and 40 Mb/s in UL. In addition, we propose a distributed BF scheme to locally optimize the variables at each SBS, which can achieve above 90% of the feasible target rate region of the centralized scheme without the instantaneous information exchange.