LiDAR-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. However, they are known to be sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR). As a result, LiDAR-based object detectors trained on data captured in normal weather tend to perform poorly in such scenarios. However, collecting and labelling sufficient training data in a diverse range of adverse weather conditions is laborious and prohibitively expensive. To address this issue, we propose a physics-based approach to simulate LiDAR point clouds of scenes in adverse weather conditions. These augmented datasets can then be used to train LiDAR-based detectors to improve their all-weather reliability. Specifically, we introduce a hybrid Monte-Carlo based approach that treats (i) the effects of large particles by placing them randomly and comparing their back reflected power against the target, and (ii) attenuation effects on average through calculation of scattering efficiencies from the Mie theory and particle size distributions. Retraining networks with this augmented data improves mean average precision evaluated on real world rainy scenes and we observe greater improvement in performance with our model relative to existing models from the literature. Furthermore, we evaluate recent state-of-the-art detectors on the simulated weather conditions and present an in-depth analysis of their performance.

A domain shift exists between the distributions of large scale, outdoor lidar datasets due to being captured using different types of lidar sensors, in different locations, and under varying weather conditions. Inclement weather in particular affects the quality of lidar data, adding artifacts such as scattered and missed points, leading to a drop in performance of 3D object detection networks trained on standard lidar datasets. Domain adaptation methods seek to adapt source-trained neural networks to a target domain. Pseudo-label based self training approaches are popular methods for source-free unsupervised domain adaptation. However, their efficacy depends on the quality of the labels generated by the source trained model. These labels may be incorrect with high confidence, rendering thresholding methods ineffective. In order to avoid reinforcing errors caused by label noise, we propose an uncertainty-aware mean teacher framework which implicitly filters incorrect pseudo-labels during training. Leveraging model uncertainty allows the mean teacher network to perform implicit filtering by down-weighing losses corresponding to uncertain pseudo-labels. Effectively, we perform automatic soft-sampling of pseudo-labeled data while aligning predictions from the student and teacher networks. We demonstrate our domain adaptation method on an adverse weather dataset created by augmenting lidar scenes from KITTI with rain, snow, and fog and show that it out-performs current domain adaptation frameworks. We make our code publicly available11https://github.com/deeptibhegde/UncertaintyAwareMeanTeacher.

We propose a method for covert communications using an IEEE 802.11 OFDM/QAM packet as a carrier. We show how to hide the covert message so that the transmitted signal does not violate the spectral mask specified by the standard, and we determine its impact on the OFDM packet error rate (PER). We show conditions under which the hidden signal is not usable and those under which it can be retrieved with a usable bit error rate (BER). The hidden signal is extracted by cancellation of the OFDM signal in the covert receiver. We explore the effects of the hidden signal on OFDM parameter estimation and the covert signal BER. We test the detectability of the covert signal with and without cancellation. We conclude with an experiment where we inject the hidden signal into Over-The-Air (OTA) recordings of 802.11 packets and demonstrate the effectiveness of the technique using that real-world OTA data.

In a spectrum-monitoring scenario, a monitor will attempt to intercept and classify a signal. If the monitor uses a Convolutional Neural Network (CNN) for classification, the intercepted signal can frustrate classification attempts by employing an adversarial waveform. An adversarial waveform is a small additive perturbation at the transmitter, and is generated similarly to adversarial attacks against image classifiers. We demonstrate that without foreknowledge of the CNN employed at the monitor the communication system can develop such an adversarial waveform and deploy it thus transferring the attack. The adversarial waveform is created by constraining the signal-to-interference ratio at the transmitter, which has the dual benefits of making the adversarial waveform easy to deploy and mitigates impairment to the communications link. We also demonstrate the vulnerability of a spectrum monitoring system to this type of attack as a function of symbol rate uncertainty, where the monitor does not have an exact estimate of the symbol rate employed by the communications link. The spectrum monitor becomes more susceptible to the attack as bandwidth is increased.

Coding schemes for the T-user binary adder channel are investigated. Recursive constructions are given for two families of mixed-rate, multiuser codes. It is shown that these basic codes can be combined by time-sharing to yield codes approaching most rates in the T-user capacity region. In particular, the best codes constructed herein achieve a sum-rate, R/sub 1/+...+R/sub T/, which is higher than all previously reported codes for almost every T and is within 0.547-bit-per-channel use of the information-theoretic limit. Extensions to a T-user, Q-frequency adder channel are also discussed.

Generalized decoding decision rules provide added flexibility in a decoding scheme, and some advantages. In a generalized decoding decision rule, the following possibilities are considered: (1) the decoder has the option of not deciding at all, or rejecting all estimates. This is termed an erasure; (2) the decoder has the option of putting out more than one estimate. The resulting output is called a list. Only if the correct codeword is not on the list is there a list error. Taking into account the lack of exact knowledge of the channel statistics and assuming a mismatch between the true channel transition probabilities and the nominal probabilities used in the decoding metric, error bounds are developed for generalized decision rules. Conditions under which the error probabilities converge to zero exponentially with the block length, in spite of the presence of mismatch, are established.>

Improvements on the rates of iterated codes for error-free decoding on the binary symmetric channel are presented. Approximations to the performance of Elias's original error-free codes are replaced with virtually exact results that demonstrate higher code rates and the ability to decode from noisier channels than the original results indicated. Prefacing an Elias code with iterations of one or more primitive Bose-Chaudhuri-Hoequenghem (BCH) codes is shown to provide error-free decoding for any channel withp \leq 0.42and to yield code rates closer to capacity than those of Elias's original code. An heuristic algorithm is given for selecting an efficient set of BCH codes to iterate.

A recent paper on polynomial codes[l] presented an important theorem concerning the BCH minimum-distance bound for the dual of a polynomial code. For a particular set of numerical examples, however, the theorem failed. This correspondence presents a modified version of the theorem that covers those cases and uses a method of proof, which, while rigorous, shows clearly the significance of certain concepts. The BCH bound on the minimum distance, however, remains the same.