Miracle-dz/UniFAR-FTUs huggingface dataset:
Breath Status Tracking Using Commodity WiFi

In this paper, we propose a contact-free breath tracking system, BreathTrack, to track the status of breath using the off-the-shelf WiFi devices by exploiting the phase variation of the channel state information(CSI). BreathTrack utilizes a reference antenna connected from the transmitter to resolve the phase distortions introduced by the hardware imperfection. Moreover, BreathTrack utilizes the sparse recovery method to find the dominant path in the multipath indoor environment and derive the corresponding complex attenuation coefficient. Then, the phase variation of the complex attenuation coefficient is utilized to extract the detailed breath status and the breath rate. Extensive experiments are conducted to show that BreathTrack could estimate the breath rate with the median accuracy of over 99\% in most scenarios, and could track the detailed status of breath directly using the raw phase variation.
svLoad: An Automated Test-Driven Architecture for Load Testing in Cloud Systems

Nowadays, Internet-based technologies possess immense processing power, capacity, flexibility, and are gradually moving towards a service- oriented functionality in order to build new distributed storage systems in the cloud. Several distributed systems are currently running in different geographically located data centers for successful deployment of modern web and social services such as Facebook, Twitter, ringID, etc. Both cache and backend servers in such distributed systems must be functional and reliable for incoming workloads by means of efficient allocation of capacity along with proper configuration and tuning of multiple system resources. To address these challenges, in this paper, we propose a test-driven automated architecture for load testing, named as 'svLoad' to compare the performance of cache and backend servers. Here, we designed test cases considering diversified real scenarios such as different protocol types, same or different URLs, with or without load, cache hit or miss, etc. using tools namely JMeter, Ansible, and some custom utility bash scripts. To validate the efficacy of our proposed methodology, we conduct a set of experiments by running these test cases over a real private cloud development setup using two open source projects - Varnish as the cache server and OpenStack Swift as the backend server. Our focus is also to find out bottlenecks of Varnish and Swift by executing load requests, and then tune the system based on our load test analysis. After successfully tuning the Swift, Varnish, and network system, based on our test analysis, we were able to improve the response time by up to 80%.
Robust Beam Management for Mobility in mmWave Systems

Proactive communication among vehicles and infrastructure units is an essential ingredient to enable fully autonomous driving. 5G NR is a candidate technology to power the technical and functional requirements for vehicle-to-everything (V2X) communication, through enhanced resource allocation and usage of mmWave spectral channels. Beam acquisition and tracking is crucial for successful operation in mmWave V2X. Traditional beam sweep strategies to achieve beam alignment suffer, however, from large overhead and latency that renders them impractical to support mobility applications. In this paper, we explore robust communication for NR V2X. We argue that imposing a hierarchical structure at the sidelink through the election of a local node scheduler is important for efficient mobility management. We further focus on the feasibility of mmWave V2X, and present field measurement results that show that mmWave V2X are capable of achieving long ranges and are generally robust to blockage, even with a wide beam at the transmitter. Building on the measurement results, we discuss key beam management building blocks for mmWave V2X that take the antenna architecture, and the stringent mobility requirements into consideration, to achieve beam alignment.
Downlink NOMA for Stochastic Cellular Networks under Millimeter Wave Channels

Non-orthogonal multiple access (NOMA) and millimeter wave communications are key technologies for the fifth generation (5G) of cellular networks in order to increase the spectral efficiency, ensure massive connectivity, and to provide large bandwidths to mobile internet services. However, the coexistence of these two techniques is critical, especially under increasingly random dense base station assignments. To this end, this paper characterizes the system coverage for users utilizing NOMA in a stochastic multi cell set-up under millimeter wave frequencies. We employ the Poisson point process (PPP) to model the base stations and user devices. Moreover, our system model considers the power allocation, imperfections in the successive interference cancellation (SIC), and blockages that affect the channel path loss and fading characteristics. The aggregate co-channel interference experienced at a user is characterized based on the moment generating function, and the outage probability is derived for a two user NOMA scenario. Analytical and numerical results show that NOMA is a feasible candidate technique to increase the spectral efficiency under millimeter wave channels if careful power allocation is implemented, with limited performance penalties for each user. It is also shown that the performance is further improved by utilizing a dense base station deployment.
5G Multi-Connectivity with Non-Ideal Backhaul: Distributed vs Cloud-Based Architecture

We investigate the throughput performance of multi-connectivity over Heterogeneous Networks (HetNets). Specifically, we examine the effect of non-ideal backhaul on the throughput for two architecture scenarios, namely: i) The distributed HetNet scenario where the multi-connectivity anchor is co-located with the macro cell; ii) the cloud-based HetNet scenario, where the multi-connectivity anchor point is located at a centralized network point. An extensive set of simulations is conducted, followed by an investigation of the effect of the backhaul latency, packet file size and offered load on the throughput. It is shown that the cloud-based HetNet architecture leads to a superior throughput performance than that of the distributed architecture. Moreover, it is shown that the backhaul delay considerably affects the overall throughput for both architecture options.
Multi-User Computation Offloading with D2D for Mobile Edge Computing

