The widespread adoption of masks and respirators has significantly influenced various aspects of society, driving technological advances to improve comfort, efficiency, and sustainability. The COVID-19 pandemic underscored their essential role in the protection of public health, with continued relevance in the industrial, environmental, and hygiene-critical sectors. Recent developments in deep learning offer promising approaches for building automated systems that can detect mask and respirator usage. In this regard, this paper first aims to present an improved version of the Facial Masks and Respirators Database (FMR-DB), which can be used to create such systems. New features include a significant increase in available images, which has been expanded from 2565 to 4200 images, and the addition of You Only Look Once (YOLO), PASCAL Visual Object Classes (PascalVOC), and Common Objects in Context (COCO) labeling for image detection tasks. Furthermore, image classification and object detection tests were conducted using Convolutional Neural Networks (CNNs), Transformers, and YOLO to determine the types of masks and respirators accurately. Finally, to the best of the authors' knowledge, these tools were used for the first time to analyze the protection levels of respirators automatically. The results provide valuable insights for developing efficient and reliable automatic recognition systems.

Low-cost light-scattering particulate matter sensors are often advocated for dense monitoring networks. Recent literature has focused on evaluating their performance. Nonetheless, low-cost sensors are also considered unreliable and imprecise. Consequently, exploring techniques for anomaly detection, resilient calibration, and improvement of data quality should be more discussed. In this study, we analyze a year-long acquisition campaign by positioning 56 low-cost light-scattering sensors near the inlet of an official particulate matter monitoring station. We use the collected measurements to design and test a data processing pipeline composed of different stages, including fault detection, filtering, outlier removal, and calibration. These can be used in large-scale deployment scenarios where the quantity of sensors data can be too high to be analyzed manually. Our framework also exploits sensor redundancy to improve reliability and accuracy. Our results show that the proposed data processing framework produces more reliable measurements, reduces errors, and increases the correlation with the official reference.

The Morra game is quite old. Back in time, traces of it can be found in ancient Egypt, ancient Rome, and even China. It involves two players who, for a limited number of turns, must try to suppose the sum of the number personally chosen with the number chosen by the opponent. The rules are simple, but it is rather difficult to play at a high level as there are multiple cognitive, motor, and perceptual processes involved.The goal of this paper is to illustrate the process of implementing a quantum random player for the Morra game and some of its variants. This can be done by using a quantum number generator circuit to generate two numbers and a quantum adder to obtain the supposed sum. The advantage of this proposal is that, unlike the implementations of the Morra game on classical computers, which only allow the generation of pseudo-random numbers, true randomness can be obtained through quantum computing.In addition to the description of the entire algorithms, the source code of the implementations is provided to give everyone the freedom to easily test both the quantum implementation of the Morra game and the variants discussed in the paper.

The use of printed banknotes is widespread despite cashless payment methods: for example, more than 27 billion euro banknotes are currently in circulation, and this amount is constantly increasing. Unfortunately, many false banknotes are in circulation, too. Central banks worlwide are continuously striving to reduce the counterfeiting. To fight against the criminal practice, a range of security features are added to banknotes, such as watermarks, micro-printing, holograms, and embossed characters. Beside these well-known characteristics, the colored fibers inside every banknote have strong potential as a security feature, but have so far been poorly exploited. The mere presence of colored fibers does not guarantee the banknote genuineness, as they can be drawn or printed by counterfeiters. However, their random position can be exploited to uniquely identify the banknote. This paper presents a technique for automatically recognizing fibers and efficiently storing their positions, considering realistic application scenarios. The classification accuracy and fault tolerance of the proposed method are theoretically demonstrated, thus showing its applicability regardless of banknote wear or any implementation issue. This is a major advantage with respect to state-of-the-art anti-counterfeit approaches. The proposed security method is strictly topical, as the European Central Bank plans to redesign euro banknotes by 2024.

