This survey is on forward-looking, emerging security concerns in post-quantum era, i.e., the implementation attacks for 2022 winners of NIST post-quantum cryptography (PQC) competition and thus the visions, insights, and discussions can be used as a step forward towards scrutinizing the new standards for applications ranging from Metaverse/Web 3.0 to deeply-embedded systems.

Recent advances in sensing, electronic, processing, machine learning, and communication technologies are accelerating the development of assisted and automated functions for commercial vehicles. Environmental perception sensor data are processed to generate a correct and complete situational awareness. It is of utmost importance to assess the robustness of the sensor data pipeline, particularly in the case of data degradation in a noisy and variable environment. Sensor data reduction and compression techniques are key for higher levels of driving automation, as there is an expectation that traditional automotive vehicle wired networks will not be able to support the needed sensor datarates (i.e. more than 10 perception sensors, including cameras, LiDARs, and RADARs, generating tens of Gb/s of data). This work proposes for the first time to consider video compression for camera data transmission on vehicle wired networks in the presence of highly noisy data, e.g. partially obstructed camera field of view. The effects are discussed in terms of machine learning vehicle detection accuracy drop, and also visualising how detection performance spatially varies on the frames using the recently introduced metric, the Spatial Recall Index. The presented parametric occlusion noise model is generated to emulate real-world occlusion patterns, whereas compression is based on the well-established AVC/H.264 compression standard. The results demonstrate that the DNN performance are stable when increasing compression despite adding small amounts of noise. However, higher levels of occlusion noise have a higher impact on DNN performance, and when combined with compression, there is a significant decrease in the DNN performance.

The conventional computing platforms based on von-Neumann architecture are highly space- and energy-intensive while handling the emerging applications such as AI, ML, and big data. To overcome the von Neumann bottleneck, compact and light-weight logic-inmemory implementations of Boolean logic gates based on emerging non-volatile memory (e-NVM) such as RRAMs, PCM, STT-MRAMs, etc., were proposed recently. However, these e-NVMs not only exhibit significant temporal and spatial variability, but their large-scale integration with CMOS process is also a technological challenge. To overcome these issues with the emerging non-volatile memories, Ferroelectric FETs based on CMOS-compatible doped Hafnium oxide with the capability of large-scale CMOS integration in the advanced logic nodes were proposed. Considering the high scalability and CMOS-compatibility of the FeFETs, in this work, for the first time, we propose a logic-inmemory implementation utilizing a single ferroelectric fullydepleted-silicon-on-insulator (Fe-FDSOI) FET exploiting the unique drain erase phenomenon. In our proposed logicin-memory implementation, inputs are applied at the gate and drain terminals using a novel input-to-voltage mapping scheme, and output is obtained as the current flowing through the Fe-FDSOI FET. We utilize an experimentally calibrated compact model of the ferroelectric capacitor connected to the baseline industry standard BSIM-IMG compact model for the FDSOI transistor for proof of concept demonstration. We also perform a comprehensive analysis of the performance metrics of the proposed logic-inmemory implementation. Our results indicate that we can realize at least 10 Boolean logic gates with high energy and area-efficiency utilizing the proposed scheme.

Paper that propose a new radio propagation modeling technic using graph neural network.Â

Users often purchase membership credentials with a fixed number of uses or limited duration from Internet service providers, we call them pay-per-use or time span of membership services. However, users’ access records, usage preferences, and habits are collected by network attackers or membership providers for creating users’ profiles, targeted advertising, and even for being sold maliciously. To deal with these problems, lots of anonymous authentication protocols are proposed to provide users with pseudonyms to conceal their real identities. Although these protocols effectively prevent network attackers from compromising users’ privacy, membership service providers can still gather users’ behavioral privacy via their member- ship credentials. Therefore, several scholars proposed k-times anonymous authentication protocols and self-blind credentials to enhance users’ privacy protection, but the k-times anonymous authentication protocols are only for pay-per-use membership services and the schemes of self-blind credentials are lack of regulating malicious users. To address these issues, this article proposes an anonymous authentication protocol for time span of membership (AATM) with self-blindness and accountability. Specifically, accountable self-blind credentials are constructed to ensure that users can create a brand new identity by them- selves for each membership access, which not only prevents a user from being linked by service providers but also supports conditional and impartial regulating. Security and performance analyses show that AATM is better than the state-of-the-art schemes in terms of security and privacy-preserving capabilities, and its computation cost also meets the practical application requirements.

