A novel base-prime set was presented generalizing bases of floating point numbers. An asymptotic analysis is presented to reasonably suggest that it might have cardinality strictly between integers and real numbers. This concrete counter-example reasonably suggests that the Continuum Hypothesis (CH) might be false.

This letter proposes a time-efficient algorithm applicable for time-varying wireless networks, which sorts a predefined number of elements in a large data set that are larger or smaller than the other or located between two parts. Based on the mean and standard deviation, a theoretical analysis for Gaussian, uniform, negative exponential, Rayleigh, and unknown distributions is presented, which finds exact and approximate thresholds. Then, the theoretical and numerical analyses show the superiority of the proposed algorithm to the well-known Merge, Quick, and K-S mean-based sorting algorithms in terms of the time complexity, the running time, and the similarity measure.

We consider future cellular networks and study the coexistence of delay-sensitive (DS) and delay-tolerant (DT) devices in machine-type communication (MTC). DS devices require to minimize access delay for their low delay requirements; in contrast, DT devices have flexible delay constraints.

An optimal approach to perform a minimum phase transformation is proposed. As an application a Chebyshev minimum phase filter is designed. Results are presented to show that residual errors are only limited by the finite machine resolution, and 4 orders or magnitude better than current state-of-the-art methodology.

Non-uniform (foveated) 360° image quality assessment using graph convolution network

The conventional deep learning model performs well for traffic flow analysis by training with a large number of labeled data using a one-model-for-one-task approach, leading to huge computational complexity in dynamic intelligent transportation system (ITS) applications. To overcome this limitation, this paper propose a Token-based Self-Supervised Network (TSSN), which can learn TF features in a task-agnostic way, and provide a well bootstrapped pre-training model for a variety of tasks. TSSN tokenizes TF data into segments, each of which is named as a token and comprised of numerous consecutive points. Masked Token Prediction (MTP), a pretext task, is designed to understand the TF correlations by forecasting tokens that are randomly masked. MTP enables TSSN to capture the high-level intrinsic semantics of TF, and provide general-purpose token embeddings. Therefore, TSSN can be more generalized while keeping high performance. As a result, by replacing the final fully-connected layer with a set of untrained new layers and fine-tuning with small-scale task- specific data, TSSN can be deployed for a variety of downstream tasks. The simulation results demonstrate that the TSSN can improve overall performance on various downstream tasks when compared to state-of-the-art models.

A variant of a robust description source coding framework motivated by goal-oriented semantic information transmission is studied here. Considering two individual distortion constraints and input and output data that takes values in finite sets, we prove a general result that provides in parametric form the various cases of optimal solutions of this problem. Then, we derive structural properties of the solution when this achieves the best rates. Capitalizing on these results, we examine the structure of the solution for one case study of general binary alphabets under Hamming distortions and solve in closed form a special case. We also solve another general binary alphabet case where a Hamming and an erasure distortion are used, as a means to highlight the importance of selecting the type of the distortion constraint in the problem.

Unmanned Aerial Vehicles (UAVs) have emerged as suitable platforms for transporting and positioning communications nodes on demand, including Wi-Fi Access Points and cellular Base Stations. This paved the way for the deployment of flying networks capable of temporarily providing wireless connectivity and reinforcing coverage and capacity of existing networks. Several solutions have been proposed for the positioning of UAVs acting as Flying Access Points (FAPs). Yet, the positioning of Flying Communications Relays (FCRs) in charge of forwarding the traffic to/from the Internet has not received equal attention. In addition, state of the art works are focused on optimizing both the flying network performance and the energy-efficiency from the communications point of view, leaving aside a relevant component: the energy spent for the UAV propulsion. We propose the Energy and Performance Aware relay Positioning (EPAP) algorithm. EPAP defines target performance-aware Signal-to-Noise Ratio (SNR) values for the wireless links established between the FCR UAV and the FAPs and, based on that, computes the trajectory to be completed by the FCR UAV so that the energy spent for the UAV propulsion is minimized. EPAP was evaluated in terms of both the flying network performance and the FCR UAV endurance, considering multiple networking scenarios. Simulation results show gains up to 25% in the FCR UAV endurance, while not compromising the Quality of Service offered by the flying network.