Quantum computing is one of the most promising technology advances of the latest years. Qubits are highly sensitive to noise, which can make the output useless. Lately, it has been shown that superconducting qubits are extremely susceptible to external sources of faults, such as ionizing radiation. When adopted in large scale, radiation-induced errors are expected to become a serious challenge for qubits reliability. We propose an evaluation of the impact of transient faults in the execution of quantum circuits on superconducting chips. Inspired by the Architectural and Program Vulnerability Factors, widely used for classical computation, we propose the Quantum Vulnerability Factor (QVF) to measure the impact of qubit corruption on the circuit output. We model faults, and design a fault injector, based on the latest studies on real machines and radiation experiments. We report the finding of more than 388,000,000 fault injections, considering single and double faults, on three algorithms, identifying the faults and qubits that are more likely to impact the output. We give guidelines on how to map the qubits in real devices to reduce the output error and to reduce the probability of having a radiation-induced corruption modifying the output. Finally, we compare simulations with experiments on physical quantum computers.

In role-playing games (RPGs), players are called upon to assume the role of a character moving in an imaginary environment and facing several challenges. Their success or failure often depends on randomizers like cards or dice. Regarding the latter, the most commonly used in RPGs are the Platonic solids with the addition of the ten-sided die. They are commonly simulated through classical computers, however, since true randomness is not in their nature, they can only generate pseudorandom numbers. On the contrary, quantum computers exploit the nondeterministic nature of quantum mechanics, so they are perfect candidates for truly random simulations in games of chance. For this reason, this paper proposes and tests various quantum circuits for sampling uniformly distributed discrete values within a fixed range, corresponding to the number of faces of the dice. The simulations reveal the pure randomness of the output of the implemented circuits. They were then used to generate random numbers within a three-dimensional dice-rolling game.

Quantum machines are among the most promising technologies expected to provide significant improvements in the following years. However, bridging the gap between real-world applications and their implementation on quantum hardware is still a complicated task. One of the main challenges is to represent through qubits (i.e., the basic units of quantum information) the problems of interest. According to the specific technology under-lying the quantum machine, it is necessary to implement a proper representation strategy, generally referred to as embedding. This paper introduces a neural-enhanced optimization framework to solve the constrained unit disk problem, which arises in the context of qubits positioning for neutral atoms-based quantum hardware. The proposed approach involves a modified autoencoder model, i.e., the Distances Encoder Network, and a custom loss, i.e., the Embedding Loss Function, respectively, to compute Euclidean distances and model the optimization constraints. The core idea behind this design relies on the capability of neural networks to approximate non-linear transformations to make the Distances Encoder Network learn the spatial transformation that maps initial non-feasible solutions of the constrained unit disk problem into feasible ones. The proposed approach outperforms classical solvers, given fixed comparable computation times, and paves the way to address other optimization problems through a similar strategy.

During the coronavirus pandemic, the mask detection problem has become of particular interest. Usually, the goal is to create a system that can detect whether or not a person is wearing a mask or respirator. However, this tends to trivialize a problem that hides a greater complexity. In fact, people wear masks or respirators in various ways, many of which are incorrect. This makes the problem ill-conditioned and creates a bias compared to training cases, with the consequence that these systems have a considerably lower accuracy when used in practice. We claim that focusing on the ways in which a mask can be worn and classifying the problem not as binary but at least as ternary, thus adding an intermediate class containing all those ways in which a mask or respirator can be worn incorrectly, could help address this problem. For this reason, this paper describes and puts to the proof the Ways to Wear a Mask or a Respirator Database (WWMR-DB). It has a fine classification of the most common ways in which a mask or respirator is worn, which can be used to test how mask detection systems work in cases that resemble the real ones more. It was used to test a neural network, the ResNet-152, which was trained on less fine databases, like the Face-Mask Label Dataset and the MaskedFace-Net. The mixed results denote the shortcomings of these databases and the need to enhance them or resort to finer databases.