Consecutive-$k$-out-of-$n$:F systems are probably one of the most studied types of networks when discussing reliability. They have been used from safety-critical environments, such as nuclear power plants or hospital’s emergency backup power supplies, to classical transportation problems, such as public water systems and oil/gas pipelines. Exact formulae for the reliability polynomial of a consecutive system are known for quite a long time. Additionally, several alternatives for computing exactly the reliability polynomial are also known. However, when dealing with large consecutive systems, exact calculations become prohibitive and approximations/bounds are the common route. We begin this article by providing an in-depth review of many known bounds. Next, we focus on the coefficients of the reliability polynomial of a consecutive system in its Bernstein form. By deriving shape properties of these coefficients, we are able to identify new bounds. Our approach is uncommon for this case, as none of the previously used bounding techniques has looked closely at each and every coefficient. This is probably the reason why we obtain tight bounds with low complexity costs. Finally, detailed simulations provide strong evidence of the fidelity of the proposed bounds.

A simple analysis bounding the performance of heat pumps for heating on the coldest days of the year per state.

A document by Petro Tshakwanda . Click on the document to view its contents.

A Hertzian dipole is a small antenna that emits radio waves. In this article, the interaction of a Hertzian dipole with the field reflected from the environment is analyzed. The study shows that a single Hertzian dipole can behave like a quantum object: in particular, it can interfere with itself at a double slit, owing to a ponderomotive force. Therefore, it cannot reach every location behind a double slit with the same probability, and the probability distribution for the location of an impact on a screen behind the double slit may show a clear interference pattern. This effect disappears if one of the slits is closed, or if the system of the Hertzian dipole, double slit and standing wave is disturbed by external influences. Both particle interference and the collapse of the wave function are considered to be effects that cannot be explained by classical physics. However, Hertzian dipoles can be tangible objects moving on classical trajectories, and their location and velocity can be measured simultaneously at any time. Thus, the Hertzian dipole provides an example of how central features of quantum mechanics can be explained by means of classical physics.

In the last two decades, muscle synergies analysis has been commonly used to assess the neurophysiological  mechanisms underlying human motor control. Despite several synergy models and algorithms have been  employed for processing the electromyographic (EMG) signal, and neural substrates indicate their non?homogeneous origin, EMG patterns are usually preprocessed without separating phasic (movement-related) and tonic (anti-gravity and related to co-contraction) components. Using a comprehensive mapping of upper?limb point-to-point movements, synergies were extracted from phasic and tonic EMG signal separately, estimating the tonic components with a linear ramp model. The goodness of reconstruction (R2 ) as a function  of the number of synergies was compared, and synergies extracted from each dataset at three threshold levels (0.80, 0.85, 0.90) were retained for further analysis. Then, shared, phasic-specific, and tonic-specific synergies  were extracted from the two datasets concatenated. We found only few shared synergies, indicating that phasic  and tonic synergies have in general different structures. Shared, phasic-specific and tonic-specific synergies  were clustered separately and compared for evaluating differences in synergy composition. Phasic-specific  clusters were more numerous than tonic-specific ones and with a higher sparseness, suggesting that they were  more differentiated among subjects. The structure of the clusters indicated that phasic synergies identify  specific patterns related to the movement (sparse composition) while tonic synergies show co-contraction of  multiple muscles for joint stabilization and holding postures. These results suggest that phasic and tonic  synergies should be extracted separately, especially when performing muscle synergy analysis in patients with  abnormal tonic activity and for tuning devices with gravity support

A document by Enrico Maria Vitucci . Click on the document to view its contents.

This publication devises a method which formulates a national strategy for the renovation of single family houses by treating each era of initial construction independent of all the others, and applies a universally adoptable procedure to evaluate the cost efficiency of carbon emission reduction. A multi-objective optimization was conducted which varied the building envelope and energy systems while optimizing for annual cost and carbon emissions. The optimization was carried out in oemof.solph and PyGMO on typical German building stocks from 11 different construction eras between 1860 and 2020. The buildings were modelled in TEASER using TABULA building stock data. The results indicate that post-war era construction has the greatest improvement potential by saving over 16 tons of CO2/yr with respect to its business as usual case. The recommended solutions for each construction era have an investment cost to emission reduction ratio which is 25% better than those of the current efficiency subsidies.

A document by Amir Hossein Fahim Raouf . Click on the document to view its contents.

The data center markets in emerging economies are being built at a furious pace. When high availability is required, as it always is in the modern digital economy, the placement of geo-distributed data centers may be influenced by factors such as technician shortage and under-developed infrastructure, both of which are typical in emerging economies. Although the data center availability subject in general has been well studied, it remains unclear how rapid and unbalanced economic development in emerging economies may affect the availability of geo-distributed data centers and their cost of ownership. In this paper, we incorporate the unbalanced availability of infrastructure and technician into the data center placement. The problem is first formulated as a mixed integer nonlinear program (MINLP).To solve this potentially large scale problem, we transform it into a QCQP, capable of handling heterogeneous workloads. The resulting problem can then be efficiently solved by off-the-shelf optimization toolboxes. With real-life data in China, we show how unbalanced development of infrastructure and technician shortage may affect the placement of data centers, and analyze the tradeoff between cost and availability. Our results indicate that technician shortage and unbalanced network infrastructure will lead to increased cost and distinct data center placement strategies.