With the emergence of mobile edge computing (MEC), mobile users are able to process various tasks by offloading large-computation-demanding tasks to MEC server located at the edge of the network. As computation offloading requires communication between mobile users and the MEC server, an efficient computation offloading scheme which decreases both task executive delay and transmission energy consumption of mobile users plays a key role in MEC. Motivated by this, we study the computation offloading scheme in a novel MEC system where mobile users can offload tasks to the MEC server or a distributed computing node (DCN). As mobile users' offloading scheme affects the delay and energy consumption each other, we show that the offloading decision-making problem of users can be formulated as a sequential game. In particular, we demonstrate that the Nash equilibrium of the game exists which manifests that the system can converge to a stable status. A multi-user and multi-destination computation offloading scheme is also proposed to achieve the Nash equilibrium. Simulation results show that the proposed computation offloading scheme can significantly decrease the task execution delay as well as the energy consumption of mobile users.
Temperature and Power Aware Server Placement Optimization for Enterprise Data Center

In data center, the optimal server location selection is related to many factors, such as power, temperature and space location. In order to reduce the local hot point in computer room, decrease the energy consumption of cooling system and eliminate the power fragment of rack, this paper studies the optimization issue of server placement for the enterprise data center based on the joint perception of temperature and power. First, based on the deep learning algorithm, the model of the power, temperature and cooling power of equipment in computer room is established. The power curve model of the server is estimated by using the Conditional Generative Adversarial Network (CGAN), and then the optimal placement position of server is calculated by genetic algorithm. Through simulation experiments, it is proved that the method proposed in this paper is stable and feasible. Compared with the three placement algorithms, this method can reduce the cooling energy consumption by 4-6% and reduce the new power demand of 6-9%, which can effectively improve the energy consumption efficiency of data center.
Partnering mechanism in construction: An empirical study on the Chinese construction industry

Partnering and its principles have increasingly been introduced to the construction industry to improve the efficiency of project delivery. However, little research outlines the mechanism behind its application. This paper presents the findings of a study that was conducted to develop and test a partnering model that reveals the relationships between the critical success factors (CSFs) of partnering and demonstrates their importance to construction. With support of data collected from the Chinese construction industry, this study has revealed strong correlations among partnering CSFs, risk management, total quality management (TQM), use of incentives, and project performance. It is concluded that project success is the outcome of the interaction between a variety of techniques, and that partnering, associated with incentives, is a basic management method through which risk management and TQM can be strongly improved.
Concurrent Hybrid Breadth-First-Search on Distributed PowerGraph for Skewed Graphs

Large-scale graph-structured computation is becoming increasingly important for various data analytics applications. However, most distributed graph processing frameworks do not directly support efficient implementation of sophisticated algorithms requiring massive graph traversals. In this paper, we propose concurrent hybrid breadth-first-search (BFS) algorithm on a popular distributed graph processing framework (Power-Graph and its optimized version PowerLyra). It leverages the small-world property of skewed graphs which apply to most realworld data sets. Hybrid BFS algorithm changes graph traversal direction to save computational workload and message transmissions. Concurrent BFS algorithm enables running multiple BFS simultaneously while sharing vertex explorations efficiently with bit operations. Extensive experiments are conducted to evaluate performance and scalability on a multi-core computer and a cluster of AWS instances. Concurrent hybrid BFS algorithm dramatically increases graph traversal efficiency with respect to the increasing number of concurrent BFS traversals. Compared with PowerGraph, PowerLyra uses fewer resources, while achieving significantly higher performance and better scalability.
A Logic Range-free Algorithm for Localization in Wireless Sensor Networks

Nowadays, localization is a very important research problem in the context of Wireless Sensor Networks (WSNs). These networks are made by nodes connected to each other wireless and able to collect data: all the retrieved information can be then held by a central unit, a more performance machine, which can in turn collect and elaborate all the data. The main contribution of this work is in an innovative way to recognize the position of a point in a certain closed environment, by exploiting the RSSI (Received Signal Strength Indicator) and a logic approach: for this purpose, Prolog has been used in order to describe an intuitive non-greedy algorithm and an appropriate simulation program able to make an estimation of a point localization, providing the global optimum. To this aim, the placement of hub in the interesting area is crucial and, in particular, when this area presents some obstacles which can alter the transmission signal. The expressive power of Prolog and the way the its logic engine works made this programming language suitable for our purpose: in fact, the backtracking strategy opportunely reflects the way the relative positioning of hubs is performed with the aim of improving the cover of a specific indoor area at each step, which is a very important application of the localization problem. Key-words: Wireless Sensor Networks, localization, coverage, Prolog
Edge Caching via Content Offloading in Heterogeneous Mobile Opportunistic Networks

