Previous work has shown that Deep Neural Networks (DNNs), including those currently in use in many fields, are extremely vulnerable to maliciously crafted inputs, known as adversarial examples. Despite extensive and thorough research of adversarial examples in many areas, adversarial 3D data, such as point clouds, remain comparatively unexplored. The study of adversarial 3D data is crucial considering its impact in real-life, high-stakes scenarios including autonomous driving. In this paper, we propose a novel adversarial attack against PointNet++, a deep neural network that performs classification and segmentation tasks using features learned directly from raw 3D points. In comparison to existing works, our attack generates not only adversarial point clouds, but also robust adversarial objects that in turn generate adversarial point clouds when sampled both in simulation and after construction in real world. We also demonstrate that our objects can bypass existing defense mechanisms designed especially against adversarial 3D data.

Droplet-based digital microfluidics technology has now come of age, and software-controlled biochips for healthcare applications are starting to emerge. However, today's digital microfluidic biochips suffer from the drawback that there is no feedback to the control software from the underlying hardware platform. Due to the lack of precision inherent in biochemical experiments, errors are likely during droplet manipulation; error recovery based on the repetition of experiments leads to wastage of expensive reagents and hard-to-prepare samples. By exploiting recent advances in the integration of optical detectors (sensors) into a digital microfluidics biochip, we present a physical-aware system reconfiguration technique that uses sensor data at intermediate checkpoints to dynamically reconfigure the biochip. A cyberphysical resynthesis technique is used to recompute electrode-actuation sequences, thereby deriving new schedules, module placement, and droplet routing pathways, with minimum impact on the time-to-response.

Drug screening based on in-vitro primary tumor cell culture has demonstrated potential in personalized cancer diagnosis. However, the limited number of tumor cells, especially from patients with early stage cancer, has hindered the widespread application of this technique. Hence, we developed a digital microfluidic system for drug screening using primary tumor cells and established a working protocol for precision medicine. Smart control logic was developed to increase the throughput of the system and decrease its footprint to parallelly screen three drugs on a 4 × 4 cm² chip in a device measuring 23 × 16 × 3.5 cm³. We validated this method in an MDA-MB-231 breast cancer xenograft mouse model and liver cancer specimens from patients, demonstrating tumor suppression in mice/patients treated with drugs that were screened to be effective on individual primary tumor cells. Mice treated with drugs screened on-chip as ineffective exhibited similar results to those in the control groups. The effective drug identified through on-chip screening demonstrated consistency with the absence of mutations in their related genes determined via exome sequencing of individual tumors, further validating this protocol. Therefore, this technique and system may promote advances in precision medicine for cancer treatment and, eventually, for any disease.

Recent advances in flow-based microfluidics have led to the emergence of biochemistry-on-a-chip as a new paradigm in clinical diagnostics and biomolecular recognition. However, a potential roadblock in the deployment of microfluidic biochips is the lack of test techniques to screen defective devices before they are used for biochemical analysis. Defective chips lead to repetition of experiments, which is undesirable due to high reagent cost and limited availability of samples. Prior work on fault detection in biochips has been limited to digital (“droplet”) microfluidics and other electrode-based technology platforms. The paper proposes the first approach for automated testing of flow-based microfluidic biochips that are designed using membrane-based valves for flow control. The proposed test technique is based on a behavioral abstraction of physical defects in microchannels and valves. The flow paths and flow control in the microfluidic device are modeled as a logic circuit composed of Boolean gates, which allows test generation to be carried out using standard automatic test pattern generation tools. The tests derived using the logic circuit model are then mapped to fluidic operations involving pumps and pressure sensors in the biochip. Feedback from pressure sensors can be compared to expected responses based on the logic circuit model, whereby the types and positions of defects are identified. We show how a fabricated biochip can be tested using the proposed method, and demonstrate experimental results for two additional fabricated chips.

As the microfluidic technology advances, the design complexity of digital microfluidic biochips (DMFB) are expected to explode in the near future. One of the most critical challenges for DMFB design is the droplet routing problem, which schedules the movement of each droplet in a time-multiplexed manner. In this paper, we propose a fast routability- and performance-driven droplet router for DMFBs. The main contributions of our work are: (1) a global moving vector analysis for constructing preferred routing tracks to minimize the number of used unit cells; (2) an entropy-based equation to determine the routing order of droplets for better routability; (3) a routing compaction technique by dynamic programming to minimize the latest arrival time of droplets. Experimental results show that our algorithm achieves 100% routing completion for all test cases on three Benchmark Suites while the previous algorithms are not. In addition to routability, compared with the state-of-the-art high-performance routing on the Benchmark Suite I (Cho and Pan, 2008), the experimental results still show that our algorithm performed better in runtime by 40%, reduced the latest arrival time by 21%, reduced the used unit cells by 10%. Furthermore, experiment results on Benchmark Suite II and III are also very promising. Based on the evaluation of three Benchmark Suites, our algorithm demonstrates the efficiency and robustness of handling complex droplet routing problem over the existing algorithms.

Advances in droplet-based digital microfluidics have led to the emergence of biochips for automating laboratory procedures in biochemistry and molecular biology. These devices enable the precise control of microliter of nanoliter volumes of biochemical samples and reagents. They combine electronics with biology, and integrate various bioassay operations, such as sample preparation, analysis, separation, and detection. Compared to conventional laboratory procedures, which are cumbersome and expensive, miniaturized digital microfluidic biochips (DMFBs) offer the advantages of higher sensitivity, lower cost, system integration, and less likelihood of human error. This tutorial paper provides an overview of DMFBs and describes emerging computer-aided design (CAD) tools for the automated synthesis and optimization of biochips, from physical modeling to fluidic-level synthesis and then to chip-level design. By efficiently utilizing the electronic design automation (EDA) technique on emerging CAD tools, users can concentrate on the development of nanoscale bioas-says, leaving chip optimization and implementation details to design-automation tools.