The Radiative Transfer (RT) theory has been widely utilized for wave propagation in random media, but it faces challenges in situations involving strong forward scattering, such as in forests with electrically large trunks, due to the singularity of the scattering phase matrix. In this paper, we present an effective approach to compute multiple scattering solutions to RT equations with singular phase matrix by combining the strategy of forward scattering extraction with an efficient numerical iterative procedure through interpolation. We evaluate the effectiveness and efficiency of our technique through simulations using a layer of vertically oriented, electrically large long cylinders to represent a layer of trunks over the ground. The results demonstrate that the proposed approach increases the computational efficiency by one to two orders of magnitude in cases where forward scattering is dominant. Additionally, a parameterized model is derived by matching the higher-order RT results with the ω − τ formalism under catered conditions. An explicit physical definition of the equivalent scattering albedo and equivalent optical thickness are proposed under boundary-free conditions. The multiple scattering effects are included in the physically derived equivalent parameters of the plant layer, which are independent of ground conditions by definition. Tests verify that the applicability of the parameterized model with ω−τ form can be extended to a wider range of vegetation and ground conditions. Besides, these equivalent parameters are directly linked to the geometric structures and electromagnetic properties of the vegetation layer, allowing their values to be frequency- and angle-dependent. Compared to the single-scattering albedo and optical thickness, the effective albedo derived from the RT model exhibits relatively weak polarization and angle dependence. This is consistent with many empirically derived parameterizations while providing a physically plausible origin for these equivalent parameters. Remarkably, we find that the transmittance linked to the parameterized tau value, incorporating multiple scattering effects, is similar to that obtained through full-wave simulations.

In this paper, the efficiency and the quality factor (Q) of an infinitesimal loop antenna with the circumference of 0.01λ enclosed in a µ-negative (MNG) shell of 0.023λ diameter is investigated. It is demonstrated that an MNG shell can be designed to produce an electrically small system with a matched input impedance leading to total efficiency approaching unity. Simulation results show that the fractional bandwidth of significantly better than Chu limit can be obtained, especially if the material is regarded dispersionless. The calculated Q of the antenna with LL model, M model, and Z model MNG are 74.5%, 55.9%, and 37.5% of those predicted by Chu limit, respectively.

ZeroMove hashing is a novel data distribution technique for distributed systems that offers several key benefits. In contrast to the consistent hashing algorithm, which requires data migration when scaling the system, ZeroMove hashing enables the addition of clusters of nodes on demand without the need to move data between nodes. A cluster is located using an encoded unique identifier, while a node is identified with a hash function within a cluster. This approach ensures that data remains in the node where it is hashed, thereby increasing availability and improving system performance. Furthermore, the ZeroMove hashing technique can significantly reduce facility and administrative expenses, making it an excellent option for largescale distributed systems. Our tests on consistent hashing and ZeroMove hashing have shown that scaling from one node to six nodes with 480,000 data records took 6100 seconds in a system based on consistent hashing. In contrast, it took only 1.2 seconds for ZeroMove hashing to achieve similar scaling under the same settings. With consistent hashing, the time taken and amount of data moved increase proportionally with the amount of data stored in the system. However, with ZeroMove hashing, these values does not increase in proportion to the amount of data being stored. This is because ZeroMove hashing only involves the exchange of small amount of metadata between nodes during scaling processes.

Several natural disasters worldwide have demonstrated that the severity of extreme weather events (EWEs) on power systems can be increased by adversely affecting renewable power. Generating renewable scenarios specific to EWEs is crucial for the effective implementation of resilience-oriented operations and planning in power systems that incorporate a high proportion of renewable energies. Despite the significant progress achieved recently, it remains a formidable challenge to produce high-quality samples from a limited dataset due to the low occurrence probability of EWEs. A conditional diffusion model-based method is firstly introduced to discern spatiotemporal correlations of renewable series assoicated with common conditional like diurnal, season, and temperature without suffering from prevalent issues like statistical assumptions and mode collapse. To guarantee the specificity of the generated samples to EWEs while maintaining diversity, a fine-tuning mechanism is proposed for the pre-trained diffusion models using a few-shot learning technology in conjunction with a self-supervised loss function. The effectiveness of the proposed method is verified on two real-world dataset. Numerical results demonstrate that the scenarios generated by the proposed method exhibit distinct advantages in terms of diversity and EWE-specific consistency. This facilitates scenario-based two-stage robust optimization and enhances the performance of resilience-oriented decisions in out-of-sample scenarios.