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Digital microfluidic biochips (DMFBs), which are used in various fields like DNA analysis, clinical diagnosis, and PCR testing, have made biochemical experiments more compact, efficient, and user-friendly than the previous methods. However, their reliability is often compromised by their inability to adapt to all kinds of errors. Errors in biochips can be categorized into two types: known errors, and unknown errors. Known errors are detectable before the start of the routing process using sensors or cameras. Unknown errors, in contrast, only become apparent during the routing process and remain undetected by sensors or cameras, which can unexpectedly stop the routing process and diminish the reliability of biochips. This paper introduces a deep reinforcement learning-based routing algorithm, designed to manage not only known errors but also unknown errors. Our experiments demonstrated that our algorithm outperformed the previous ones in terms of the success rate of the routing, in the scenarios including both known errors and unknown errors. Additionally, our algorithm contributed to detecting unknown errors during the routing process, identifying the most efficient routing path with a high probability.

Digital microfluidic biochip (DMFB) has attracted attention in the biochemical and medical industries. In particular, a microelectrode dot array (MEDA) biochip, which is composed of a two-dimensional microelectrode array, enables to realize fine-grained manipulation such as dilution, mixing, sensing, and so on in real-time. Unlike existing DMFB biochips, a MEDA architecture allows microelectrodes to control a certain volume of droplet in a fine-grained manner and can vary droplet volume and shape in such a way that it efficiently conducts synthesis and manipulation of droplets. There have been many works in order to improve the efficiency of synthesis of MEDA biochips; however, the synthesis, especially droplet routing, has never considered the shape-dependent velocity of droplets. In this paper, we propose the droplet routing techniques for MEDA biochips with shape-dependent velocity of droplets. The proposed techniques take the advantage of variant velocities of droplets dependent on the shapes and aim to reduce the overall routing time of a droplet from a source to a destination. Simulation results confirm that the proposed techniques can shorten the routing time by 80% compared to the state-of-the-art techniques.

Delivery drones have been attracting attention as a means of solving recent logistics issues, and many companies are focusing on their practical applications. Many research studies on delivery drones have been active for several decades. Among them, extended routing problems for drones have been proposed based on the Traveling Salesman Problem (TSP), which is used, for example, in truck vehicle routing problems. In parcel delivery by drones, additional constraints such as battery capacity, payload, and weather conditions need to be considered. This study addresses the routing problem for delivery drones. Most existing studies assume that the drone’s flight speed is constant regardless of the load. On the other hand, some studies assume that the flight speed varies with the load. This routing problem is called the Flight Speed-Aware Traveling Salesman Problem (FSTSP). The complexity of the drone flight speed function in this problem makes it difficult to solve the routing problem using general-purpose mathematical optimization solvers. In this study, the routing problem is reduced to an integer programming problem by using linear and quadratic approximations of the flight speed function. This enables us to solve the problem using general-purpose mathematical optimization solvers. In experiments, we compared the existing and proposed methods in terms of solving time and total flight time. The experimental results show that the proposed method with multiple threads has a shorter solving time than the state-of-the-art method when the number of customers is 17 or more. In terms of total flight time, the proposed methods deteriorate by an average of 0.4% for integer quadratic programming and an average of 1.9% for integer cubic programming compared to state-of-the-art methods. These experimental results show that the quadratic and cubic approximations of the problem have almost no degradation of the solution.

There are certain growth stages and spectral regions that are optimal for obtaining a high accuracy in rice yield prediction by remote sensing. However, there is insufficient knowledge to establish a yield prediction model widely applicable for growth environments with different meteorological factors. In this study, high temporal resolution remote sensing using unmanned aerial vehicle-based hyperspectral imaging was performed to improve the yield prediction accuracy of paddy rice cultivated in different environments. The normalized difference spectral index, an index derived from canopy reflectance at any two spectral bands, was used for a simple linear regression analysis to estimate the optimum stage and spectral region for yield prediction. Although the highest prediction accuracy was obtained from the red-edge and near-infrared regions at the booting stage, the generalization performance for different growth environments was slightly higher at the heading stage than at the booting stage. The coefficient of determination and the root mean squared percentage error for the heading stage were R2 = 0.858 and RMSPE = 7.52%, and they were R2 = 0.853 and RMSPE = 9.22% for the booting stage, respectively. In addition, a correction by solar radiation was ineffective at improving the prediction accuracy. The results demonstrate the possibility of establishing regression models with a high prediction accuracy from a single remote sensing measurement at the heading stage without using meteorological data correction.

High‐level synthesis (HLS), which automatically synthesizes a register‐transfer level (RTL) circuit from a behavioral description written in a high‐level programming language such as C/C++, is becoming a more popular technique for improving design productivity. In general, HLS tools often generate a circuit with a larger area than those of hand‐designed ones. One reason for this issue is that HLS tools often generate multiple instances of the same module from a function. To eliminate such a redundancy in circuit area in HLS, HLS tools are capable of sharing modules. Function‐level module sharing at a behavioral description written in a high‐level programming language may promote function reuse to increase effectiveness and reduce circuit area. In this paper, we present two HLS techniques for module sharing at the function level.