Twitter allows its users to share their concerns, ideas and daily activities online. This shared content by individuals provides a rich source of natural occurring data. The rapidity and flexibility of tweets have motivated its use as a tool for mobilizing masses for organizing strikes, protests, and other demonstrations. In this paper, we hypothesized that unrestricted data available on Twitter has potential power to predict day and location of major protests when deep learning and data mining is applied to its unstructured data. The prediction model used in the study is Bidirectional Long Short Term Memory (BiLSTM) Model. The results of our experiment shows that our model performs better than existing baselines models achieving an accuracy of 82.02% on testing data and 95.28% on training data. Overall, the aim of the paper is to study the efficacy of data mining techniques on publicly available data on Twitter in predicting mass protests and demonstrations.

This is the preprint version of ZTA operationalization approaches. We discuss three distinct categories: i) centralized operationalization of ZTA (cZTA), ii) federated operationalization of ZTA (fZTA), and iii) decentralized operationalization of ZTA (dZTA).

The small arms generally capture a significant range of infantry weapons that an individual soldier can carry and operate. Excessive use with inadequate maintenance of these weapons gradually forms abnormalities like corrosion, crack, bulge, and particle deposition on their inner surface. Such abnormalities inspection has been conventionally manual (with open-eyes), tedious, inefficient requiring much effort and time, and susceptible to incorrect detection. Therefore, this paper presents a new software-based inspection system with automatic detection and estimation of the surface abnormality as a timely requirement. The state of abnormalities is determined and characterized for the small arms made of steel that eventually result in different faults and reduce the weapons’ efficiency. A software-based system is designed with a high definition, small-aperture, and low-powered camera to acquire, process, and analyze the inner-surface images in real-time. The surface abnormalities are then determined and estimated according to the detected features of the abnormalities. The proposed system would offer an effortless early-detection and more accurate estimation of the inner-surface abnormalities to ensure appropriate diagnosis and longer lifecycle of the small arms.

The road to extreme low latency urges the invention of intelligent access schemes that depart from the shortcomings associated with the classic ones. In this work, we present a novel traffic prediction and fast uplink framework for IoT devices activated by binary Markovian events. First, we apply the forward algorithm in the context of hidden Markov models (HMM) to predict the hidden states, which are used to schedule the available resources to the devices with maximum likelihood activation probabilities via fast uplink grant. In addition, we evaluate the regret metric as the number of missed resource allocation opportunities to evaluate the performance of the prediction. Next, we formulate a fairness optimization problem to minimize the age of information while keeping the regret as minimum as possible. Finally, we propose an iterative algorithm to estimate the model hyperparameters (activation probabilities) in a real-time application and apply an online-learning version of the proposed traffic prediction scheme. Simulation results show that the proposed algorithms outperform baseline models such as time division multiple access (TDMA) and grant-free (GF) random-access in terms of regret, the efficiency of system usage, and age of information.

This tutorial introduces state-of-the-art methods for tracking multiple spatially extended objects based on unlabeled noisy point clouds, e.g., from radar or lidar sensors. In the first part, the focus lies on tracking a single extended object, i.e., the objective is to simultaneously estimate the shape and position of a moving object based on spatially distributed noisy detections. Model-based approaches for tracking elliptical and star-convex shaped objects are treated. Furthermore, recent learning-based approaches are discussed, which learn the spatial distribution of detections from real data. The second part considers the track management and data association problem, i.e., the initialization and termination of tracks as well as the association of detections to objects. After an in-depth analysis of the data association problem for extended objects, various data association approaches are discussed. Finally, recent frameworks for multiple extended object tracking are introduced such as the random finite set-based Poisson multi-Bernoulli mixture framework.