Content transmission over Mobile Opportunistic Networks is in the manner of “store-carry-forward” due to opportunistic contacts between node pairs. Most of the existing works focus on the “forward” process rather than how to “store” content in the network. Moving contents to the edge of network can significantly offload network traffic while satisfying content requests from mobile users locally. In this paper, considering the preference of nodes for different content items, we propose Fetcher Selection Greedy Algorithm (FSGA), a novel strategy to select a subset of nodes for cooperative caching scheme by evaluating each node utility in the network. To improve the cooperative caching opportunity and reduce the traffic pressure of core network, we introduce the Heterogeneous Mobile Opportunistic Networks (HMONs)architecture by deploying an Access Point (AP)which is acting as a bridge to connect the core network and mobile nodes. The mobile nodes in HMONs are segmented into two kinds, one is called $Fetchers$ which are responsible for caching content from AP then forwarding them to the other ones, and the other mobile nodes are $Regulars$. We formulate and analyze the average content transmission delay in different transmission conditions. Finally, our experiment results indicate that the cooperative caching scheme improves the network performance.
Efficient Reconfigurable Hardware Core for Convolutional Neural Networks

The Convolutional Neural Network (CNN) is one of the most promising methods in modern machine learning, but its intensive requirement of computing resources limits the application on embedded systems. Since the energy consumption of a CNN is dominated by convolutions, methods such as Winograd and fast FIR algorithms (FFA) are introduced to reduce the computation complexity of convolutions. However, hardware implementations of these algorithms suffer from the reduction of efficiency when processing different CNN models, because their fixed architectures can not efficiently support all sizes of convolution kernels. In this paper, for the first time, we propose an FFA-based all-size Reconfigurable Convolution Core (RCC) to tackle this problem. The proposed RCC can efficiently perform 5 mainstream sizes of convolution kernels, while achieving significant computation complexity reduction compared with the conventional convolution architecture. Considering the strict resource budget of embedded systems, we explore a large design space to obtain an optimal tradeoff between hardware utilization and reconfigurability. Moreover, we propose an overlapping dataflow scheme for the RCC to reduce the workload of the communication bandwidth. The synthesis result shows that the proposed design can run over 600MHz.
A New Preamble Design for Channel Estimation in Offset QAM Filter Bank Multicarrier Systems

In this paper, we propose a new preamble design for channel estimation in filter bank multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM). The proposed preamble structure is formed by duplicating a preamble unit (PU) defined as a 3x3 symbol matrix in a time-frequency lattice. The center element of the PU could be a real symbol "1" surrounded appropriately by imaginary symbols "j" and "-j" such that the mean-squared error (MSE) of channel estimation is minimized for the center subcarrier. The center element could also be an imaginary symbol "j" with surrounding symbols set as "1" or "-1" in a similar way for MSE minimization. The proposed preamble design can be regarded as an improved version of the extended interference approximation method with real or imaginary symbols (E-IAM-C) for channel estimation in FBMC&#x002F;OQAM systems. With the estimated channel gains of all center subcarriers, we can calculate the remaining subcarriers' channel gains simply by linear interpolation. Theoretical analysis and simulation results show that the proposed preamble design for channel estimation outperforms E-IAM-C in terms of the MSE, bit error rate, and computational complexity.
28 GHz Foliage Propagation Channel Measurements

This paper presents results from channel sounding campaigns that investigate the impact of foliage blockage on the wireless propagation channel characteristics at 28 GHz. The measurements are performed with a real-time channel sounder equipped with phased array antennas that allow beamforming and electronic beam steering for directionally resolved measurements. Thanks to the short measurement time and the excellent phase stability of the system, we obtain both directional and omnidirectional channel power delay profiles without any delay uncertainty. We derive models for the distance-dependent foliage attenuation for different receiver heights. Additionally, we compare the angular spread and delay spread for links with and without foliage blockage to understand the effects of foliage on all channel characteristics.
Graceful Quality Improvement in Wireless 360-Degree Video Delivery

