LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function.

In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models.

π-Extended Thiazole-Containing Polymer Semiconductor for Balanced Charge-Carrier Mobilities.

A low-band gap semiconducting polymer with an acceptor-donor-acceptor architecture is newly designed and synthesized by incorporating a π-extended thiazole-vinylene-thiazole unit. The resulting thiazole-containing diketopyrrolopyrrole copolymer exhibits well-balanced ambipolar characteristics with hole mobility of up to 0.11 cm2 V-1 s-1 and electron mobility of up to 0.30 cm2 V-1 s-1 , which are suitable for applications in polymer electronics.

Neuron selection based on deflection coefficient maximization for the neural decoding of dexterous finger movements.

Future generations of brain-machine interface (BMI) will require more dexterous motion control such as hand and finger movements. Since a population of neurons in the primary motor cortex (M1) area is correlated with finger movements, neural activities recorded in M1 area are used to reconstruct an intended finger movement. In a BMI system, decoding discrete finger movements from a large number of input neurons does not guarantee a higher decoding accuracy in spite of the increase in computational burden. Hence, we hypothesize that selecting neurons important for coding dexterous flexion/extension of finger movements would improve the BMI performance. In this paper, two metrics are presented to quantitatively measure the importance of each neuron based on Bayes risk minimization and deflection coefficient maximization in a statistical decision problem. Since motor cortical neurons are active with movements of several different fingers, the proposed method is more suitable for a discrete decoding of flexion-extension finger movements than the previous methods for decoding reaching movements. In particular, the proposed metrics yielded high decoding accuracies across all subjects and also in the case of including six combined two-finger movements. While our data acquisition and analysis was done off-line and post processing, our results point to the significance of highly coding neurons in improving BMI performance.

Effects of cyano-substituents on the molecular packing structures of conjugated polymers for bulk-heterojunction solar cells.

The molecular packing structures of two conjugated polymers based on alkoxy naphthalene, one with cyano-substituents and one without, have been investigated to determine the effects of electron-withdrawing cyano-groups on the performance of bulk-heterojunction solar cells. The substituted cyano-groups facilitate the self-assembly of the polymer chains, and the cyano-substituted polymer:PC71BM blend exhibits enhanced exciton dissociation to PC71BM. Moreover, the electron-withdrawing cyano-groups lower the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) levels of the conjugated polymer, which leads to a higher open circuit voltage (V(OC)) and a lower energy loss during electron transfer from the donor to the acceptor. A bulk-heterojunction device fabricated with the cyano-substituted polymer:PC71BM blend has a higher V(OC) (0.89 V), a higher fill factor (FF) (51.4%), and a lower short circuit current (J(SC)) (7.4 mA/cm(2)) than that of the noncyano-substituted polymer:PC71BM blend under AM 1.5G illumination with an intensity of 100 mW cm(-2). Thus, the cyano-substitution of conjugated polymers may be an effective strategy for optimizing the domain size and crystallinity of the polymer:PC71BM blend, and for increasing V(OC) by tuning the HOMO and LUMO energy levels of the conjugated polymer.

Neuron Selection by Relative Importance for Neural Decoding of Dexterous Finger Prosthesis Control Application.

Future generations of upper limb prosthesis will have dexterous hand with individual fingers and will be controlled directly by neural signals. Neurons from the primary motor (M1) cortex code for finger movements and provide the source for neural control of dexterous prosthesis. Each neuron's activation can be quantified by the change in firing rate before and after finger movement, and the quantified value is then represented by the neural activity over each trial for the intended movement. Since this neural activity varies with the intended movement, we define the relative importance of each neuron independent of specific intended movements. The relative importance of each neuron is determined by the inter-movement variance of the neural activities for respective intended movements. Neurons are ranked by the relative importance and then a subpopulation of rank-ordered neurons is selected for the neural decoding. The use of the proposed neuron selection method in individual finger movements improved decoding accuracy by 21.5% in the case of decoding with only 5 neurons and by 9.2% in the case of decoding with only 10 neurons. With only 15 highly-ranked neurons, a decoding accuracy of 99.5% was achieved. The performance improvement is still maintained when combined movements of two fingers were included though the decoding accuracy fell to 95.7%. Since the proposed neuron selection method can achieve the targeting accuracy of decoding algorithms with less number of input neurons, it can be significant for developing brain-machine interfaces for direct neural control of hand prostheses.