Quantum computing is an up-and-coming technology that is expected to revolutionize the computation paradigm in the next few years. Qubits, the primary computing elements of quantum circuits, exploit the quantum physics proprieties to increase the parallelism and speed of computation drastically. Unfortunately, besides being intrinsically noisy, qubits have also been shown to be highly susceptible to external sources of faults, such as ionizing radiation. The latest discoveries highlight a much higher radiation sensitivity of qubits than traditional transistors and identify a much more complex fault model than bit-flip.We propose a framework to identify the quantum circuits sensitivity to radiation-induced faults and the probability for a fault in a qubit to propagate to the output. Based on the latest studies and radiation experiments performed on real quantum machines, we model the transient faults in a qubit as a phase shift with a parametrized magnitude. Additionally, our framework can inject multiple qubit faults, tuning the phase shift magnitude based on the proximity of the qubit to the particle strike location. As we show in the paper, the proposed fault injector is highly flexible, and it can be used on both quantum circuit simulators and real quantum machines. We report the finding of more than 285, 249, 536 injections on the Qiskit simulator and 53, 248 injections on real IBM machines. We consider three quantum algorithms and identify the faults and qubits that are more likely to impact the output. We also consider the fault propagation dependence on the circuit scale, showing that the reliability profile for some quantum algorithms is scale-dependent, with increased impact from radiation-induced faults as we increase the number of qubits. Finally, we also consider multi qubits faults, showing that they are much more critical than single faults. The fault injector and the data presented in this paper are available in a public repository to allow further analysis.

Quantum Computing is a highly promising new computation paradigm. Unfortunately, quantum bits (qubits) are extremely fragile and their state can be gradually or suddenly modified by intrinsic noise or external perturbation. In this paper, we target the sensitivity of quantum circuits to radiation-induced transient faults. We consider quantum circuit cuts that split the circuit into smaller independent portions, and understand how faults propagate in each portion. As we show, the cuts have different vulnerabilities, and our methodology successfully identifies the circuit portion that is more likely to contribute to the overall circuit error rate. Our evaluation shows that a circuit cut can have a 4.6 x higher probability than the other cuts, when corrupted, to modify the circuit output. Our study, identifying the most critical cuts, moves towards the possibility of implementing a selective hardening for quantum circuits.

In-memory key-value stores have quickly become a key enabling technology to build high-performance applications that must cope with massively distributed workloads. In-memory key-value stores (also referred to as NoSQL) primarly aim to offer low-latency and high-throughput data access which motivates the rapid adoption of modern network cards such as Remote Direct Memory Access (RDMA). In this paper, we present the fundamental design principles for exploiting RDMAs in modern NoSQL systems. Moreover, we describe a break-down analysis of the state-of-the-art of the RDMA-based in-memory NoSQL systems regarding the indexing, data consistency, and the communication protocol. In addition, we compare traditional in-memory NoSQL with their RDMA-enabled counterparts. Finally, we present a comprehensive analysis and evaluation of the existing systems based on a wide range of configurations such as the number of clients, real-world request distributions, and workload read-write ratios.

Since the onset of the coronavirus pandemic, researchers from all over the world have been working on projects aimed at countering its advance. The authors of this paper want to go in this direction through the study of a system capable of recognizing the type of mask or respirator worn by a person. It can be used to implement automatic entry controls in high protection areas, where people can feel comfortable and safe. It can also be used to make sure that people who work daily in contact with particles, chemicals, or other impurities wear appropriate respiratory protection. In this paper, a proof-of-concept of this system will be presented. It has been realized by using a state-of-the-art Convolutional Neural Network (CNN), EfficientNet, which was trained on a novel database, called the Facial Masks and Respirators Database (FMR-DB). Unlike other databases released so far, it has an accurate classification of the most important types of facial masks and respirators and their degree of protection. It is also at the complete disposal of the scientific community.

Particulate Matter (PM) air pollution has received growing attention in recent years due to the increased sensitivity to the problem and the spread of low-cost sensing devices. These low-cost devices are able to produce valuable data, but they come with certain hurdles which are engineering challenges to be overcome. These challenges are mainly: reduction of consumed energy; reduction of data logged and transmitted; aging of sensors. The aim of this paper is to understand if it is possible to reduce the duty-cycle of an air pollution monitoring sensor and still get meaningful data on the general behavior. This has advantages from all perspectives: it enables less logging of redundant information; it reduces the strain of the sensor to extend its lifespan and to reduce maintenance costs; it reduces the energy consumed by the sensor, essential in battery-powered devices.

