Defense Notices

The abilities of feature learning, scene understanding, and task generalization are the consistent pursuit in deep learning-based computer vision. A number of object detectors with various network structures and algorithms have been proposed to learn more effective features, to extract more contextual and semantic information, and to achieve more robust and more accurate performance on different datasets. Nevertheless, the problem is still not well addressed in practical applications. One major issue lies in the inefficient feature learning and propagation in challenging situations like small objects, occlusion, illumination, etc. Another big issue is the poor generalization ability on datasets with different feature distribution. 

The study aims to explore different learning frameworks and strategies to solve the above issues. (1) We propose a new model to make full use of different features from details to semantic ones for better detection of small and occluded objects. The proposed model emphasizes more on the effectiveness of semantic and contextual information from features produced in high-level layers. (2) To achieve more efficient learning, we propose the near-orthogonality regularization, which takes the neuron redundancy into consideration, to generate better deep learning models. (3) We are currently working on tightening the object localization by integrating the localization score into a non-maximum suppression (NMS) to achieve more accurate detection results, and on the domain adaptive learning that encourages the learning models to acquire higher generalization ability of domain transfer. 

Trends toward large-scale integration and the high-power application of green energy resources necessitate the advent of efficient power converter topologies, multilevel converters. Multilevel inverters are effective solutions for high power and medium voltage DC-to-AC conversion due to their higher efficiency, provision of system redundancy, and generation of near-sinusoidal output voltage waveform. Recently, modular multilevel converter (MMC) has become increasingly attractive. To improve the harmonic profile of the output voltage, there is the need to increase the number of output voltage levels. However, this would require increasing the number of submodules (SMs) and power semi-conductor devices and their associated gate driver and protection circuitry, resulting in the overall multilevel converter to be complex and expensive. Specifically, the need for large number of bulky capacitors in SMs of conventional MMC is seen as a major obstacle. This work proposes an MMC-based multilevel converter that provides the same output voltage as conventional MMC but has reduced number of bulky capacitors. This is achieved by introduction of an extra middle arm to the conventional MMC. Due to similar dynamic equations of the proposed converter with conventional MMC, several previously developed control methods for voltage balancing in the literature for conventional MMCs are applicable to the proposed MMC with minimal effort. Comparative loss analysis of the conventional MMC and the proposed multilevel converter under different power factors and modulation indexes illustrates the lower switching loss of proposed MMC. In addition, a new voltage balancing technique based on carrier-disposition pulse width modulation for modular multilevel converter is proposed.

The second part of this work focuses on an improved control of MMC-based high-power DC/DC converters. Medium-voltage DC (MVDC) and high-voltage DC (HVDC) grids have been the focus of numerous research studies in recent years due to their increasing applications in rapidly growing grid-connected renewable energy systems, such as wind and solar farms. MMC-based DC/DC converters are employed for collecting power from renewable energy sources. Among various developed DC/DC converter topologies, MMC-based DC/DC converter with medium-frequency (MF) transformer is a valuable topology due to its numerous advantages. Specifically, they offer a significant reduction in the size of the MMC arm capacitors along with the ac-link transformer and arm inductors due to the ac-link transformer operating at medium frequencies. As such, this work focuses on improving the control of isolated MMC-based DC/DC (IMMDC) converters. The single phase shift (SPS) control is a popular method in IMMDC converter to control the power transfer. This work proposes conjoined phase shift-amplitude ratio index (PSAR) control that considers amplitude ratio indexes of MMC legs of MF transformer’s secondary side as additional control variables. Compared with SPS control, PSAR control not only provides wider transmission power range and enhances operation flexibility of converter, but also reduces current stress of medium-frequency transformer and power switches of MMCs. An algorithm is developed for simple implementation of the PSAR control to work at the least current stress operating point. Hardware-in-the-loop results confirm the theoretical outcomes of the proposed control method.

As the upcoming fifth-generation (5G) and future wireless network is envisioned in areas such as augmented and virtual reality, industrial control, automated driving or flying, robotics, etc, the requirement of supporting ultra-reliable low-latency communications (URLLC) is increasingly urgent than ever. From the channel coding perspective, URLLC requires codewords being transported in finite block-lengths. In this regards, we propose novel encoding algorithms and analyze their performance behaviors for the finite-length Luby transform (LT) codes.

Luby transform (LT) codes, the first practical realization and the fundamental core of fountain codes, play a key role in the fountain codes family. Recently, researchers show that the performance of LT codes for finite block-lengths can be improved by adding memory into the encoder. However, this work only utilizes one memory, leaving the possibilities of exploiting and how to exploiting more memories an open problem. To explore this unknown, this proposed research targets to 1) propose an encoding algorithm to utilize one more memory and compare its performance with the existing work; 2) generalize the memory based encoding method to arbitrary memory orders and mathematically analyze its performance; 3) find out the optimal memory order in terms of bit error rate (BER), frame error rate (FER), and decoding convergence speed; 4) Apply the memory based encoding algorithm to additive white Gaussian noise (AWGN) channels and analyze its performance.

