Learning How to Demodulate from Few Pilots via Meta-Learning

Consider an Internet-of-Things (IoT) scenario in which devices transmit sporadically using short packets with few pilot symbols. Each device transmits over a fading channel and is characterized by an amplifier with a unique non-linear transfer function. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the amplifier's distortion. This paper proposes to tackle this problem using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training in order to learn a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Numerical results validate the advantages of the approach as compared to training schemes that either do not leverage prior transmissions or apply a standard learning algorithm on previously received data

Graphene and hexagonal boron nitride heterostructures for beyond CMOS applications

Scaling limits of conventional complementary metal oxide semiconductor (CMOS) technology has motivated the research of numerous beyond CMOS device concepts. One such device is the interlayer tunnel FET (ITFET). This device is demonstrated using the two-dimensional (2D) materials bilayer graphene and hexagonal boron nitride (hBN). Stacking these materials together, we fabricate a double bilayer graphene and hBN heterostructure where the two graphene layers function as the top and bottom electrodes and the hBN as the tunnel barrier of the ITFET. Significant negative differential resistance (NDR) in the interlayer current-voltage characteristic is demonstrated at room temperature. Electrostatic analysis reveals that the multiple NDR peaks are due to energetic band alignment between the two sub-bands of the top and bottom bilayer graphene at the K-point of the Brillouin zone. Temperature dependent and parallel magnetic field measurements are conducted to further confirm that the conduction mechanism is momentum and energy conserving resonant tunneling. In addition, we demonstrate how the NDR can be used for implementing a one-transistor static random access memory element. Improvements in the transfer method which allowed rotationally aligned top and bottom electrode layers, made possible extensive experimental studies of 2D heterostructure based ITFETs. Utilizing such technique, we conducted experiments involving thicker multilayer graphene. We analyze how the graphene thickness and stacking order can influence the resonance condition and how this affects the overall device characteristic. The effects of interlayer hBN scaling such as increased tunneling current and peak position shifting is also briefly dealt with. Current-voltage simulations based on Bardeen transfer Hamiltonian approach were conducted for these devices, and it is shown that the peak positions predicted by theory match well with those obtained through measurements.Electrical and Computer Engineerin

Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning

This paper considers an Internet-of-Things (IoT) scenario in which devices sporadically transmit short packets with few pilot symbols over a fading channel. Devices are characterized by unique transmission non-idealities, such as I/Q imbalance. The number of pilots is generally insufficient to obtain an accurate estimate of the end-to-end channel, which includes the effects of fading and of the transmission-side distortion. This paper proposes to tackle this problem by using meta-learning. Accordingly, pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots. Various state-of-the-art meta-learning schemes are adapted to the problem at hand and evaluated, including Model-Agnostic Meta-Learning (MAML), First-Order MAML (FOMAML), REPTILE, and fast Context Adaptation VIA meta-learning (CAVIA). Both offline and online solutions are developed. In the latter case, an integrated online meta-learning and adaptive pilot number selection scheme is proposed. Numerical results validate the advantages of meta-learning as compared to training schemes that either do not leverage prior transmissions or apply a standard joint learning algorithms on previously received data.Comment: journal paper to appear in IEEE Transactions on Signal Processing, subsumes (arXiv:1903.02184

Inverse problem for a planar conductivity inclusion

This paper concerns the inverse problem of determining a planar conductivity inclusion. Our aim is to analytically recover from the generalized polarization tensors (GPTs), which can be obtained from exterior measurements, a homogeneous inclusion with arbitrary constant conductivity. The primary outcome of recovering a homogeneous inclusion is an inversion formula in terms of the GPTs for conformal mapping coefficients associated with the inclusion. To prove the formula, we establish matrix factorizations for the GPTs.Comment: 28 pages, 6 figure

Facilitation of corticospinal excitability by virtual reality exercise following anodal transcranial direct current stimulation in healthy volunteers and subacute stroke subjects

