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Recent advancements in communication technologies have significantly enhanced localization techniques, improving both accuracy and operating modes. Initially, localization methods relied on global navigation satellite systems, offering high accuracy but proving inefficient in Non-Line-of-Sight scenarios. Furthermore, the absence of a passive mode, where the user can be localized without explicitly requesting it, renders these methods unsuitable for applications like passive tracking systems. Fingerprinting methods, a pattern matching techniques based on signal power estimation from target devices and distance estimation from reference points, can be seen as a valid and promising alternative. However, these methods face limitations due to extensive measurement campaigns needed to establish accurate sampling systems within specific areas and the substantial amount of data required for machine learning algorithms to achieve optimal performance. This study introduces a novel fingerprinting method capable of passive operation, involving all smartphones within a designated area, suitable for both indoor and outdoor scenarios. The proposed solution leverages Generative Adversarial Networks (GANs) to augment fingerprinting datasets, enhancing machine learning models’ capabilities. Additionally, the offline phase’s cost-effectiveness is improved by integrating a Bayesian system as a secondary machine learning component.

Monitoring rainfall is becoming increasingly important due to the impacts of climate change. In this context, opportunistic sensing based on satellite microwave links (SMLs) is gaining significant interest for its potential to provide real-time, low-cost, and valuable information. In this work, we deployed a network of 26 SML sensors over a survey territory corresponding to a catchment area of 140 km2, and the data collected from it were used to train two machine learning (ML) algorithms and a statistical method to predict wet/dry conditions. Additionally, a voting mechanism based on the majority vote of the three classifiers enhances the performance of the individual methods. The results show that ML algorithms are valuable solutions for real-time monitoring of rainfall, even in an extended sensor network. The voting mechanism improves the prediction of low precipitation at the expense of a higher false positive rate.

Owing to the geographically scattered end devices (EDs) in long-range wide area networks (LoRaWAN), devices that combat challenging cyber threats and attacks are of critical significance. In this perspective, LoRa Alliance® is continuously evolving the security of LoRaWAN and recently introduced a new version i.e., LoRaWAN 1.1x that is featured with security improvement. However, the wireless nature of LoRaWAN implementation still leaves it vulnerable to security breaches that compromise its integrity. Several problems have been pinpointed in the newer version such as one issue with key distribution in LoRaWAN 1.1 is that the keys are often pre-installed on the devices at the time of manufacturing. It can introduce security risks if the keys are not adequately protected or if the devices are compromised before they are deployed. In other words, the pre-installed keys may not be updated regularly, which can also introduce security risks. Thus, the keys need to be handled securely to maintain the security of the network and the over-the-air firmware updates feature could introduce new security challenges for the key distribution. This paper presents a key generation and distribution (KGD) mechanism that securely exchanges the root key between the ED and the application server (AS). The KGD protocol provides authentication by integrating Advanced Encryption Standard (AES-128) in addition to a secure hash function known as Argon2. The proposed protocol utilizes Elliptic-Curve Diffie-Hellman (ECDH) key exchange method that makes the protocol resilient to cyber threats. The ECDH algorithm exchanges the keys on the insecure channels and is, therefore, vulnerable to Man-in-the-Middle (MITM) attacks in the network. Therefore, to validate the key agreement and avoid adversaries, the KGD protocol considers the Elliptic Curve Digital Signature Algorithm (ECDSA) that authenticates and allows legitimate instances in the network. In last, a formal security analysis using the Scyther tool validates the security enhancement of the KGD protocol.

The rapid increase in Internet of Things (IoT) applications has raised security and privacy issues due to the huge amount of data acquired by IoT devices and transmitted through the Internet. Therefore, there is a need to understand what strategies should be applied to make IoT systems robust to security flaws and privacy weaknesses. In this paper, we first identify and discuss the best practices for IoT privacy and security, which include a set of procedures that can be taken as the guidelines to determine and solve privacy and security issues of IoT systems. Then, we follow and apply the identified best practices to two real IoT-based use cases: a crowding monitoring system and a vehicular mobility system. Finally, we computed the risk assessment score to evaluate the impact of the application of the identified best practices on the implemented IoT systems. We observe that following the proposed best practices the implemented IoT systems achieve an overall risk score of 1.3, which is from 215% to 361% lower than that achieved by comparable IoT systems proposed in the literature studies.

