The feature selection step - the ability for a model to extract good features - is the most important aspect of any model, as if some features that are part of the set which defines the concept class are excluded, no machine learning algorithm can capture and truly learn the concept class of interest. It has been the norm that the feature selection step is carried out on groups of properties, called attributes. In this work, we propose a new method for feature selection based on Information Gain, that allows for selection of attributes without assuming independence between them and by assigning ranks to properties instead of the whole attribute. This approach can be used with single class learning given that the probability distribution of the features is known. We tested this approach against Mutual Information and TFIDF, using a model based on conceptual space theory. Despite the mostly biased model, a slight increase in the accuracy of classification has been obtained compared to the other feature selection methods. Based on the dataset used, we found out that the absence of certain instructions such as jmp, could be used to detect Trojans with weak association. We anticipate that the proposed approach can be used in future work to allow a model to construct its own dimensions freely from sequences of opcodes.
A novel Doppler LiDAR sensor has been developed with the help of NASA technology transfer. This sensor can make highly accurate, three-dimensional velocity measurements in the body frame of a vehicle. However, the practicability of such a sensor in an IMU-mechanized navigation solution is unknown. A covariance analysis can be run with a simulation flight across multiple trajectories as well as different IMU grades to understand the sensors accuracy and contribution to a navigation system. With a full simulated analysis of this sensor, it is much easier for researchers and others to understand the full benefits that a Doppler Lidar system might bring to a navigation solution. We conduct the analysis by way of covariance analysis as well as Monte Carlo and give a brief explanation of what covariance analysis is and how it can be used to obtain results for this new sensor. We define this new software tool we have created and speak on how researchers can use it to conduct covariance analysis tests of their own.
A microsphere lens integrated on top of an MWIR photodetector has exhibited an increase in detector overall sensitivity over a desired spectral bandwidth and, more importantly, a decrease in Noise-to-Signal-Ratio (NSR), as we have shown in our prior work. Using signal processing methods, we have also shown, for the first time, the existence of an adverse effect of microsphere misalignment on the detector's overall sensitivity. In this paper, we model and analyze the effect of various misalignment types between the microsphere-lens and the photodetector on the resulting detector sensitivity. As we show here, microsphere lens-sensor misalignment can result, to some varying degrees, in reduced spectral responses in microspheres-lens-enhanced MWIR photodetectors. We also model and analyze the effect of such misalignment on multiple SLS photodetector sizes, namely 35 $\mu\mathrm{m}$ and 40 $\mu \mathrm{m}$, and microsphere material types, namely Sapphire and Polystyrene. In the case of Polystyrene material, the results show that the sensitivity of the microsphere-lens-enhanced detector is mainly and significantly reduced by the microsphere Polystyrene material alone around wavenumber 3000, due to the Polystyrene material spectral absorptions around that wavenumber.
In this paper, the real-time cooperative guidance strategy for the aircraft trying to evade an incoming attacking missile was developed. The aircraft performs evasive maneuvers, launching a defender missile to divert the missile. Instead of classical strategies, which are based on optimal control or differential game, the engagement was formulated as a multi-agent game. The problem was solved by using the deep reinforcement learning method. To address the sparse reward problem, a general reward design method was presented utilizing the shaping technique. Guidance law, reward function, and training approach were demonstrated through the learning process and simulations. The application of the non-sparse reward function accelerated the model convergence. Considering a standard optimal guidance law as a benchmark, the effectiveness and advantages (guarantees of the aircraft's escape and win rates in multi-agent game) of the proposed guidance strategy were validated by the simulation results. Compared to the standard optimal guidance law, the proposed guidance strategy performed better with less prior knowledge.
This paper presents an octree-based compression algorithm for LiDAR point cloud data. This solution is presented as an alternative to LASzip, the current state-of-the-art in LiDAR compression, that provides an average 60% higher compression ratio on Geiger-mode data, and is capable of natively compressing both LAS and BPF file formats.