The worldwide energy demand is growing quickly, with an anticipated rate of growth of 48% from 2012 to 2040. Consequently, investments in all forms of renewable energy generation systems have been growing rapidly. Increased use of clean renewable energy resources such as hydropower, wind, solar, geothermal, and biomass is expected to noticeably renewable energy resources alleviate many present environmental concerns associated with fossil fuel-based energy generation.  In recent years, wind and solar energies are gained the most attention among all other renewable resources. As a result, both have become the target of extensive research and development for dynamic performance optimization, cost reduction, and power reliability assurance.  

The performance of Photovoltaic (PV) systems is highly affected by environmental and ambient conditions such as irradiance fluctuations and temperature swings. Furthermore, the initial capital cost for establishing the PV infrastructure is very high. Therefore, its essential that the PV systems always harvest the maximum energy possible by operating at the most efficient operating point, i.e. Maximum Power Point (MPP), to increase conversion efficiency and thus result in lowest cost of captured energy.

The dissertation is an effort to develop a new PV conversion system for large scale PV grid-connected systems which provides efficacy enhancements compared to conventional systems by balancing voltage mismatches between the PV modules. Hence, it analyzes the theoretical models for three selected DC/DC converters. To accomplish this goal, this work first introduces a new adaptive maximum PV energy extraction technique for PV grid-tied systems. Then, it supplements the proposed technique with a global search approach to distinguish absolute maximum power peaks within multi-peaks in case of partially shaded PV module conditions. Next, it proposes an adaptive MPP tracking (MPPT) strategy based on the concept of model predictive control (MPC) in conjunction with a new current sensor-less approach to reduce the number of required sensors in the system.  Finally, this work proposes a power balancing technique for injection of balanced three-phase power into the grid using a Cascaded H-Bridge (CHB) converter topology which brings together the entire system and results in the final proposed PV power system. The resulting PV system offers enhanced reliability by guaranteeing effective system operation under unbalanced phase voltages caused by severe partial shading.

The developed grid connected PV solar system is evaluated using simulations under realistic dynamic ambient conditions, partial shading, and fully shading conditions and the obtained results confirm its effectiveness and merits comparted to conventional systems.

Organizations are becoming more exposed to security threats due to shift towards cloud infrastructure and IoT devices. One growing category of cyber threats is Distributes Denial of Service (DDoS) attacks. It is hard to detect DDoS attacks due to evolving attack patterns and increasing data volume. So, creating filter rules manually to distinguish between legitimate and malicious traffic is a complex task. Current work explores a supervised machine learning based approach for DDoS detection. The proposed model uses a step forward feature selection method to extract 15 best network features and random forest classifier for detecting DDoS traffic. This solution can be used as an automatic detection algorithm for DDoS mitigation pipelines implemented in the most up-to-date DDoS security solutions.

Multiple-Input Multiple-Output (MIMO) systems with large-scale transmit antenna arrays, often called massive MIMO, are a very promising direction for 5G due to their ability to increase capacity and enhance both spectrum and energy efficiency. To get the benefit of massive MIMO systems, accurate downlink channel state information at the transmitter (CSIT) is essential for downlink beamforming and resource allocation. Conventional approaches to obtain CSIT for FDD massive MIMO systems require downlink training and CSI feedback. However, such training will cause a large overhead for massive MIMO systems because of the large dimensionality of the channel matrix. In this dissertation, we improve the performance of FDD massive MIMO networks in terms of downlink training overhead reduction, by designing an efficient downlink beamforming method and developing a new algorithm to estimate the channel state information based on compressive sensing techniques. First, we design an efficient downlink beamforming method based on partial CSI. By exploiting the relationship between uplink direction of arrivals (DoAs) and downlink direction of departures (DoDs), we derive an expression for estimated downlink DoDs, which will be used for downlink beamforming. Second, By exploiting the sparsity structure of downlink channel matrix, we develop an algorithm that selects the best features from the measurement matrix to obtain efficient CSIT acquisition that can reduce the downlink training overhead compared with conventional LS/MMSE estimators. In both cases, we compare the performance of our proposed beamforming method with traditional methods in terms of downlink achievable rate and simulation results show that our proposed method outperform the traditional beamforming methods.​

The process of going through data to discover hidden connections and predict future trends has a long history. In this data-driven world, data mining is an important process to extract knowledge or insights from data in various forms. It explores the unknown credible patterns which are significant in solving many problems. There are quite a few techniques in data mining including classification, clustering, and prediction. We will discuss the classification, by using a technique called rule induction using four different approaches.