BACKGROUND: There is growing evidence that the combination of non-invasive brain stimulation and motor skill training is an effective new treatment option in neurorehabilitation. We investigated the beneficial effects of the application of transcranial direct current stimulation (tDCS) combined with virtual reality (VR) motor training. METHODS: In total, 15 healthy, right-handed volunteers and 15 patients with stroke in the subacute stage participated. Four different conditions (A: active wrist exercise, B: VR wrist exercise, C: VR wrist exercise following anodal tDCS (1 mV, 20 min) on the left (healthy volunteer) or affected (stroke patient) primary motor cortex, and D: anodal tDCS without exercise) were provided in random order on separate days. We compared during and post-exercise corticospinal excitability under different conditions in healthy volunteers (A, B, C, D) and stroke patients (B, C, D) by measuring the changes in amplitudes of motor evoked potentials in the extensor carpi radialis muscle, elicited with single-pulse transcranial magnetic stimulation. For statistical analyses, a linear mixed model for a repeated-measures covariance pattern model with unstructured covariance within groups (healthy or stroke groups) was used. RESULTS: The VR wrist exercise (B) facilitated post-exercise corticospinal excitability more than the active wrist exercise (A) or anodal tDCS without exercise (D) in healthy volunteers. Moreover, the post-exercise corticospinal facilitation after tDCS and VR exercise (C) was greater and was sustained for 20 min after exercise versus the other conditions in healthy volunteers (A, B, D) and in subacute stroke patients (B, D). CONCLUSIONS: The combined effect of VR motor training following tDCS was synergistic and short-term corticospinal facilitation was superior to the application of VR training, active motor training, or tDCS without exercise condition. These results support the concept of combining brain stimulation with VR motor training to promote recovery after a stroke. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1743-0003-11-124) contains supplementary material, which is available to authorized users

The Ship Detection Using Airborne and In-situ Measurements Based on Hyperspectral Remote Sensing

International audienceMaritime accidents around the Korean Peninsula are increasing, and the ship detection research using remote sensing data is consequently becoming increasingly important. This study presented a new ship detection algorithm using hyperspectral images that provide the spectral information of several hundred channels in the ship detection field, which depends on high resolution optical imagery. We applied a spectral matching algorithm between the reflection spectrum of the ship deck obtained from two field observations and the ship and seawater spectrum of the hyperspectral sensor of an airborne visible/infrared imaging spectrometer. A total of five detection algorithms were used, namely spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), spectral angle mapper (SAM), and spectral information divergence (SID). SDS showed an error in the detection of seawater inside the ship, and SAM showed a clear classification result with a difference between ship and seawater of approximately 1.8 times. Additionally, the present study classified the vessels included in hyperspectral images by presenting the adaptive thresholds of each technique. As a result, SAM and SID showed superior ship detection abilities compared to those of other detection algorithms

ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network

Recent developments in artificial neural networks and their learning algorithms have enabled new research directions in computer vision, language modeling, and neuroscience. Among various neural network algorithms, spiking neural networks (SNNs) are well-suited for understanding the behavior of biological neural circuits. In this work, we propose to guide the training of a sparse SNN in order to replace a sub-region of a cultured hippocampal network with limited hardware resources. To verify our approach with a realistic experimental setup, we record spikes of cultured hippocampal neurons with a microelectrode array (in vitro). The main focus of this work is to dynamically cut unimportant synapses during SNN training on the fly so that the model can be realized on resource-constrained hardware, e.g., implantable devices. To do so, we adopt a simple STDP learning rule to easily select important synapses that impact the quality of spike timing learning. By combining the STDP rule with online supervised learning, we can precisely predict the spike pattern of the cultured network in real-time. The reduction in the model complexity, i.e., the reduced number of connections, significantly reduces the required hardware resources, which is crucial in developing an implantable chip for the treatment of neurological disorders. In addition to the new learning algorithm, we prototype a sparse SNN hardware on a small FPGA with pipelined execution and parallel computing to verify the possibility of real-time replacement. As a result, we can replace a sub-region of the biological neural circuit within 22 μs using 2.5 × fewer hardware resources, i.e., by allowing 80% sparsity in the SNN model, compared to the fully-connected SNN model. With energy-efficient algorithms and hardware, this work presents an essential step toward real-time neuroprosthetic computation