A Scalable, Secure, and Energy-Efficient Image Representation for Wireless Systems

The recent growth in wireless communications presents a new challenge to multimedia communications. Digital image transmission is a very common form of multimedia communication. Due to limited bandwidth and broadcast nature of the wireless medium, it is necessary to compress and encrypt images before they are sent. On the other hand, it is important to efficiently utilize the limited energy in wireless devices. In a wireless device, two major sources of energy consumption are energy used for computation and energy used for transmission. Computation energy can be reduced by minimizing the time spent on compression and encryption. Transmission energy can be reduced by sending a smaller image file that is obtained by compressing the original highest quality image. Image quality is often sacrificed in the compression process. Therefore, users should have the flexibility to control the image quality to determine whether such a tradeoff is acceptable. It is also desirable for users to have control over image quality in different areas of the image so that less important areas can be compressed more, while retaining the details in important areas. To reduce computations for encryption, a partial encryption scheme can be employed to encrypt only the critical parts of an image file, without sacrificing security. This thesis proposes a scalable and secure image representation scheme that allows users to select different image quality and security levels. The binary space partitioning (BSP) tree presentation is selected because this representation allows convenient compression and scalable encryption. The Advanced Encryption Standard (AES) is chosen as the encryption algorithm because it is fast and secure. Our experimental result shows that our new tree construction method and our pruning formula reduces execution time, hence computation energy, by about 90%. Our image quality prediction model accurately predicts image quality to within 2-3dB of the actual image PSNR

ATHENA : a pagerank-based scheme to solve the thundering herd in authentication

Vehicles in intelligent transport systems (ITS) react to an emergency situation by broadcasting critical messages like Decentralized Environmental Notification Messages (DENMs). A digital signature is attached to each message to secure the integrity of communication, and this message is inoperative until the authentication completes. This creates a challenge for vehicles to verify massive messages in some scenarios where it could incur the thundering herd in authentication, if there is a critical situation happening in heavy road traffic. To address this problem, we propose ATHENA, a pagerank-based scheme to solve the thundering herd in ITS authentication that utilises the transmission of messages and pagerank algorithm to rank the broadcasting vehicles. Simulation results show the efficiency of ATHENA and the effectiveness of performance enhancements compared with others

With Federal Moratorium Expiring, 15 Million People at Risk of Eviction

Nationwide, renters are recovering from an unprecedented economic crisis. With vaccines widely accessible, employment rising, and federal and state benefits available to millions of people, many of the over 100 million people living in rental housing are making a gradual recovery. Despite this progress, a meaningful percentage of renters are on the precipice of eviction, displacement, and homelessness. More than 15 million people live in households that are currently behind on their rental payments (7.4 million adults, 6.5 million households), which places them at legal risk of eviction. According to one estimate, these households collectively owe more than $20 billion to their landlords. On a per tenant basis, average debt owed to landlords exceeds$3,000, with significant variation based on time away from work, family needs, and other factors.When the Centers for Disease Control and Prevention (CDC) eviction moratorium ends on July 31st, these renters may face eviction, civil lawsuits for unpaid rent, and aggressive debt collection—crises that will continue to cause harm years into the future. Nearly 50% of those who are behind on rent anticipate that they will be evicted in the next two months. The threat of eviction is particularly acute for renters of color. Currently, 22% of Black renters and 17% of Latinx renters are in debt to their landlords, compared to 15% overall and 11% of White renters. Rental debt is also challenging for renters with children, with 19% unable to make payments.This report highlights the current number of people at risk of eviction as the federal moratorium expires, how we got here, and policies states can implement to help prevent a wave of evictions from cascading into long-term health and financial crises for millions of households

A threat based approach to computational offloading for collaborative cruise control

The interaction between discrete components of Internet of Things (IoT) and Intelligent Transportation Systems (ITS) is vital for a collaborative system. The secure and reliable use of Cruise Control (CC) with Cloud and Edge Cloud to achieve complete autonomy for a vehicle is a key component and a major challenge for ITS. This research unravels the complications that arise when Adaptive Cruise Control (ACC) is incorporated into a collaborative environment. It mainly answers the question of where to securely compute Collaborative Cruise Control’s (CCC) data in a connected environment. To address this, the paper initially reviews previous research in the domain of Vehicular Cloud, ITS architecture, related threat modelling approaches, and secure implementations of ACC. An overview application model for CCC is developed for performing a threat analysis with the purpose of investigating the reasons why a vehicle suffers collision. Through the use of interviews, the research analyses and suggests the location of computational data by creating a taxonomy between the Edge Cloud, Cloud and the On-board Unit (OBU) while validating the model

