Department Publications

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 …

Intraoperative PET Navigation System for Cancer Surgery Toggle navigation J-STAGE home 資料・記事を探す 資料を探す:資料タイトルから 資料を探す:分野から 資料を探す:発行機関 から 記事を探す データを探す(J-STAGE Data) J-STAGEについて J-STAGEの概要 各種サービス・機能 公開データ 利用規約・ポリシー 新規登載の申し込み ニュース&PR お知らせ一覧 リリースノート メンテナンス情報 イベント情報 J-STAGEニュース 特集コンテンツ 各種広報媒体 サポート J-STAGE登載 機関用コンテンツ 登載ガイドライン・マニュアル 閲覧者向けヘルプ 動作確認済みブラウザ FAQ お 問い合わせ サイトマップ サインイン カート JA English 日本語 資料・記事を探す 資料を探す:資料 タイトルから 資料を探す:分野から 資料を探す:発行機関から 記事を探す データを探す(J-STAGE Data) J-STAGEについて J-STAGEの概要 各種サービス・機能 公開データ 利用規約・ポリシー 新規登載の申し込み ニュース&PR お知らせ一覧 リリースノート メンテナンス情報 イベント情報 J-STAGE…

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 …

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity for the users in such a scenario. The main contributions of this work include a less complex approach to optimally position the UAVs and to assign user equipment (UE) to each ABS, such that the total spectral efficiency (TSE) of the network is maximized, while maintaining a minimum QoS requirement for the UEs. The main advantage of the proposed approach is that it only requires the knowledge of UE and ABS locations and statistical channel state information. The optimal 2-dimensional (2D) positions of the ABSs and the UE assignments are found using K-means clustering and a stable marriage approach, considering the characteristics of the air-to-ground propagation channels, the impact of co-channel interference from other ABSs, and the energy constraints of the ABSs. Two approaches are proposed to find the optimal altitudes of the ABSs, using search space constrained exhaustive search and particle swarm optimization (PSO). The numerical results show that the PSO-based approach results in higher TSE compared to the exhaustive search-based approach in dense networks, consuming similar amount of energy for ABS movements. Both approaches lead up to approximately 8-fold energy savings compared to ABS placement using naive exhaustive search.

Neural processing along the ascending auditory pathway is often associated with a progressive reduction in characteristic processing rates. For instance, the well-known frequency-following response (FFR) of the auditory midbrain, as measured with electroencephalography (EEG), is dominated by frequencies from ∼100 Hz to several hundred Hz, phase-locking to the acoustic stimulus at those frequencies. In contrast, cortical responses, whether measured by EEG or magnetoencephalography (MEG), are typically characterized by frequencies of a few Hz to a few tens of Hz, time-locking to acoustic envelope features. In this study we investigated a crossover case, cortically generated responses time-locked to continuous speech features at FFR-like rates. Using MEG, we analyzed responses in the high gamma range of 70–200 Hz to continuous speech using neural source-localized reverse correlation and the …

Vehicle-to-vehicle (V2V) communication plays a pivotal role in intelligent transport systems (ITS) with cellular-vehicle to everything (C-V2X) and IEEE 802.11p being the two competing enabling technologies. This paper presents multi-dimensional discrete-time Markov chain (DTMC) based models to study the medium access control (MAC) layer performance of the IEEE 802.11p standard and C-V2X Mode 4, considering periodic cooperative awareness messages (CAMs) and event-driven decentralized environmental notification messages (DENMs). Closed-form solutions for the steady-state probabilities of the models are obtained, which are then utilized to derive expressions for several key performance metrics. Numerical results are provided to draw insights on the performance. In particular, a performance comparison between IEEE 802.11p and C-V2X Mode 4 in terms of the average delay, the collision …

This paper discusses advances related to the integration of future mobile electronic THz systems. Without claiming to provide a comprehensive review of this surging research area, the authors gathered research on selected topics that are expected to be of relevance for the future exploration of components for practical mobile THz imaging and sensing applications. First, a brief technology review of integrated mobile THz components is given. Advances in III-V technology, silicon technology, and resonant-tunneling diodes (RTD) are discussed. Based on an RTD source and a SiGe-HBT direct detector, low-cost and compact computed tomography is presented for volumetric continuous-wave imaging at around 300 GHz. Moreover, aspects of system integration of mobile THz MIMO radars are discussed. Thereby, a novel phase-locked loop concept utilizing a high-stability yttrium-iron-garnet-tuned oscillator to …

Geometric information of scenes available with four-dimensional (4-D) light fields (LFs) paves the way for post-capture refocusing. Light field refocusing methods proposed so far have been limited to a single planar or a volumetric region of a scene. In this letter, we demonstrate simultaneous refocusing of multiple volumetric regions in LFs. To this end, we employ a 4-D sparse finite-extent impulse response (FIR) filter consisting of multiple hyperfan-shaped passbands. We design the 4-D sparse FIR filter as an optimal filter in the least-squares sense. Experimental results confirm that the proposed filter provides 63% average reduction in computational complexity with negligible degradation in the fidelity of multi-volumetric refocused LFs compared to a 4-D nonsparse FIR filter.

