Deep neural networks (DNNs) require increasingly high compute and memory resources. Microscaling (MX) data formats improve energy efficiency and preserve accuracy under aggressive bit-width reduction, but further gains from continued bit-width reduction remain challenging. This work proposes a hybrid computation scheme that integrates MX data formats with stochastic computing (SC) to improve the energy efficiency of DNN inference under constrained bit widths. Model parameters are stored in MX format, while multiplications and accumulations are performed in the SC domain. MX reduces memory footprint, while SC improves compute energy efficiency. To address the latency and accuracy challenges of SC, we employ a parallel bitstream-generation scheme and an encoding strategy that reduces random fluctuation error. Experimental results demonstrate up to a 2× improvement in energy efficiency while maintaining inference accuracy within 1–2% of an FP32 baseline.

Deep learning models have been widely used recently for environmental monitoring. Unless designed carefully, they are well known for their large computational complexity and long computation time, which limit their direct utilization as onsite embedded solutions. Therefore, to assure their suitability for onboard processing on drones during tactical responses, we propose in this paper energy-efficient and highly accurate tiny machine learning (TinyML) models in the field of radar remote sensing for oil spill monitoring. More precisely, the proposed signal processing models are based on artificial neural networks and use radar signals to detect oil slicks on top of the seawater and estimate their thicknesses using drones in calm ocean conditions. Despite their extremely small size (7-37 Bytes), their accuracy in detection and parameter estimation exceeds 94%. Moreover, the proposed models are characterized by low-power (32-173 mW) and low-latency (0.35-0.59 µs) performance when implemented on the FPGA computing platform.

To locate oil slicks, most state-of-the-art systems use synthetic aperture radar (SAR) imaging techniques at specific incident angles. However, only a few works present a detailed quantitative electromagnetic (EM) modeling of emulsified oil slicks. Therefore, in this paper, we investigate the electromagnetic modeling of selected oil emulsion models by extending their analysis over multiple SAR operating frequency bands and from a different operation point (from the nadir), which is required for the development of better monitoring solutions. The study presents a detailed spectral analysis of oil emulsions in all L-, C-, and X- frequency bands, and shows how their dielectric properties change differently according to the scanning EM wave frequency. We further highlight how the oil thickness introduces a cyclic behavior in the radar measurement, which affects pure oil slick detection at each frequency band. Finally, we investigate the radar backscattering sensitivity to emulsions, against clean water surfaces, for different percentages of water-in-oil and different slick thicknesses.

Oil spills severely damage marine life and coastal environments. To reduce their polluting effect on the ecosystem, it is important to promptly react to potential spills for early detection and monitoring. In this paper, we propose a drone-based solution with a deep-learning U-net model. It processes the radar backscattering dominated by the specular component in calm ocean conditions to detect contaminated sea surfaces with oil spills. Results show that our approach achieves a high detection rate exceeding 90% for thick oil slicks in the range of 1-10 mm.

For environmental and financial reasons, it is critical to develop new monitoring techniques that reduce the damage to the world’s marine ecosystems from oil spills. Information about the oil spill, such as the distribution of its thickness and its physical characteristics, will help in effective spill containment and tactical countermeasures. In this paper, based on radar sensing, we develop an iterative maximum-likelihood estimation approach to remotely extract both required information: the slick thickness and a physical characteristic of spilled slicks represented by the relative permittivity (dielectric constant). The targeted ranges are 1-10 mm thicknesses for thick oils, and 1.9-3.3 relative permittivities for light and crude oils. Results prove the performance accuracy of the proposed iterative approach with few iterations.

In this paper, we present a maximum a-posteriori probabilistic approach for oil spill detection at very low wind speeds using nadir-looking wide-band radar systems mounted on drones. Such platforms allow for to have radar measurements for calm ocean conditions when the winds' speed is very small challenging current state-of-the-art techniques used for oil spill detection. We study the detection accuracy by exploiting the variation in the distribution of radar power reflectivities from both C- and X-band for different oil thicknesses and electromagnetic wave frequencies. The joint probability density function (pdf)-based detector shows that by optimally combining reflectivity values evaluated at multiple scanning frequencies, the performance is boosted over the whole range of possible slick thicknesses. The probability of detection is further improved by running multiple scans of the scene.

Learning for model based control can be sample-efficient and generalize well, however successfully learning models and controllers that represent the problem at hand can be challenging for complex tasks. Using inaccurate models for learning can lead to sub-optimal solutions that are unlikely to perform well in practice. In this work, we present a learning approach which iterates between model learning and data collection and leverages forward model prediction error for learning control. We show how using the controller’s prediction as input to a forward model can create a differentiable connection between the controller and the model, allowing us to formulate a loss in the state space. This lets us include forward model prediction error during controller learning and we show that this creates a loss objective that significantly improves learning on different motor control tasks. We provide empirical and theoretical results that show the benefits of our method and present evaluations in simulation for learning control on a 7 DoF manipulator and an underactuated 12 DoF quadruped. We show that our approach successfully learns controllers for challenging motor control tasks involving contact switching.

We introduce Crocoddyl (Contact RObot COntrol by Differential DYnamic Library), an open-source framework tailored for efficient multi-contact optimal control. Crocoddyl efficiently computes the state trajectory and the control policy for a given predefined sequence of contacts. Its efficiency is due to the use of sparse analytical derivatives, exploitation of the problem structure, and data sharing. It employs differential geometry to properly describe the state of any geometrical system, e.g. floating-base systems. Additionally, we propose a novel optimal control algorithm called Feasibility-driven Differential Dynamic Programming (FDDP). Our method does not add extra decision variables which often increases the computation time per iteration due to factorization. FDDP shows a greater globalization strategy compared to classical Differential Dynamic Programming (DDP) algorithms. Concretely, we propose two modifications to the classical DDP algorithm. First, the backward pass accepts infeasible state-control trajectories. Second, the rollout keeps the gaps open during the early "exploratory" iterations (as expected in multipleshooting methods with only equality constraints). We showcase the performance of our framework using different tasks. With our method, we can compute highly-dynamic maneuvers (e.g. jumping, front-flip) within few milliseconds.

The explosion in the mobile broadband data usage and the advent of applications with different uplink and downlink quality of service requirements has triggered an increased interest in time division duplexing (TDD) systems. Despite the flexibility it offers in dynamically changing the uplink and downlink transmission schedules according to the traffic variations in a given network, the TDD mode of operation introduces increasing complexity in managing the interference generated by the concurrent uplink and downlink tranmission in different cells. In this paper, we present a statistical framework for the modeling of interference in TDD systems and the analysis the impact of dynamic switching point configuration on the TDD-based network operation. The probability density function (pdf) for each type of interference is first derived using moment generating functions. Then, interference maps are generated to analyze the impact of TDD operation in a given network.