In-memory key-value stores have quickly become a key enabling technology to build high-performance applications that must cope with massively distributed workloads. In-memory key-value stores (also referred to as NoSQL) primarly aim to offer low-latency and high-throughput data access which motivates the rapid adoption of modern network cards such as Remote Direct Memory Access (RDMA). In this paper, we present the fundamental design principles for exploiting RDMAs in modern NoSQL systems. Moreover, we describe a break-down analysis of the state-of-the-art of the RDMA-based in-memory NoSQL systems. In addition, we compare traditional in-memory NoSQL with their RDMA-enabled counterparts.

Energy consumption in Organic Light Emitting Diode (OLED) depends on the displayed contents. The power consumed by an OLED-based display is directly proportional to the luminance of the image pixels. In this paper, a novel idea is proposed to generate energy-efficient images, which consume less power when shown on an OLED-based display. The Blue color component of an image pixel is the most power-hungry i.e. it consumes more power as compared to the Red and Green color components. The main idea is to reduce the intensity of the blue color to the best possible level so that the overall power consumption is reduced while maintaining the perceptual quality of an image. The idea is inspired by the famous "Land Effect", which demonstrates that it is possible to generate a full-color image by using only two color components instead of three. experiments are performed on the Kodak image database. The results show that the proposed method is able to reduce the power consumption by 18% on average and the modified images do not lose the perceptual quality. Social media platform, where users scroll over many images, is an ideal application for the proposed method since it will greatly reduce the power consumption in mobile phones during surfing social networking applications.

The undergoing fourth industrial revolution is constantly putting an accent on the integration of all entities within a single computational system, by moving from a centralized cloud architecture to edge computation. The decentralization of computational power needs a fog architecture, since it's becoming essential to reduce the latency required to take decisions in the industrial environment, in a way that does not violate timing constraints. This work describes the development and the deployment of an industrial fog architecture based on open source tools for Industry 4.0, called IFog4.0. The proposed architecture is able to exchange data using industrial networks and communication protocols, such as Profinet and Modbus TCP, and it is mainly intended to be deployed to Small and Medium Enterprise (SME). Automation is provided by the proposed programming Integrated Development Environment (IDE), while platform management is implemented by the Fog-Management tool. Furthermore, IFog4.0 embeds other open source tools such as Docker and Grafana, to perform data visualization and transmission. Verification is carried out by deploying the platform to an emulated industrial environment. A gas regulation station has been selected as the main usecase.

Air pollution, in particular due to particulate matter, is considered a critical issue, and it is receiving ever growing attention. Environmental monitoring is not limited to the outdoor case in crowded cities, but it is also usefully applied to indoor cases such as factories and offices, or in indoor/outdoor mixed case, such as construction sites or in general industrial plants. This paper describes the case study of an environmental monitoring system, which is compliant with Internet of Things domain. In particular it is suitable for indoor/outdoor industrial applications focused on Particulate Matter measurements. In order to create a reference model, a PM10/PM2.5 sensor not yet tested in literature is used, the Honeywell HPMA115S0-XXX. Four PM10/PM2.5 sensors are used together to test repeatability. This device turned out to be effective in esteeming the level of pollution using low cost sensors, as an alternative to expensive and not portable professional devices. The implementation proposed in this paper is able to give high R values of linear regression, guaranteeing at the same time low cost in terms of hardware and low power consumption.

Ongoing emphasis on the fourth industrial revolution requires further focus on the Internet of Things (IoT) as a means to integrate all relevant entities within a single technological system. In the integration process, a gateway to relay the raw data to an IoT endpoint is essential so that a joint interface among the heterogeneous domains can be provided. This work describes the development of a distributed industrial IoT gateway, called DIIG, able to relay industrial network data to a centralized data-store. DIIG exploits a real-time client server programming model based on S7 communication and Modbus TCP protocols. The subsequent analysis carried out on the testbed mainly focuses on the performance evaluation of the gateway. In order to achieve high performance data transmission in a fair environment, a parallel real-time communication mechanism has been proposed.