In 360-degree video streaming, a user may watch a part of video frames, namely, viewport, through an interactive display, e.g., head-mounted display, for the immersive experience. One of major issues for high-quality 360-degree video delivery is how to reduce perceptual redundancy in video frames according to user's viewing viewport. To this end, an existing scheme divides video frames into multiple tiles and adaptively encodes each tile based on user's viewing viewport using digital-based video compression. However, the video compression causes an issue in wireless channel called cliff effect, which causes sudden quality degradation in user's viewport and poor immersion in immersive applications. To reduce perceptual redundancy without cliff effect in wireless 360-degree video delivery, we propose a novel transmission scheme. Our scheme skips quantization and entropy coding, instead, directly transmits linear-transformed signals based on three-dimensional discrete cosine transform (3D-DeT) or the combination of one-dimensional DeT (1D-DeT) and spherical wavelet transform (SWT). By skipping both operations, the quality of viewport can be simply enhanced by its power allocation since it is directly applied at the pixel level instead of the bit level. It is demonstrated with 360-degree video sequences that the proposed scheme offers a great advantage over the conventional digital-based schemes.
The Impact of Long Dead Time on the Photocount Distribution of SPAD Receivers

A single photon avalanche diode (SPAD) receiver can provide a significantly improved detection sensitivity over conventional photodiodes. However, upon detecting a photon, the SPAD receiver is unable to respond to subsequent incident photons for a certain period of time called dead time. When the SPAD receiver is counting in consecutive intervals and its dead time is relatively long compared to the counting interval, the dead time in one interval may overlap the next intervals, causing counting losses in those intervals. In this paper, the photocount statistics of an active quenching SPAD receiver with a long dead time is investigated. While detecting in consecutive time intervals, the SPAD receiver can be modeled as a Markov chain. It is shown that the SPAD experiences a transient period, before eventually reaching its steady state. The duration of the transient period increases with both the dead time and the signal photon rate. The bit error performance of the system with an on-off keying (OOK) modulated signal is also evaluated. Our results show that the SPAD's dead time significantly degrades the error performance of high data rate links.
Blockchain Based Data Integrity Verification in P2P Cloud Storage

With the popularity of cloud storage, how to verify the integrity of data on the cloud has become a challenging problem. Traditional verification framework involves the Third Party Auditors (TPAs) which are not entirely credible. In this paper, we present a framework for blockchain-based data integrity verification in P2P cloud storage, making verification more open, transparent, and auditable. In this framework, we present Merkle trees for data integrity verification, and analyze the system performance under different Merkle trees structures. Furthermore, we develop rational sampling strategies to make sampling verification more effective. Moreover, we discuss the optimal sample size to tradeoff the conflict between verification overhead and verification precision, and suggest two efficient algorithms of order of verification. Finally, we conduct a series of experiments to evaluate the schemes of our framework. The experimental results show that our schemes can effectively improve the performance of data integrity verification.
Comparison of Hilbert Vibration Decomposition with Empirical Mode Decomposition for Classifying Epileptic Seizures

Epilepsy is a frequently seen neurological disorder manifested by repeating seizures. EEG signals of epilepsy patients are able to depict these seizures due to their high temporal resolution. However, it is generally challenging to differentiate these seizures by manual observation. Furthermore, Fourier based signal processing methods are unable to sufficiently analyze EEG signals as they are nonlinear and nonstationary by nature. Therefore, methods such as empirical mode decomposition (EMD) are exploited when working on epileptic EEG signals with the intention of detecting epileptic seizures. In this paper, we propose a framework in order to extract features from healthy, interictal and ictal EEG signals decomposed via EMD and Hilbert vibration decomposition (HVD), and then classify these signals with a convolutional neural network (CNN). Then, we evaluate the performance of both decomposition methods in detecting epileptic seizures. The obtained features are used for 10-fold cross validation with a CNN. The study was conducted on a benchmark dataset, where the EMD yielded 95.11% classification accuracy while HVD method achieved 100% accuracy. The overall performance of the HVD was found better compared to the EMD.
Dual Polarized Spatial Modulation for Land Mobile Satellite Communications

In this paper, two novel high-rate multiple-input multiple-out (MIMO) transmission schemes are proposed for dual polarized land mobile satellite (LMS) communications, where the polarization dimension and spatial dimension are jointly exploited to convey information. To be specific, we first extend the concept of polarized modulation (PMod) from single antenna LMS systems to multi-antennas LMS systems, which utilizes the parallel polarization states for bit mapping. This new scheme, namely generalized PMod (GPMod), is capable of improving the transmission rate linearly with the number of transmit antennas. For high-rate scenarios, the proposed GPMod needs to deal with the interference introduced by cross polarization, which may degrade the system performance. In order to circumvent the cross-polarization interference, we employ the concept of index modulation to both polarization dimension and spatial dimension and proposed a novel interference-free scheme, namely polarized spatial modulation (PMod-SM). Finally, an upper bound for the average bit error probability (ABEP) of the proposed scheme is derived for the generalized spatially correlated Rician channel model. Both analytical and simulation results are presented to demonstrate that the proposed PMod-SM scheme is capable of achieving considerable performance gains compared to the conventional PMod and the GPMod schemes.