A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications.

This paper reviews the state of the arts and trends of the AI-Based biomedical processing algorithms and hardware. The algorithms and hardware for different biomedical applications such as ECG, EEG and hearing aid have been reviewed and discussed. For algorithm design, various widely used biomedical signal classification algorithms have been discussed including support vector machine (SVM), back propagation neural network (BPNN), convolutional neural networks (CNN), probabilistic neural networks (PNN), recurrent neural networks (RNN), Short-term Memory Network (LSTM), fuzzy neural network and etc. The pros and cons of the classification algorithms have been analyzed and compared in the context of application scenarios. The research trends of AI-Based biomedical processing algorithms and applications are also discussed. For hardware design, various AI-Based biomedical processors have been reviewed and discussed, including ECG classification processor, EEG classification processor, EMG classification processor and hearing aid processor. Various techniques on architecture and circuit level have been analyzed and compared. The research trends of the AI-Based biomedical processor have also been discussed.

A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection.

The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learning framework to detect prostate cancer in the sequential CEUS images. The proposed method uniformly extracts features from both the spatial and the temporal dimensions by performing three-dimensional convolution operations, which captures the dynamic information of the perfusion process encoded in multiple adjacent frames for prostate cancer detection. The deep learning models were trained and validated against expert delineations over the CEUS images recorded using two types of contrast agents, i.e., the anti-PSMA based agent targeted to prostate cancer cells and the non-targeted blank agent. Experiments showed that the deep learning method achieved over 91 percent specificity and 90 percent average accuracy over the targeted CEUS images for prostate cancer detection, which was superior ( ) than previously reported approaches and implementations.

Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing.

Compressive sensing is widely used in biomedical applications, and the sampling matrix plays a critical role on both quality and power consumption of signal acquisition. It projects a high-dimensional vector of data into a low-dimensional subspace by matrix-vector multiplication. An optimal sampling matrix can ensure accurate data reconstruction and/or high compression ratio. Most existing optimization methods can only produce real-valued embedding matrices that result in large energy consumption during data acquisition. In this paper, we propose an efficient method that finds an optimal Boolean sampling matrix in order to reduce the energy consumption. Compared to random Boolean embedding, our data-driven Boolean sampling matrix can improve the image recovery quality by 9 dB. Moreover, in terms of sampling hardware complexity, it reduces the energy consumption by 4.6× and the silicon area by 1.9× over the data-driven real-valued embedding.

Healable capacitive touch screen sensors based on transparent composite electrodes comprising silver nanowires and a furan/maleimide diels-alder cycloaddition polymer.

A healable transparent capacitive touch screen sensor has been fabricated based on a healable silver nanowire-polymer composite electrode. The composite electrode features a layer of silver nanowire percolation network embedded into the surface layer of a polymer substrate comprising an ultrathin soldering polymer layer to confine the nanowires to the surface of a healable Diels-Alder cycloaddition copolymer and to attain low contact resistance between the nanowires. The composite electrode has a figure-of-merit sheet resistance of 18 Ω/sq with 80% transmittance at 550 nm. A surface crack cut on the conductive surface with 18 Ω is healed by heating at 100 °C, and the sheet resistance recovers to 21 Ω in 6 min. A healable touch screen sensor with an array of 8×8 capacitive sensing points is prepared by stacking two composite films patterned with 8 rows and 8 columns of coupling electrodes at 90° angle. After deliberate damage, the coupling electrodes recover touch sensing function upon heating at 80 °C for 30 s. A capacitive touch screen based on Arduino is demonstrated capable of performing quick recovery from malfunction caused by a razor blade cutting. After four cycles of cutting and healing, the sensor array remains functional.

Enantioselective synthesis of Anomala osakana pheromone and Janus integer pheromone: a flexible approach to chiral gamma-butyrolactones.

The enantioselective synthesis of Anomala osakana pheromone and Janus integer pheromone has been achieved without using any protecting groups. The synthesis involved using an asymmetric alkynylation to obtain gamma-hydroxy-alpha,beta-acetylenic esters with high ee (84%) and yields ( approximately 80%), followed by selective hydrogenation and lactonization in high overall yields (87% and 89%).

Asymmetric addition of phenylacetylene to aromatic ketones catalyzed by zinc or titanium complexes with chiral hydroxysulfonamide.

Various new chiral hydroxysulfonamide ligands (3a-3n, 4a-4d) were prepared. Compounds 3a, 3g, 3i, 3k-3n, 4a-4d could accelerate the reaction and reduce reaction time, and 3a, 3g, 3i, 3k-3n catalyzed the reaction without titanium. The results obtained were promising in terms of yields and enantiomeric excesses (3k up to 85% ee, 4a up to 83% ee).