Human gait is a dynamic biometrical feature that describes the kinematics of human walking. Gait modeling is studied in order to find a pattern of walking that can be used for diagnosis of walking disorder or abnormal walk detection. Difficulty in walking progressively increases with aging and causes unintentional falls, which is a common incident among elderly people. Fall prediction systems can help to prevent unintentional falls that could cause serious injuries, therefore they can reduce the health service costs. This paper presents an algorithm with polynomial classification model of human gait for real-time fall prediction. This approach enables the user to detect the transition from a normal to an abnormal walking pattern. A dataset based on the state-of-the-art techniques in simulating abnormal walks was created by using an accelerometer embedded in a smartphone, which is recognized to be precise enough for fall avoidance systems. The proposed approach improves state-of-the-art fall prediction approaches, by achieving 99.2% of accuracy in abnormal walk detection.

Summary In this paper, we present our implementation of the fast Fourier transforms on graphic processing unit (GPU) using OpenCL. This implementation of the FFT (ToPe‐FFT) is based on the Cooley‐Tukey set of algorithms with support for 1D and higher dimensional transforms using different radices. Factorization for mix‐radices enables our code to target FFTs of near arbitrary length. In systems with multiple graphic cards (GPUs), the library automatically balances the FFT computation thus achieving maximum resource utilization and higher speedup. Based on profiling and micro‐benchmarking of ToPe‐FFT, it is observed that the average speedup of our library for different sizes is 48× faster than the single CPU‐based code using FFTW and 3× faster than NVIDIA's GPU‐based cuFFT library.

Summary Recent graphic processing units (GPUs) have remarkable raw computing power, which can be used for very computationally challenging problems. Like in micromagnetic simulations, where the magnetostatic field computation to analyze the magnetic behavior at very small time and space scale demands a huge computation time. This paper presents a multidimensional FFT‐based parallel implementation of a magnetostatic field computation on GPUs. We have developed a specialized 3D FFT library for magnetostatic field calculation on GPUs. This made it possible to fully exploit the symmetries inherent in the field calculation and other optimizations specific to the GPUs architecture. We have compared our results with the widely used CPU‐based parallel OOMMF program and with an equivalent serial implementation on CPU. The results have shown a speedup of up to 95x and 8.7x for single and 66x and 4.6x for double precision floating point accuracy against equivalent serial implementation and OOMMF, respectively.

Wireless sensor networks (WSN) are one of the significant technologies due to their diverse applications such as health care monitoring, smart phones, military, disaster management, and other surveillance systems. Sensor nodes are usually deployed in large number that work independently in unattended harsh environments. Due to constraint resources, typically the scarce battery power, these wireless nodes are grouped into clusters for energy efficient communication. In clustering hierarchical schemes have achieved great interest for minimizing energy consumption. Hierarchical schemes are generally categorized as cluster-based and grid-based approaches. In cluster-based approaches, nodes are grouped into clusters, where a resourceful sensor node is nominated as a cluster head (CH) while in grid-based approach the network is divided into confined virtual grids usually performed by the base station. This paper highlights and discusses the design challenges for cluster-based schemes, the important cluster formation parameters, and classification of hierarchical clustering protocols. Moreover, existing cluster-based and grid-based techniques are evaluated by considering certain parameters to help users in selecting appropriate technique. Furthermore, a detailed summary of these protocols is presented with their advantages, disadvantages, and applicability in particular cases.

Symmetric encryption is the most widely adopted security solution for wireless sensor networks. The main open issue in this context is represented by the establishment of symmetric keys. Although many key management schemes have been proposed in order to guarantee a high security level, a solution without weaknesses does not yet exist. An important class of key management schemes is based on a transitory master key (MK). In this approach, a global secret is used during the initialization phase to generate pair-wise keys, and it is deleted during the working phase. However, if an adversary compromises a node before the deletion of the MK, the security of the whole network is compromised. In this paper, a new key negotiation routine is proposed. The new routine is integrated with a well-known key computation mechanism based on a transitory master secret. The goal of the proposed approach is to reduce the time required for the initialization phase, thus reducing the probability that the master secret is compromised. This goal is achieved by splitting the initialization phase in hierarchical subphases with an increasing level of security. An experimental analysis demonstrates that the proposed scheme provides a significant reduction in the time required before deleting the transitory secret material, thus increasing the overall security level. Moreover, the proposed scheme allows to add new nodes after the first deployment with a suited routine able to complete the key establishment in the same time as for the initial deployment.