In the dynamic fields of semiconductor design and digital system development, effective verification procedures are in high demand. In particular, functional verification is essential for space systems to guarantee mission success, prevent errors in intricate environments, and uphold the reliability vital for effective space exploration. Testing phase results extremely challenging because of the high complexity of systems, often due to the intricate interplay of hardware, software, and environmental factors, impeding comprehensive validation. This paper presents an innovative methodology for the functional verification of systems involved in space telecommunications: this is based on the development and integration of software Twin Models within advanced and automated verification environments, with the dual purpose of simplifying the stimulation of the Design Under Test (DUT) and producing instant-by-instant expected behavior. The proposed solutions are developed in SystemVerilog Hardware Verification Language (HVL) and are fully compliant with Universal Verification Methodology (UVM) standard. As proof of the effectiveness and applicability of this approach, examples of verification environments for different systems are given, including high-speed interfaces, such as SpaceWire (SpW) CODECs, and high-speed encoders and modulators for both Direct-to-Earth and inter-satellite communications applications. A test campaign was conducted for all these systems, and using the Twin Models made it possible to achieve 100% of both functional and code coverage. The final result is a significant simplification in the creation of tests and DUT debugging and a very highly flexible, self-checking, and automated verification environment able to test any possible DUT configuration efficiently.

Recent research has found that neural networks for computer vision are vulnerable to several types of external attacks that modify the input of the model, with the malicious intent of producing a misclassification. With the increase in the number of feasible attacks, many defence approaches have been proposed to mitigate the effect of these attacks and protect the models. Mainly, the research on both attack and defence has focused on RGB images, while other domains, such as the infrared domain, are currently underexplored. In this paper, we propose two attacks, and we evaluate them on multiple datasets and neural network models, showing that the results outperform others established attacks, on both RGB as well as infrared domains. In addition, we show that our proposal can be used in an adversarial training protocol to produce more robust models, with respect to both adversarial attacks and natural perturbations that can be applied to input images. Lastly, we study if a successful attack in a domain can be transferred to an aligned image in another domain, without any further tuning. The code, containing all the files and the configurations used to run the experiments, is available https://github.com/jaryP/IR-RGB-domain-attackonline.

The wireless power transfer systems represent a viable solution to make more user friendly the procedure of charging the battery of electric vehicles. The performances of these systems are greatly affected by their compensation networks and are usually studied by considering the first harmonic equivalent circuit of the system. This paper presents a new approach to writing the equations that describe these circuits and expresses them in a form that highlight the contribution of the coupled coils and of their compensation networks. The procedure to obtain the equations is described in details and the equations themselves are analyzed to derive from them the qualitative characteristics of the wireless power transfer system. In the last part of the paper a numerical analysis of a study case is reported, supporting the results by means of simulations.

This paper proposes, for the first time, a completely stand alone avalanche diode model based on a C++ code. The code, implemented within the Advanced Design Systems (ADS) environment, describes the diode behavior in terms of impedance and noise, by means of equations for which no more look-up tables are required. This also contains a modification with respect to the original Gilden and Hines model. Although completely general, the model is presented and discussed using a 20 square microns p-i-n diode, previously published by the authors. In order to validate the model in complex systems, the aforementioned diode is placed within two circuits of practical interest that have been designed using a commercial 130-nm SiGe BiCMOS technology. In particular: (a) the diode is connected with a wide-band bias tee and an 6-dB attenuator, in order to create a Noise Source (NS) system block, and (b) the same NS structure is connected with an Low-Noise Amplifier (LNA). The two prototypes have been fabricated and experimentally characterized in terms of impedance and Excess Noise Ratio (ENR). Finally the experimental results have been compared with model simulations. In the frequency range from DC to 26.5 GHz the agreement between simulated ENR and experiments is within 1.4 dB for the prototype (a) and 1.9 dB for the prototype (b). The obtained results enable the adoption of avalanche noise diodes in Computer Aided Design (CAD) tools, for the prediction of integrated circuits performances, and confirms the reliability of the developed model.

The converter switching in variable-frequency drives can generate high frequency common mode voltage between the machine winding and the converter ground, leading to high frequency parasitic currents which can flow through the machine bearings unless precautions are taken in design and installation. These parasitic and unintended currents in the bearings cause deterioration of the lubrication film and surface damage to the rolling elements of the bearings. These problems will be exacerbated as wide-bandgap semiconductors with faster switching rise times start becoming more widespread in variable-frequency drives. This paper reviews the modelling and mitigation techniques of high frequency bearing currents in inverter fed AC drives. It aims to provide a solid base for the research community to further understanding the bearing currents phenomenon and helping to find novel improved technique for their mitigation and measurement.

