SEE-TREND: SEcurE Traffic-Related EveNt Detection in Smart Communities

It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. SEE-TREND promotes Smart Mobility by implementing an anonymous, probabilistic collection of traffic-related data from passing vehicles. The collected data are then aggregated and used by its inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic-related information along the roadway to help the driving public make informed decisions about their travel plans, thereby preventing congestion altogether or mitigating its nefarious effects

A Primer on Software Defined Radios

The commercial success of cellular phone systems during the late 1980s and early 1990 years heralded the wireless revolution that became apparent at the turn of the 21st century and has led the modern society to a highly interconnected world where ubiquitous connectivity and mobility are enabled by powerful wireless terminals. Software defined radio (SDR) technology has played a major role in accelerating the pace at which wireless capabilities have advanced, in particular over the past 15 years, and SDRs are now at the core of modern wireless communication systems. In this paper we give an overview of SDRs that includes a discussion of drivers and technologies that have contributed to their continuous advancement, and presents the theory needed to understand the architecture and operation of current SDRs. We also review the choices for SDR platforms and the programming options that are currently available for SDR research, development, and teaching, and present case studies illustrating SDR use. Our hope is that the paper will be useful as a reference to wireless researchers and developers working in the industry or in academic settings on further advancing and refining the capabilities of wireless systems

Spectrum Sensing With Energy Detection in Multiple Alternating Time Slots

Energy detection (ED) represents a low complexity approach used by secondary users (SU) to sense spectrum occupancy by primary users (PU) in cognitive radio (CR) systems. In this paper, we present a new algorithm that senses the spectrum occupancy by performing ED in K consecutive sensing time slots starting from the current slot and continuing by alternating before and after the current slot. We consider a PU traffic model specified in terms of an average duty cycle value, and derive analytical expressions for the false alarm probability (FAP) and correct detection probability (CDP) for any value of K . Our analysis is corroborated with numerical results in which Monte-Carlo simulations are used to determine FAP and CDP to confirm the validity of the analytical expressions obtained and to illustrate that good receiver operating characteristic (ROC) performance is reached for reasonably small values of K

Novel Nonlinear Neural-Network Layers for High Performance and Generalization in Modulation-Recognition Applications

The paper presents a novel type of capsule network (CAP) that uses custom-defined neural network (NN) layers for blind classification of digitally modulated signals using their in-phase/quadrature (I/Q) components. The custom NN layers of the CAP are inspired by cyclostationary signal processing (CSP) techniques and implement feature extraction capabilities that are akin to the calculation of the cyclic cumulant (CC) features employed in conventional CSP-based approaches to blind modulation classification and signal identification. The classification performance and the generalization abilities of the proposed CAP are tested using two distinct datasets that contain similar classes of digitally modulated signals but that have been generated independently, and numerical results obtained reveal that the proposed CAP with novel NN feature extraction layers achieves high classification accuracy while also outperforming alternative deep learning (DL)-based approaches for signal classification in terms of both classification accuracy and generalization abilities.Comment: 6 pages, 7 figures, to be published in IEEE MILCOM 2023: IEEE Military Communications Conference 2023. arXiv admin note: text overlap with arXiv:2211.0023

A Primer on Software Defined Radios

The commercial success of cellular phone systems during the late 1980s and early 1990 years heralded the wireless revolution that became apparent at the turn of the 21st century and has led the modern society to a highly interconnected world where ubiquitous connectivity and mobility are enabled by powerful wireless terminals. Software defined radio (SDR) technology has played a major role in accelerating the pace at which wireless capabilities have advanced, in particular over the past 15 years, and SDRs are now at the core of modern wireless communication systems. In this paper we give an overview of SDRs that includes a discussion of drivers and technologies that have contributed to their continuous advancement, and presents the theory needed to understand the architecture and operation of current SDRs. We also review the choices for SDR platforms and the programming options that are currently available for SDR research, development, and teaching, and present case studies illustrating SDR use. Our hope is that the paper will be useful as a reference to wireless researchers and developers working in the industry or in academic settings on further advancing and refining the capabilities of wireless systems