Low-complexity signal processing algorithms and reconfigurable digital hardware architectures are proposed for multi-depth filtering and occlusion suppression in 4-D light fields (LFs). The proposed multi-depth-pass (MDP) and multi-depth-reject (MDR) 4-D filters allow simultaneous enhancement of planar objects at multiple desired depths and attenuation of planar objects at multiple undesired depths, thereby achieving electronically tunable multi-depth focusing. Partial-separability and recursive nature of the proposed MDP and MDR transfer functions ensure ultra-low hardware complexity suitable for real-time processing of large volume of input samples encountered in 4-D LFs. Filter synthesis details and examples with synthetic and real LFs of size 15  ×  15 × 434  ×  625 are presented. A generic framework encapsulating a multitude of possible digital hardware realizations is described, employing J …

This paper studies optimum detectors and error rate analysis for wireless systems with low-resolution quantizers in the presence of fading and noise. A universal lower bound on the average symbol error probability (SEP), correct for all M-ary modulation schemes, is obtained when the number of quantization bits is not enough to resolve M signal points. In the special case of M-ary phase shift keying (M-PSK), the maximum likelihood detector is derived. Utilizing the structure of the derived detector, a general average SEP expression for M-PSK modulation with n-bit quantization is obtained when the wireless channel is subject to fading with a circularly-symmetric distribution. For the Nakagami-m fading, it is shown that a transceiver architecture with n-bit quantization is asymptotically optimum in terms of communication reliability if n ≥ log 2 M + 1. That is, the decay exponent for the average SEP is the same and equal …

Stroke patients with small central nervous system infarcts often demonstrate an acute dysexecutive syndrome characterized by difficulty with attention, concentration, and processing speed, independent of lesion size or location. We use magnetoencephalography (MEG) to show that disruption of network dynamics may be responsible. Nine patients with recent minor strokes and eight age-similar controls underwent cognitive screening using the Montreal cognitive assessment (MoCA) and MEG to evaluate differences in cerebral activation patterns. During MEG, subjects participated in a visual picture–word matching task. Task complexity was increased as testing progressed. Cluster-based permutation tests determined differences in activation patterns within the visual cortex, fusiform gyrus, and lateral temporal lobe. At visit 1, MoCA scores were significantly lower for patients than controls (median [interquartile range …

Four-dimensional (4-D) light fields (LFs) enable novel imaging technologies, which are traditionally based on two-dimensional images. In most of these applications, denoising of LFs is required as a preprocessing technique before any subsequent processing. We propose a real-time LF denoising method using a novel 4-D linear and shift-invariant hyperfan filter. The proposed method exploits sparsity of the spectrum of a LF and the 4-D hyperfan filter is implemented in the 4-D mixed-domain (i.e.,two-dimensional space and two-dimensional frequency) leading to significant reductions in computational and memory complexities. A software implementation of the proposed method provides better or comparable denoising performance for grayscale and color LFs with respect to the metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to previously reported linear LF denoising methods, while …

The increased popularity of investigations and exploits in the fields of neurological rehabilitation, human emotion recognition, and other relevant brain-computer interfaces demand the need for flexible electrophysiology data acquisition systems. Such systems often require to be multi-modal and multi-channel capable of acquiring and processing several different types of physiological signals simultaneously in realtime. Developments of modular and scalable electrophysiological data acquisition systems for experimental research enhance understanding and progress in the field. To contribute to such an endeavor, we present an open-source hardware project called High-Channel Count Electrophysiology or HiCCE, targeting to produce an easily-adaptable, cost-effective, and affordable electrophysiological acquisition system as an alternative solution for mostly available commercial tools and the current state of the …

Growing concerns over privacy invasion due to video camera based monitoring systems have made way to non-invasive Wi-Fi signal sensing based alternatives. This paper introduces a novel end-to-end deep learning framework that utilizes the changes in orthogonal frequency division multiplexing (OFDM) sub-carrier amplitude information to simultaneously predict the identity, activity and the trajectory of a user and create a user profile that is of similar utility to a one made through a video camera based approach. The novelty of the proposed solution is that the system is fully autonomous and requires zero user intervention unlike systems that require user originated initialization, or a user held transmitting device to facilitate the prediction. Experimental results demonstrate over 95% accuracy for user identification and activity recognition, while the user localization results exhibit a ±12cm error, which is a significant …

This paper describes a technique for de-embedding electromagnetic mutual coupling effects between nearest neighbors of a ULA receiver. Measured S-parameters across a range of frequencies of interest are used in a closed-form mathematical model that relates measured S-parameters, measured LNA reflection coefficients, and transmission line parameters to digital beamformer design equations, such that the beam shape distortions arising from mutual coupling can be compensated in the DSP algorithm. A 5.8 GHz 32-element receiver array with custom receivers and 32-channel Xilinx Virtex-6 Sx35 FPGA based DSP system implementing a real-time complex-valued Frost beamformer operating at IF is proposed to show the benefits of de-embedding mutual coupling for far-field side-lobe suppression. The worst-case side-lobe level is reduced by 11.2 dB, and the average side-lobe level is reduced by 5.2 dB, for …