Abstract—Bulk data transfer applications are greedy in nature in that they have virtually unlimited bandwidth requirements. If serviced by a best effort congestion control, they can create periodic packet loss, long delay and throughput degradation of cross traffic, which may disrupt the normal operation of concurrent QoS sensitive applications. A Lower than Best Effort (LBE) congestion control can make greedy applications only utilize bandwidth left by QoS sensitive applications, thus improve QoS. We propose FlexiS – a Flexible Sender side LBE congestion control mechanism. Unlike most existing LBE congestion controls, which use base delay in incipient congestion detection, FlexiS employs a trend of delay based congestion detector that is more robust to dynamic network environments, in which base delay is difficult to estimate. A non-linear increase and multiplicative decrease controller is used to adapt sending rate by FlexiS. Extensive emulation and Internet tests showed that: (1) FlexiS generally has much lower impact on coexisting best effort traffic than LEDBAT; (2) FlexiS is scalable across a wide range of available bandwidth; (3) aggregate FlexiS traffic can effectively utilize available bandwidth; (4) in most situations, FlexiS flows with the same or different RTTs can fairly share available bandwidth – achieving a fairness index greater than 0.99; (5) the performance of FlexiS is almost not affected by route change and active queue management.

The paper is under final review on IEEE Transaction on Network and Service Management. Innovative traffic engineering functions and services require disrupting routing and forwarding mechanisms to be performed with low overhead over complex network topologies. Source routing (SR) is a prominent alternative to table-based routing for providing the needed expressiveness and agility by reducing the number of network states. This work proposes the M-PolKA, a topology-agnostic multipath source routing scheme and orchestration architecture for reliable communications, which explores special properties from the Residue Number System (RNS) polynomial arithmetic. A P4-based proof-of-concept is experimentally demonstrated using emulated and hardware prototypes. Also, use cases for revealing M-PolKA’s functionalities are tested in different scenarios in order to address problems, such as communication reliability improvement, agile path migration and fast failure reaction. Finally, low overhead for extra functionalities is observed when RNS-based SR is compared to traditional routing approaches.

A surveillance system is an important security aid. In most criminal cases, they become crucial evidence. However, traditional surveillance systems are incapable of preventing loss or crime. Because they only record it, unless someone keeps monitoring it. The human action recognition in this paper overcomes the limitation. Wall climbing, punching, kicking, and falling down were all recognized with a good accuracy rate in the experiment. This technology is ready to support whole-size security systems. It will send messages about detected abnormal action and the location to security staff to take action before it's too late. Furthermore, it can be implemented on an edge-based device at a low cost and does not necessitate a large amount of bandwidth.

Radio frequency-orbital angular momentum (RF-OAM) is a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. Since OAM requires precise alignment of the transmit and the receive antennas, the electronic beam steering approach has been proposed for the uniform circular array (UCA)-based OAM communication system to circumvent large performance degradation induced by small antenna misalignment in practical environment. However, in the case of large-angle misalignment, the OAM channel capacity can not be effectively compensated only by the electronic beam steering. To solve this problem, we propose a hybrid mechanical and electronic beam steering scheme, in which mechanical rotating devices controlled by pulse width modulation (PWM) signals as the execution unit are utilized to eliminate the large misalignment angle, while electronic beam steering is in charge of the remaining small misalignment angle caused by perturbations. Furthermore, due to the interferometry, the receive signal-to-noise ratios (SNRs) are not uniform at the elements of the receive UCA. Therefore, a rotatable UCA structure is proposed for the OAM receiver to maximize the channel capacity, in which the simulated annealing algorithm is adopted to obtain the optimal rotation angle at first, then the servo system performs mechanical rotation, at last the electronic beam steering is adjusted accordingly. Both mathematical analysis and simulation results validate that the proposed hybrid mechanical and electronic beam steering scheme can effectively eliminate the effect of diverse misalignment errors of any practical OAM channel and maximize the OAM channel capacity.

Anomaly detection is a common and critical data mining task, it seeks to identify observations that differ significantly from others. Anomalies may indicate rare but significant events that require action. Market manipulation is an activity that undermines stock markets worldwide. This paper shares five large real-world, labelled data sets of anomalous stock market data where market manipulation is alleged to have occurred. Cutting edge deep learning techniques are then shown to successfully detect the anomalous periods. An LSTM based method with dynamic thresholding is particularly promising in this domain as it was able to identify contextual local anomalies in the data quickly, taking seconds to score two years of trading data for each stock, which can often be a challenge for deep learning approaches.