The management of tubo-ovarian abscesses associated with appendicitis

A relatively uncommon medical complication, the tubo-ovarian abscess (TOA) can result from ascending pelvic infection in the female genital tract. The paper aims at exploring the characteristics of patients with TOA and at comparing the outcomes of the surgical management with postoperative recovery. A retrospective analysis of 25 cases diagnosed with bilateral, right or left TOA at “St. Apostol Andrei” Emergency Clinical County Hospital Constanta, Romania between January 2019 and December 2020 was performed. The patients’ clinical characteristics, age, environment of origin, socio-economic background, educational level, the means of contraception used, surgical procedures, and postoperative recovery were analyzed. Out of the 25 cases, 40% presented bilateral TOA, 32% right TOA and 28% left TOA and left adnexectomy. In 3 patients (37.5%) with right TOA, appendicectomy was also performed. In 72% of the cases, a median pubic-umbilical incision was performed. The remaining 7 patients (28%) with left TOA underwent Pfannenstiel incision. All cases had a favorable postoperative evolution, the patients being discharged 5-9 days after surgery, benefiting from antibiotic therapy 10 days after their discharge. The co-existence of appendicitis and adnexal pathology should raise awareness for adopting a surgical strategy in order to prevent postoperative complications

Three-Event Energy Detection with Adaptive Threshold for Spectrum Sensing in Cognitive Radio Systems

Implementation of dynamic spectrum access (DSA) in cognitive radio (CR) systems requires the unlicensed secondary users (SU) to implement spectrum sensing to monitor the activity of the licensed primary users (PU). Energy detection (ED) is one of the most widely used methods for spectrum sensing in CR systems, and in this paper we present a novel ED algorithm with an adaptive sensing threshold. The three-event ED (3EED) algorithm for spectrum sensing is considered for which an accurate approximation of the optimal decision threshold that minimizes the decision error probability (DEP) is found using Newton’s method with forced convergence in one iteration. The proposed algorithm is analyzed and illustrated with numerical results obtained from simulations that closely match the theoretical results and show that it outperforms the conventional ED (CED) algorithm for spectrum sensing

ON THE UPLINK-DOWNLINK DUALITY FOR GAUSSIAN VECTOR CHANNELS WITH COLORED NOISE AND APPLICATIONS TO CDMA TRANSMITTER ADAPTATION

ABSTRACT In this paper we study the uplink-downlink duality for Gaussian vector channels with colored noise, and we derive the duality transformations (downlink-to-uplink and vice versa) that imply transmit covariance matrices for which user rates in uplink and downlink are identical. These transformations are then applied for transmitter adaptation in Code Division Multiple Access (CDMA) systems to obtain an ensemble of downlink CDMA codewords from an optimal ensemble of uplink codewords

Dynamic Adaptation of Joint Transmission Power and Contention Window in VANET

In this paper, we propose an algorithm for joint adaptation of transmission power and contention window to improve the performance of vehicular network in a cross layer approach. The high mobility of vehicles in vehicular communication results in the change in topology of the Vehicular Ad-hoc Network (VANET) dynamically, and the communication link between two vehicles might remain active only for short duration of time. In order for VANET to make a connection for long time and to mitigate adverse effects due to high and fixed transmission power, the proposed algorithm adapts transmission power dynamically based on estimated local traffic density. In addition to that, the prioritization of messages according to their urgency is performed for timely propagation of high priority messages to the destination region. In this paper, we incorporate the contention based MAC protocol 802.11e enhanced distributed channel access (EDCA) mechanism to implement a priority-based vehicle-to-vehicle (V2V) communication. Simulation results show that the proposed algorithm is successful in getting better throughput with lower average end-to-end delay than the algorithm with static/default parameters