Analysis of crowds' movement using Twitter

Over the last decade, the infrastructure supporting the smart city has lived together with and was surpassed by the rise of social media. The tremendous growth of both mobile devices and social media users has unearthed a new kind of services in the so‐called location‐based social networks (LBSNs). In this new scenario, the term crowdsensing refers to sharing data collected by sensing humans with the aim of measuring phenomena of common interest. Crowd‐sourced location data provide the ability to study, for the first time, the movement of individuals in urban environments. In this paper, we address the problem of monitoring crowds, whereabouts and movement, which can assist decision making in education, emergency training, urban planning, traffic engineering, etc. Precisely, two‐phase density‐based analysis for collectives and crowds (2PD‐CC) is a novel methodology over public data in LBSN, which combines density‐based clustering, outlier detection a topic modeling over a region under study to detect, predict, and explain abnormal group behavior. In order to validate the methodology and its potential application to full‐scale problems, an experiment over Twitter data was performed in Madrid city.     

Resource and power allocation for achieving rate fairness in D2D communications overlaying cellular networks

Wireless Networks - This paper presents a novel resource and power allocation scheme for device-to-device (D2D) communications overlaying cellular networks. The proposed scheme is implemented in...     

Resilient BBU placement in 5G C-RAN over optical aggregation networks

Photonic Network Communications - The huge data demand envisioned for the 5G requires radical changes in the mobile network architecture and technology. Centralized radio access network (C-RAN) is...     

Ice‐Assisted Synthesis of Black Phosphorus Nanosheets as a Metal‐Free Photocatalyst: 2D/2D Heterostructure for Broadband H2 Evolution

A 2D/2D heterojunction of black phosphorous (BP)/graphitic carbon nitride (g‐C₃N₄) is designed and synthesized for photocatalytic H₂ evolution. The ice‐assisted exfoliation method developed herein for preparing BP nanosheets from bulk BP, leads to high yield of few‐layer BP nanosheets (≈6 layers on average) with large lateral size at reduced duration and power for liquid exfoliation. The combination of BP with g‐C₃N₄ protects BP from oxidation and contributes to enhanced activity both under λ > 420 nm and λ > 475 nm light irradiation and to long‐term stability. The H₂ production rate of BP/g‐C₃N₄ (384.17 µmol g^?1 h^?1) is comparable to, and even surpasses that of the previously reported, precious metal‐loaded photocatalyst under λ > 420 nm light. The efficient charge transfer between BP and g‐C₃N₄ (likely due to formed N? P bonds) and broadened photon absorption (supported both experimentally and theoretically) contribute to the excellent photocatalytic performance. The possible mechanisms of H₂ evolution under various forms of light irradiation is unveiled. This work presents a novel, facile method to prepare 2D nanomaterials and provides a successful paradigm for the design of metal‐free photocatalysts with improved charge‐carrier dynamics for renewable energy conversion.     

An energy-aware forecasting-based framework using a novel moving coordinate scheme for sensor real-time communications

Wireless Networks - In order to save on the energy expended by a sensor node in its communications with the sink, forecasting-based frameworks have recently been proposed. Those frameworks...     

Rakeness with block sparse Bayesian learning for efficient ZigBee‐based EEG telemonitoring

Future healthcare systems are shifted toward long‐term patient monitoring using embedded ultra‐low power devices. In this paper, the strengths of both rakeness‐based compressive sensing (CS) and block sparse Bayesian learning (BSBL) are exploited for efficient electroencephalogram (EEG) transmission/reception over wireless body area networks. A binary sensing matrix based on the rakeness concept is used to find the most energetic signal directions. A balance is achieved between collecting energy and enforcing restricted isometry property to capture the underlying signal structure. Correct presentation of the EEG oscillatory activity, EEG wave shape, and main signal characteristics is provided using the discrete cosine transform based BSBL, which models the intra‐block correlation. The IEEE 802.15.4 wireless communication technology (ZigBee) is employed, since it targets low data rate communications in an energy efficient manner. To alleviate noise and channel multipath effects, a recursive least square based equalizer is used, with an adaptation algorithm that continually updates the filter weights using successive input samples. For the same compression ratio (CR), results indicate that the proposed system permits a higher reconstruction quality compared with the standard CS algorithm. For higher CRs, lower dimensional projections are allowed, meanwhile guaranteeing a correct reconstruction. Thus, low computational high quality data compression/reconstruction are achieved with minimal energy expenditure at the sensors nodes.     

IoTScal‐H : Hybrid monitoring solution based on cloud computing for autonomic middleware‐level scalability management within IoT systems and different SLA traffic requirements

Wireless sensor network (WSN) technologies have enabled ubiquitous sensing to intersect many areas of modern day living. The creation of these devices offers the ability to get, gather, exchange, and consume environmental measurement from the physical world in a communicating‐actuating network, called the Internet of Things (IoT). As the number of physical world objects from heterogeneous network environments grows, the data produced by these objects raise uncontrollably, bringing a delicate challenge into scalability management in the IoT networks. Cloud computing is a much more mature technology, offering unlimited virtual capabilities in terms of storage capacity and processing power. Ostensibly, it seems that cloud computing and IoT are evolving independently on their own paths, but in reality, the integration of clouds with IoT will lead to deal with the inability to scale automatically depending on the overload caused by the drastic growth of the number of connected devices and/or by the huge amount of exchanged data in the IoT networks. In this paper, our objective is to promote the scalability management, using hybrid mechanism that will combine traffic‐oriented mechanism and resources‐oriented mechanism, with adaption actions. By the use of autonomic middleware within IoT systems, we seek to improve the monitoring components's architectural design, based on cloud computing‐oriented scalability solution. The intention is to maximize the number of satisfied requests, while maintaining at an acceptable QoS level of the system performances (RTT of the system, RAM, and CPU of the middleware). In order to evaluate our solution performance, we have performed different scenarios testbed experiments. Generally, our proposed results are better than those mentioned as reference.     

Bispectral analysis-based approach for steady-state visual evoked potentials detection

Multimedia Tools and Applications - Brain-Computer Interface (BCI) systems are widely based on steady-state visual evoked potentials (SSVEP) detection using electroencephalography (EEG) signals....     

A new thermal infrared and visible spectrum images-based pedestrian detection system

In this paper, we propose a hybrid system for pedestrian detection, in which both thermal and visible images of the same scene are used. The proposed method is achieved in two basic steps: (1) Hypotheses generation (HG) where the locations of possible pedestrians in an image are determined and (2) hypotheses verification (HV), where tests are done to check the presence of pedestrians in the generated hypotheses. HG step segments the thermal image using a modified version of OTSU thresholding technique. The segmentation results are mapped into the corresponding visible image to obtain the regions of interests (possible pedestrians). A post-processing is done on the resulting regions of interests to keep only significant ones. HV is performed using random forest as classifier and a color-based histogram of oriented gradients (HOG) together with the histograms of oriented optical flow (HOOF) as features. The proposed approach has been tested on OSU Color-Thermal, INO Video Analytics and LITIV data sets and the results justify its effectiveness.

     

Color image segmentation by combining the convex active contour and the Chan Vese model

Pattern Analysis and Applications - In this paper, we present a robust and computationally efficient image segmentation technique based on a hybrid convex active contour and the Chan–Vese...