Self-aware robots rely on depth sensing to interact with the surrounding environment, e.g. to pursue object grasping. Yet, dealing with tiny items, often occurring in industrial robotics scenarios, may represent a challenge due to lack of sensors yielding sufficiently accurate depth measurements. Existing active sensors fail at measuring details of small objects (< 1cm) because of limitations in the working range, e.g. usually beyond 50 cm away, while off-the-shelf stereo cameras are not suited to close-range acquisitions due to the need for extremely short baselines. Therefore, we propose a framework designed for accurate depth sensing and particularly amenable to reconstruction of miniature objects. By leveraging on a single camera mounted in eye-on-hand configuration and the high repeatability of a robot, we acquire multiple images and process them through a stereo algorithm revised to fully exploit multiple vantage points. Using a novel dataset addressing performance evaluation in industrial applications, our Single camera Stereo Robot (SiSteR) delivers high accuracy even when dealing with miniature objects. We will provide a public dataset and an open-source implementation of our proposal to foster further development in this field.

Integrated noise sources (or hot loads) are essential to enable precise gain and noise figure Built-In Test Equipment (BITE) measurements. The present paper describes a millimeter-wave, solid-state noise source implemented in a standard, 130-nm Silicon-Germanium (SiGe) Bipolar-Complementary Metal Oxide Semiconductor (BiCMOS) process. This device is based on a p-i-n (varactor) diode that has two states: a cold state, when it is off, and an hot state when the diode is driven into avalanche breakdown. Two noise diodes with 10 and 20 square microns area have been fabricated and experimentally characterized. The measurements highlight a breakdown voltage is close to 10.7 V, whereas Excess Noise Ratio (ENR) equal to 16 dB (10 square microns diode) and 19 dB (20 square microns diode) are observed at 40 GHz, for a current density of 0.1 mA per square micron. For the first time the ENR is studied as a function of the physical device temperature, showing a slight decrease of -0.008 dB/K as the temperature increases from 298 to 358 K. An accurate modeling of the noise source is finally provided through a small-signal equivalent circuit that can be easily implemented into Computer Aided Design (CAD) tools. This contains some modifications with respect to the original Gliden and Hines model. The obtained results enable the employment of p-i-n avalanche noise diodes for the automatic characterization of integrated circuits in the production environment, as well as for the calibration of millimeter-wave receivers and radiometers during their operational life.

The notion of k-truss has been introduced a decade ago in social network analysis and security for community detection, as a form of cohesive subgraphs less stringent than a clique (set of pairwise linked nodes), and more selective than a k-core (induced subgraph with minimum degree k). A k-truss is an inclusion-maximal subgraph H in which each edge belongs to at least k - 2 triangles inside H. The truss decomposition establishes, for each edge e, the maximum k for which e belongs to a k-truss. Analogously to the largest clique and to the maximum k-core, the strongest community for k-truss is the max-truss, which corresponds to the k-truss having the maximum k. Even though the computation of truss decomposition and of the max-truss takes polynomial time, on a large scale, it suffers from handling a potentially cubic number of wedges. In this paper, we provide a new algorithm FMT, which advances the state of the art on different sides: lower execution time, lower memory usage, and no need for expensive hardware. We compare FMT experimentally with the most recent state-of-the-art algorithms on a set of large real-world and synthetic networks with over a billion edges. The massive improvement allows FMT to compute the max-truss of networks of tens of billions of edges on a single standard server machine.

Today's stream processing systems handle high-volume data streams in an efficient manner. To achieve this goal, they are designed to scale out on large clusters of commodity machines. However, despite the efficient use of distributed architectures, they lack support to co-processors like graphical processing units (GPUs) ready to accelerate data-parallel tasks. The main reason for this lack of integration is that GPU processing and the streaming paradigm have different processing models, with GPUs needing a bulk of data present at once while the streaming paradigm advocates a tuple-at-a-time processing model. This paper contributes to fill this gap by proposing Gasser, a system for offloading the execution of sliding-window operators on GPUs. The system focuses on completely general functions by targeting the parallel processing of non-incremental queries that are not supported by the few existing GPU-based streaming prototypes. Furthermore, Gasser provides an auto-tuning approach able to automatically find the optimal value of the configuration parameters (i.e., batch length and the degree of parallelism) needed to optimize throughput and latency with the given query and data stream. The experimental part assesses the performance efficiency of Gasser by comparing its peak throughput and latency against Apache Flink, a popular and scalable streaming system. Furthermore, we evaluate the penalty induced by supporting completely general queries against the performance achieved by the state-of-the-art solution specifically optimized for incremental queries. Finally, we show the speed and accuracy of the auto-tuning approach adopted by Gasser, which is able to self-configure the system by finding the right configuration parameters without manual tuning by the users.