Department Publications

In this letter, we propose a physical-layer security (PLS) scheme for millimeter-wave systems. Our scheme employs analog beamforming with random frequency variation (RFV) for transmitted symbols. We derive the exact closed-form expression of the secrecy rate of our RFV scheme. Furthermore, we verify the superior performance of RFV scheme compared to previously proposed PLS schemes. In particular, the RFV scheme provides higher secrecy rate for more than 70% of the angular range, when the bandwidth is 10% of the carrier frequency. Furthermore, there is no array gain loss at the legitimate user, compared to previously proposed PLS schemes.

Recent advances in Reinforcement Learning (RL) have surpassed human-level performance in many simulated environments. However, existing reinforcement learning techniques are incapable of explicitly incorporating already known domain-specific knowledge into the learning process. Therefore, the agents have to explore and learn the domain knowledge independently through a trial and error approach, which consumes both time and resources to make valid responses. Hence, we adapt the Deep Deterministic Policy Gradient (DDPG) algorithm to incorporate an adviser, which allows integrating domain knowledge in the form of pre-learned policies or pre-defined relationships to enhance the agent’s learning process. Our experiments on OpenAi Gym benchmark tasks show that integrating domain knowledge through advisers expedites the learning and improves the policy towards better optima.

Image generation from a single image using generative adversarial networks is quite interesting due to the realism of generated images. However, recent approaches need improvement for such realistic and diverse image generation, when the global context of the image is important such as in face, animal, and architectural image generation. This is mainly due to the use of fewer convolutional layers for mainly capturing the patch statistics and, thereby, not being able to capture global statistics very well. We solve this problem by using attention blocks at selected scales and feeding a random Gaussian blurred image to the discriminator for training. Our results are visually better than the state-of-the-art particularly in generating images that require global context. The diversity of our image generation, measured using the average standard deviation of pixels, is also better.

This paper introduces a novel locomotion strategy of combining wheeled and legged actuation in a parallel structure. This enables the robot to traverse rough terrains using the articulated limbs and use wheeled locomotion for more efficient traversal over smooth terrains. Authors present a prototype robot architecture to embody the concept and show experimentally the robot traversing a variety of indoor and outdoor terrains. Additionally, a novel butterfly gait is introduced for the robot that enables stable and efficient traversal of challenging terrains.

Three-dimensional space-time velocity filters may be used to enhance dynamic passband objects of interest in videos while attenuating moving interfering objects based on their velocities. In this paper, we show that the attenuation of interfering stopband objects may be significantly improved using recently proposed shifted-velocity filters. It is shown that an improvement of approximately 20 dB in signal-to-interference ratio may be achieved for stopband to passband velocity differences of only 1 pixels/frame. More importantly, this improvement is achieved without increasing the computational complexity.

Physical unclonable functions (PUFs) are widely considered in secret key generation for resource constrained devices. However, PUFs require additional hardware overhead. In this letter, we focus on developing a PUF-efficient, robust, and secure key generation scheme. First, a novel method for extracting quaternary PUF responses is proposed to increase the entropy of a PUF response, in which a 2-bit response is extracted from evaluating a single PUF cell multiple times. The probability masses of the responses can be adjusted by setting parameters appropriately. Then, a chosen secret model based fuzzy extractor (FE) is designed to extract secret keys from the quaternary PUF responses. To improve the security of this FE, it is modeled as a wiretap channel system, and wiretap polar coding is adopted to reduce secrecy leakage. An upper bound of secrecy leakage is also given in this letter, and it suggests that …

Large-batch training of deep neural networks (DNN) has recently been widely studied, since traversing the optimization landscape is faster with large batches and the emergence of parallel computing has made large-batch training feasible. However, its generalization is still worse compared to small-batch training, which is commonly attributed to the low gradient variance with large batches. We show that sign stochastic gradient descent (signSGD) is a suitable candidate for large-batch ResNet training with improved generalization compared to vanilla SGD. We further modify signSGD to improve the convergence for large batches while retaining its generalization properties. In particular, we propose r-signSGD, which restricts certain parameter updates of signSGD—r-signSGD updates a parameter of the neural network only if its gradient sign matches its gradient momentum sign. This prevents unnecessary/wrong …

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A high step-down, non-isolated, DC-DC converter composed of a switched-inductor is proposed in this paper. The switched-inductor utilizes a flying and switched-capacitor network to step-down the input voltage. The current-stress at the output capacitor has been reduced by the output inductor providing additional step-down. Equivalent circuits of the proposed converter are used to explain the operation in ideal conditions. The converter is further analyzed to investigate the impact of equivalent-series-resistances of switched-capacitors on the overall voltage gain. Moreover, expressions for voltage-current stresses of switching elements and design equations for passive devices have been derived at the steady-state analysis. Finally, the operation of the proposed converter is validated using simulations.

Cortical processing of arithmetic and of language rely on both shared and task-specific neural mechanisms, which should also be dissociable from the particular sensory modality used to probe them. Here, spoken arithmetical and non-mathematical statements were employed to investigate neural processing of arithmetic, compared to general language processing, in an attention-modulated cocktail party paradigm. Magnetoencephalography (MEG) data were recorded from 22 human subjects listening to audio mixtures of spoken sentences and arithmetic equations while selectively attending to one of the two speech streams. Short sentences and simple equations were presented diotically at fixed and distinct word/symbol and sentence/equation rates. Critically, this allowed neural responses to acoustics, words, and symbols to be dissociated from responses to sentences and equations. Indeed, the simultaneous neural processing of the acoustics of words and symbols were observed in auditory cortex for both streams. Neural responses to sentences and equations, however, were predominantly to the attended stream, originating primarily from left temporal, and parietal areas, respectively. Additionally, these neural responses were correlated with behavioral performance in a deviant detection task. Source-localized Temporal Response Functions revealed distinct cortical dynamics of responses to sentences in left temporal areas and equations in bilateral temporal, parietal, and motor areas. Finally, the target of attention could be decoded from MEG responses, especially in left superior parietal areas. In short, the neural responses to arithmetic …

Our ability to communicate using speech depends on complex, rapid processing mechanisms in the human brain. These cortical processes make it possible for us to easily understand one another even in noisy environments. Measurements of neural activity have found that cortical responses time-lock to the acoustic and linguistic features of speech. Investigating the neural mechanisms that underlie this ability could lead to a better understanding of human cognition, language comprehension, and hearing and speech impairments.

Unmanned aerial vehicles (UAVs) commonly referred to as drones are becoming the attractive solution that provides wireless connectivity in a situation wherever the standard terrestrial base station is burden with high data traffic or unreachable in a disaster scenario. Our work examines the problem of best deployment of the UAVs as an aerial base station to empower wireless coverage to the users in the coverage less area. The foremost contributions of this study incorporate couple of strategies to deploy the ABSs and associate the UEs to the best ABS, where the total spectral efficiency of the network is maximized. Approaches will be chosen considering the prevailing environmental condition and system constraints. The first strategy uses an unsupervised clustering algorithm to determine the 2D positioning of the UAV ((x, y) coordinates) and a stable marriage approach is used for UE assignment by …