Toner savings in monochromatic printing are an important target for improving green computing performance and more specifically green printing. In order to extend the lifetime of the printer cartridge, some options are available for laser printers, usually reducing the number of dots with respect to the normal print quality. However available algorithms and patents do not provide a method for dynamically adapting the percentage of toner savings to the required printing quality. In this paper, we introduce a new quasi-random sequence-based algorithm for reducing the number of dots in the printing process, able to achieve optimal discrepancy and low computational complexity, for all print quality levels. In order to reduce patterns in the removed dots, blue noise dithering is applied when the desired percentage of toner savings is moderate. The proposed solution can be easily implemented in the printer firmware, given its low computational complexity. In order to verify the results from a perceptual point of view, an extended test with 135 volunteers and more than 5000 comparisons has been performed, besides checking that toner is effectively saved. Results show that the proposed approach can produce a reduction of the perceived quality almost directly proportional to the number of monochromatic dots skipped, with only a reduced influence from the font used. The perceptual results are better in the proposal than in the previous approaches. The proposed algorithm appears to be a promising technique for improving green printing in monochromatic laser printers without using custom fonts.

Thresholds of vision under low-light conditions have been studied for determining the human response to light stimuli in different contexts. Blackwell's work on thresholds of vision of the human visual system did not explore some variables whose analysis would help to characterize more deeply the visual system. This paper extends Blackwell's results exploring new dimensions of the human visual system response including chromaticity of the stimuli, mono/binocular vision, and dark adaptation. Tests have been performed on a sample of 45 observers, with a simplified laboratory setup with respect to Blackwell and without specific hardware/software, producing results in terms of visual adaptation, monocular, and binocular vision and aging effects.

Wireless sensor networks offer benefits in several applications but are vulnerable to various security threats, such as eavesdropping and hardware tampering. In order to reach secure communications among nodes, many approaches employ symmetric encryption. Several key management schemes have been proposed in order to establish symmetric keys. The paper presents an innovative key management scheme called random seed distribution with transitory master key, which adopts the random distribution of secret material and a transitory master key used to generate pairwise keys. The proposed approach addresses the main drawbacks of the previous approaches based on these techniques. Moreover, it overperforms the state-of-the-art protocols by providing always a high security level.

The reader-to-reader collision problem represents a research topic of great recent interest for the radio frequency identification (RFID) technology. Among the state-of-the-art anticollision protocols, the ones that provide high throughput often have special requirements, such as extra hardware. This study investigates new high throughput solutions for static RFID networks without additional requirements. In this paper, two contributions are presented: a new configuration, called Killer, and a new protocol, called distributed color noncooperative selection (DCNS). The proposed configuration generates selfish behavior, thereby increasing channel utilization and throughput. DCNS fully exploits the Killer configuration and provides new features, such as dynamic priority management, which modifies the performance of the RFID readers when it is requested. Simulations have been conducted in order to analyze the effects of the innovations proposed. The proposed approach is especially suitable for low-cost applications with a priority not uniformly distributed among readers. The experimental analysis has shown that DCNS provides a greater throughput than the state-of-the-art protocols, even those with additional requirements (e.g., 16 percent better than NFRA).

Supply chain is a typical scenario of exploiting Radio Frequency Identification (RFID) technology. Its growing use in all the supply chain areas makes the presence of many close RFID readers more common. In such environment, interferences among readers are critical. Many protocols have been proposed to reduce reader-to-reader collisions. Experimental data showed that the Neighbor Friendly Reader Anticollision (NFRA) protocol (IEEE Trans. Ind. Electron., vol. 56, no. 7, pp. 2326-2336, July 2009) maximizes the network throughput. However, it does not take into account the delay between the request and the granting of query tags, causing delays for some readers. This paper proposes two approaches to increase the fairness and ensure a high throughput for each reader. A theoretical analysis, supported by experimental simulations, demonstrates the improvements achieved.