Towards integrated superconducting detectors on lithium niobate waveguides

Superconducting detectors are now well-established tools for low-light optics, and in particular quantum optics, boasting high-efficiency, fast response and low noise. Similarly, lithium niobate is an important platform for integrated optics given its high second-order nonlinearity, used for high-speed electro-optic modulation and polarization conversion, as well as frequency conversion and sources of quantum light. Combining these technologies addresses the requirements for a single platform capable of generating, manipulating and measuring quantum light in many degrees of freedom, in a compact and potentially scalable manner. We will report on progress integrating tungsten transition-edge sensors (TESs) and amorphous tungsten silicide superconducting nanowire single-photon detectors (SNSPDs) on titanium in-diffused lithium niobate waveguides. The travelling-wave design couples the evanescent field from the waveguides into the superconducting absorber. We will report on simulations and measurements of the absorption, which we can characterize at room temperature prior to cooling down the devices. Independently, we show how the detectors respond to flood illumination, normally incident on the devices, demonstrating their functionality.Comment: 7 pages, 4 figure

Variability of daily maximum wind speed across China, 1975–2016: an examination of likely causes

Assessing change in daily maximum wind speed and its likely causes is crucial for many applications such as wind power generation and wind disaster risk governance. Multidecadal variability of observed near-surface daily maximum wind speed (DMWS) from 778 stations over China is analyzed for 1975–2016. A robust homogenization protocol using the R package Climatol was applied to the DMWS observations. The homogenized dataset displayed a significant (p 0.10); that is, DMWS declined during the cold semester (October–March) and increased during the warm semester (April–September). Correlation analysis of the Arctic Oscillation, the Southern Oscillation, and the west Pacific modes exhibited significant correlation with DMWS variability, unveiling their complementarity in modulating DMWS. Further, we explored potential physical processes relating to the atmospheric circulation changes and their impacts on DMWS and found that 1) overall weakened horizontal airflow [large-scale mean horizontal pressure gradient (from −0.24 to +0.02 hPa decade−1) and geostrophic wind speed (from −0.6 to +0.6 m s−1 decade−1)], 2) widely decreased atmospheric vertical momentum transport [atmospheric stratification thermal instability (from −3 to +1.5 decade−1) and vertical wind shear (from −0.4 to +0.2 m s−1 decade−1)], and 3) decreased extratropical cyclones frequency (from −0.3 to 0 month decade−1) are likely causes of DMWS change.This study was supported by the National Natural Science Foundation of China (Grant 41621061), the National Key Research and Development Program–Global Change and Mitigation Project (Grant 2016YFA0602404), funding from STINT (CH2015-6226), and the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement 703733 (STILLING project). This work has been also supported by the VR project (2017-03780) funded by the Swedish Research Council and Ramon y Cajal fellowship (RYC-2017-22830) and Grant RTI2018-095749-A-I00 (MCIU/AEI/FEDER, UE)

Compressive characterization of telecom photon pairs in the spatial and spectral degrees of freedom

In the past few years, physicists and engineers have demonstrated the possibility of utilizing multiple degrees of freedom of the photon to perform information processing tasks for a wide variety of applications. Furthermore, complex states of light offer the possibility of encoding and processing many bits of information in a single photon. However, the challenges involved in the process of extracting large amounts of information, encoded in photonic states, impose practical limitations to realistic quantum technologies. Here, we demonstrate characterization of quantum correlated photon pairs in the spatial and spectral degrees of freedom. Our technique utilizes a series of random projective measurements in the spatial basis that do not perturb the spectral properties of the photon. The sparsity in the spatial properties of downconverted photons allows us to exploit the potential of compressive sensing to reduce the number of measurements to reconstruct spatial and spectral properties of correlated photon pairs at telecom wavelength. We demonstrate characterization of a photonic state with 12 × 109 dimensions using only 20% of the measurements with respect to the conventional raster scan technique. Our characterization technique opens the possibility of increasing and exploiting the complexity and dimensionality of quantum protocols that utilize multiple degrees of freedom of light with high efficiency