We compare the complexity of rule sets induced using characteristic sets and maximal consistent blocks. The complexity of rule sets is determined by the total number of rules induced for a given data set and the total number of conditions present in each rule. We used Incomplete Data sets to induce rules. These data sets have missing attribute values. Both methods were implemented and analyzed to check how it influences the complexity. Preliminary results suggest that the choice between characteristic sets and generalized maximal consistent blocks is inconsequential. But the cardinality of the rule sets is always smaller for incomplete data sets with “do not care” conditions. Thus, the choice between interpretations of the missing attribute value is more important than the choice between characteristic sets and generalized maximal consistent blocks.

As people gather during different social, political or musical events, automated crowd analysis can lead to effective and better management of such events to prevent any unwanted scene as well as avoid political manipulation of crowd numbers. Crowd counting remains an integral part of crowd analysis and also an active research area in the field of computer vision. Existing methods fail to perform where crowd density is either too high or too low in an image, thus resulting in either overestimation or underestimation. These methods also mix crowd-like cluttered background regions (e.g. tree leaves or small and continuous patterns) in images with actual crowd, resulting in further crowd overestimation. In this work, we present a novel deep convolutional neural network (CNN) based framework ZiZoNet for automated crowd counting in static images in very low to very high crowd density scenarios to address above issues. ZiZoNet consists of three modules namely Crowd Density Classifier (CDC), Decision Module (DM) and Count Regressor Module (CRM). The test image, divided into 224x224 patches, passes through crowd density classifier (CDC) that classifies each patch to a class label (no-crowd (NC), low-crowd (LC), medium-crowd (MC), high-crowd (HC)). Based on the CDC information and using either heuristic Rule-set Engine (RSE) or machine learning based Random Forest based Decision Block (RFDB), DM decides which mode (zoom-in, normal or zoom-out) this image should use for crowd counting. CRM then performs patch-wise crowd estimate for this image accordingly as decided or instructed by the DM module. Extensive experiments on three diverse and challenging crowd counting benchmarks (UCF-QNRF, ShanghaiTech, AHU-Crowd) show that our method outperforms current state-of-the-art models under most of the evaluation criteria.​

Due to their extreme spatial confinement, surface plasmon resonances show great potential in the design of future devices that would blur the boundaries between electronics and optics. Traditionally, plasmonic interactions are induced with geometries involving noble metals and dielectrics. However, accessing these plasmonic modes requires delicate election of material parameters with little margin for error, controllability, or room for signal bandwidth. To rectify this, two novel plasmonic mechanisms with a high degree of control are explored: For the near infrared region, transparent conductive oxides (TCOs) exhibit tunability not only in "static" plasmon generation (through material doping) but could also allow modulation on a plasmon carrier through external bias induced switching. These effects rely on the electron accumulation layer that is created at the interface between an insulator and a doped oxide. Here a rigorous study of the electromagnetic characteristics of these electron accumulation layers is presented. As a consequence of the spatially graded permittivity profiles of these systems it will be shown that these systems display unique properties. The concept of Accumulation-layer Surface Plasmons (ASP) is introduced and the conditions for the existence or for the suppression of surface-wave eigenmodes are analyzed. A second method could allow access to modes of arbitrarily high order. Sub-wavelength plasmonic nanoparticles can support an infinite discrete set of orthogonal localized surface plasmon modes, however only the lowest order resonances can be effectively excited by incident light alone. By allowing the background medium to vary in time, novel localized surface plasmon dynamics emerge. In particular, we show that these temporal permittivity variations lift the orthogonality of the localized surface plasmon modes and introduce coupling among different angular momentum states. Exploiting these dynamics, surface plasmon amplification of high order resonances can be achieved under the action of a spatially uniform optical pump of appropriate frequency.

This project report presents results of experiments on rule induction from incomplete data using probabilistic approximations. Mining incomplete data using probabilistic approximations is a well-established technique. Main goal of this report is to present research on a comparison carried out on two different approaches to mining incomplete data using probabilistic approximations: single local probabilistic approximations approach and concept probabilistic approximations. These approaches were implemented in python programming language and experiments were carried out on incomplete data sets with two interpretations of missing attribute values: lost values and do not care conditions. Our main objective was to compare concept and single local approximations in terms of the error rate computed using double hold-out method for validation. For our experiments we used seven incomplete data sets with many missing attribute values. The best results were accomplished by concept probabilistic approximations for five data sets and by single local probabilistic approximations for remaining two data sets.