The New Jersey Families Study; Spring 2024

Newslette

On MMSE Properties of Codes for the Gaussian Broadcast and Wiretap Channels

This work concerns the behavior of "good" (capacity achieving) codes in several multi-user settings in the Gaussian regime, in terms of their minimum mean-square error (MMSE) behavior. The settings investigated in this context include the Gaussian wiretap channel, the Gaussian broadcast channel (BC) and the Gaussian BC with confidential messages (BCC). In particular this work addresses the effects of transmitting such codes on unintended receivers, that is, receivers that neither require reliable decoding of the transmitted messages nor are they eavesdroppers that must be kept ignorant, to some extent, of the transmitted message. This work also examines the effect on the capacity region that occurs when we limit the allowed disturbance in terms of MMSE on some unintended receiver. This trade-off between the capacity region and the disturbance constraint is given explicitly for the Gaussian BC and the secrecy capacity region of the Gaussian BCC

Distributed Compressed Estimation for Wireless Sensor Networks Based on Compressive Sensing

This letter proposes a novel distributed compressed estimation scheme for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is also presented and an algorithm is developed to optimize measurement matrices, which can further improve the performance of the proposed distributed compressed estimation scheme. Simulations for a wireless sensor network illustrate the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance

Cooperative Robotic Fabrication for a Circular Economy

In a cooperative robotic fabrication (CRF) framework, multiple industrial robots are specifically sequenced to work together, thus allowing them to execute coordinated processes with greater geometric and structural variation. In the context of the construction industry, agents in a cooperative setup can perform complementary functions such as placing or removing building components while simultaneously providing temporary support to a structure. This approach can reduce, or completely remove, the need for temporary external supports and scaffolding that would typically be required for stability during the construction of geometrically complex spanning spatial structures. For a circular economy, this means overall reductions to primary resource inputs and improvements to the disassembly, reuse, and reassembly potential of a structure at the end of its life. This chapter gives a summary of three projects that successfully demonstrate the use of cooperative robotic fabrication to promote several principles of a circular economy through different scaffold-free construction applications. The topics covered in this chapter will be of interest to researchers and professionals interested in the emergent intersection of digital fabrication, robotics, and sustainability applied to the building industry

Higher emissions scenarios lead to more extreme flooding in the United States

Understanding projected changes in flooding across the contiguous United States (CONUS) helps increase our capability to adapt to and mitigate against this hazard. Here, we assess future changes in flooding across CONUS using outputs from 28 global climate models and four scenarios of the Coupled Model Intercomparison Project Phase 6. We find that CONUS is projected to experience an overall increase in flooding, especially under higher emission scenarios; there are subregional differences, with the Northeast and Southeast (Great Plains of the North and Southwest) showing higher tendency towards increasing (decreasing) flooding due to changes in flood processes at the seasonal scale. Moreover, even though trends may not be detected in the historical period, these projected future trends highlight the current needs for incorporating climate change in the future infrastructure designs and management of the water resources

Recent insights from non-mammalian models of brain injuries: an emerging literature

Traumatic brain injury (TBI) is a major global health concern and is increasingly recognized as a risk factor for neurodegenerative diseases including Alzheimer’s disease (AD) and chronic traumatic encephalopathy (CTE). Repetitive TBIs (rTBIs), commonly observed in contact sports, military service, and intimate partner violence (IPV), pose a significant risk for long-term sequelae. To study the long-term consequences of TBI and rTBI, researchers have typically used mammalian models to recapitulate brain injury and neurodegenerative phenotypes. However, there are several limitations to these models, including: (1) lengthy observation periods, (2) high cost, (3) difficult genetic manipulations, and (4) ethical concerns regarding prolonged and repeated injury of a large number of mammals. Aquatic vertebrate model organisms, including Petromyzon marinus (sea lampreys), zebrafish (Danio rerio), and invertebrates, Caenorhabditis elegans (C. elegans), and Drosophila melanogaster (Drosophila), are emerging as valuable tools for investigating the mechanisms of rTBI and tauopathy. These non-mammalian models offer unique advantages, including genetic tractability, simpler nervous systems, cost-effectiveness, and quick discovery-based approaches and high-throughput screens for therapeutics, which facilitate the study of rTBI-induced neurodegeneration and tau-related pathology. Here, we explore the use of non-vertebrate and aquatic vertebrate models to study TBI and neurodegeneration. Drosophila, in particular, provides an opportunity to explore the longitudinal effects of mild rTBI and its impact on endogenous tau, thereby offering valuable insights into the complex interplay between rTBI, tauopathy, and neurodegeneration. These models provide a platform for mechanistic studies and therapeutic interventions, ultimately advancing our understanding of the long-term consequences associated with rTBI and potential avenues for intervention

Relay Control for Full-Duplex Relaying with Wireless Information and Energy Transfer

This study investigates wireless information and energy transfer for dual-hop amplify-and-forward full-duplex relaying systems. By forming energy efficiency (EE) maximization problem into a concave fractional program of transmission power, three relay control schemes are separately designed to enable energy harvesting and full-duplex information relaying. With Rician fading modeled residual self-interference channel, analytical expressions of outage probability and ergodic capacity are presented for the maximum relay, signal-to-interference-plus-noise-ratio (SINR) relay, and target relay. It has shown that EE maximization problem of the maximum relay is concave for time switching factor, so that bisection method has been applied to obtain the optimized value. By incorporating instantaneous channel information, the SINR relay with collateral time switching factor achieves an improved EE over the maximum relay in delay-limited and delay-tolerant transmissions. Without requiring channel information for the second-hop, the target relay ensures a competitive performance for outage probability, ergodic capacity, and EE. Comparing to the direct source-destination transmission, numerical results show that the proposed relaying scheme is beneficial in achieving a comparable EE for low-rate delay-limited transmission

Few-Shot Machine Learning at the Grid-Edge: Data-Driven Impedance Models for Model-Free Smart Inverters

The future electric grid will be pervasively supported by a large number of smart inverters distributed at the grid edge, whose dynamics are critical for grid stability and resiliency. The operating conditions of these inverters may vary across a wide range, leading to various impedance patterns and complicated grid-inverter interaction behaviors. Existing analytical impedance models require thorough and precise understandings of system parameters and make numerous assumptions to reduce the system complexity. They can hardly capture complete electrical behaviors of physical systems when inverters are controlled with sophisticated algorithms or performing complex functions. Real-world impedance acquisitions across multiple operating points through simulations or measurements are expensive and impractical. Leveraging the recent advances in artificial intelligence and machine learning, we present the InvNet, a few-shot machine learning framework that is capable of characterizing inverter impedance patterns across a wide operation range when only limited impedance data for each inverter is available. The InvNet is capable of extrapolating from physics-based models to real-world models and from inverters to inverters. Comprehensive evaluations were conducted to verify the effectiveness of the proposed approach in various application scenarios. All data and models were open-sourced. We showcase machine learning and neural networks as powerful tools for modeling black-box characteristics of sophisticated grid-edge energy systems and their capabilities of analyzing behaviors of larger-scale systems that cannot be described via traditional analytical methods

Algebraic Program Analysis

This paper is a tutorial on algebraic program analysis. It explains the foundations of algebraic program analysis, its strengths and limitations, and gives examples of algebraic program analyses for numerical invariant generation and termination analysis