Nowadays, several kinds of applications based on Radio Frequency Identification (RFID) employ a large number of tags and readers, involving collision problems. A relevant group of reader-to-reader anti-collision protocols are based on time division. Normally these protocols do not require special readers or additional entities; their main challenge is the collision resolution, since after a collision, readers have to choose a new time slot trying to avoid new collisions.It is observed that after a collision, its slot is often unoccupied, therefore this paper proposes to introduce the slot change probability as an additional parameter, in order to reduce the number of readers that change slot and the number of colliding transmissions. A new version of Distributed Color Selection (DCS) is presented, analyzed and compared with well-known protocols based on time division. The simulation analysis shows that the average time required to transmit may be reduced by over 10%.

Variable preamble sampling is a technique used for accessing the medium in low-power constrained networks such as wireless sensor networks. We provide an optimal bound for power consumption in preamble sampling techniques, and we present a model of this technique as a function of power consumption and time of radio utilization. We compare our model by means of experimental experiences performed with commercially-available hardware. Our model shows the tendency of the network for constant and variable preamble sampling approaches and may be used to analyze large networks without actually deploying them.

In this paper an agri-food traceability system based on public key cryptography and Radio Frequency Identification (RFID) technology is proposed. In order to guarantee safety in food, an efficient tracking and tracing system is required. RFID devices allow recording all useful information for traceability directly on the commodity. The security issues are discussed and two different methods based on public cryptography are proposed and evaluated. The first algorithm uses a nested RSA based structure to improve security, while the second also provides authenticity of data. An experimental analysis demonstrated that the proposed system is well suitable on PDAs too.

RFID technology increases rapidly its applicability in new areas of interest without guaranteeing security and privacy issues. This paper presents a new architecture of an RFID transponder with cryptographic capabilities. Other than being compatible with the EPC Class-1 Gen-2 communication protocol, our tag implements an asymmetric ciphering module that proved useful in authentication and anti-counterfeit schemes, particularly critical in many application fields. Experimental results concerning area requirements and power consumption indicate its feasibility.

In video encoding, block motion estimation represents a CPU-intensive task. For this reason, many fast algorithms have been developed to improve searching and matching phases. A milestone within the lossless approach is partial distortion elimination (PDE/SpiralPDE) in which distortion is the difference between the block to be coded and the candidate prediction block. In this paper, (i) we analyze distortion behavior from local information using the Taylor series expansion and show that our general analysis includes other previous similar approaches. (ii) Then, we propose two full-search (lossless), fast-matching, block motion estimation algorithms, based on the PDE idea. The proposed algorithms, called fast full search with sorting by distortion (FFSSD) and fast full search with sorting by gradient (FFSSG), sort the contributions to distortion and the gradient values, respectively, in order to quickly discard invalid blocks. Experimental results show that the proposed algorithms outperform other existing full search algorithms, reducing by up to 20% the total CPU encoding time (with respect to SpiralPDE), while the computation strictly required by the motion estimation is reduced by about 30%. (iii) Finally, we experimentally find an operational lower bound (based on standard test sequences) for the average number of checked pixels in the PDE approach, which measures the performance of the searching and matching phases. In particular, SpiralPDE achieves performances very close to the searching phase bound, while there is still a remarkable margin on the matching phase. We then show that our algorithms, aimed at improving the performances of the matching phase, achieve interesting results, significantly approaching this margin.

The generation of 3-D solid objects, and more generally solid geometric modelling, is very important in Computer Aided Design (CAD). An important role is played by the Constructive Solid Geometry (CSG) representation scheme. IN CSG, objects are described by trees of Boolean operations on half-spaces or boundaries of primitive solids. The study of techniques to speed up the rendering of scenes modelled with the CSG scheme is an attractive field of research; in this paper we propose a new algorithm which reduces the computational complexity for ray casting approaches. Our strategy identifies a set of areas on the plane of view where the rays starting from the observer have to be traced; for each zone, only a portion of the entire CSG tree has to be considered for intersection tests, instead of the whole database of the primitive objects. A comparison of our algorithm with a ray caster that adopts bounding volume hierarchies and with a freeware ray tracer called POV-Ray shows that, for the examples considered, we may reduce the intersection tests to one third of those performed when standard optimizations are adopted.