Recent years, autonomous drones have attracted attention in many fields due to their convenience. Autonomous drones require precise depth information so as to avoid collision to fly fast and both of RGB image and LiDAR point cloud are often employed in applications based on Convolutional Neural Networks (CNNs) to estimate the distance to obstacles. Such applications are implemented onboard embedded systems. In order to precisely estimate the depth, such CNN models are in general so complex to extract many features that the computational complexity increases, requiring long inference time. In order to solve the issue, we employ optical flow to aid in-depth estimation. In addition, we propose a new attention structure that makes maximum use of optical flow without complicating the network. Furthermore, we achieve improved performance without modifying the depth estimator by adding a perceptual discriminator in training. The proposed model is evaluated through accuracy, error, and inference time on the KITTI dataset. In the experiments, we have demonstrated the proposed method achieves better performance by up to 34% accuracy, 55% error reduction and 66% faster inference time on Jetson nano compared to previous methods. The proposed method is also evaluated through a collision avoidance in simulated drone flight and achieves the lowest collision rate of all estimation methods. These experimental results show the potential of proposed method to be used in real-world autonomous drone flight applications.

The convolutional neural network is a subfield of artificial neural networks and has made great achievements in various domains over the past decade. The technique has been widely applied including computer vision, natural language processing, image analysis, etc. The field of measurement also achieves exciting progress with convolutional neural networks. This paper gives a survey of convolutional neural networks from software algorithms to hardware architectures perspective and the applications in measurement. The survey hopes to give contributions to the cross-research of artificial intelligent and measurement.

Approximate computing is a promising approach to the design of area–power-performance-efficient circuits for computation error-tolerant applications such as image processing and machine learning. Approximate functional units, such as approximate adders and approximate multipliers, have been actively studied for the past decade, and some of these approximate functional units can dynamically change the degree of computation accuracy. The greater their computational inaccuracy, the faster they are. This study examined the high-level synthesis of approximate circuits that take advantage of such accuracy-controllable functional units. Scheduling methods based on integer linear programming (ILP) and list scheduling were proposed. Under resource and time constraints, the proposed method tries to minimize the computation error of the output value by selectively multi-cycling operations. Operations that have a large impact on the output accuracy are multi-cycled to perform exact computing, whereas operations with a small impact on the accuracy are assigned a single cycle for approximate computing. In the experiments, we explored the trade-off between performance, hardware cost, and accuracy to demonstrate the effectiveness of this work.

With the increasing demand for fast, accurate, and reliable biological sensor systems, miniaturized systems have been aimed at droplet-based sensor systems and have been promising. A micro-electrode dot array (MEDA) biochip, which is one kind of the miniaturized systems for biochemical protocols such as dispensing, dilutions, mixing, and so on, has become widespread due to enabling dynamical control of the droplets in microfluidic manipulations. In MEDA biochips, the electrowetting-on-dielectric (EWOD) technique stands out since it can actuate droplets with nano/picoliter volumes. Microelectrode cells on MEDA actuate multiple droplets simultaneously to route locations for the purpose of the biochemical operations. Taking advantage of the feature, droplets are often routed in parallel to achieve high-throughput outcomes. Regarding parallel manipulation of multiple droplets, however, the droplets are known to be initially placed at a distant position to avoid undesirable mixing. The droplets thus result in traveling a long way for a manipulation, and the required biochip size for routing is also enlarged. This paper proposes a routing method for droplets to reduce the biochip size on a MEDA biochip with the allowance of splitting during routing operations. We mathematically derive the routing problem, and the experiments demonstrate that our proposal can significantly reduce the biochip size by 70.8% on average, compared to the state-of-the-art method.

In this work, we propose a method for estimating depth for an image of a monocular camera in order to avoid a collision for the autonomous flight of a drone. The highest flight speed of a drone is generally approximate 22.2 m/s, and long-distant depth information is crucial for autonomous flights since if the long-distance information is not available, the drone flying at high speeds is prone to collisions. However, long-range, measurable depth cameras are too heavy to be equipped on a drone. This work applies Pix2Pix, which is a kind of Conditional Generative Adversarial Nets (CGAN). Pix2Pix generates depth images from a monocular camera. Additionally, this work applies optical flow to enhance the accuracy of depth estimation. In this work, we propose a highly accurate depth estimation method that effectively embeds an optical flow map into a monocular image. The models are trained with taking advantage of AirSim, which is one of the flight simulators. AirSim can take both monocular and depth images over a hundred meter in the virtual environment, and our model generates a depth image that provides the long-distance information than images captured by a common depth camera. We evaluate accuracy and error of our proposed method using test images in AirSim. In addition, the proposed method is utilized for flight simulation to evaluate the effectiveness to collision avoidance. As a result, our proposed method is higher accuracy and lower error than a state of work. Moreover, our proposed method is lower collision than a state of work.

Delivery drones have been attracting attention as one of the promising technologies to deliver packages. Several research studies on routing problems specifically for drone delivery scenarios have extended Vehicle Routing Problems (VRPs). Most existing VRPs are based on Traveling Salesman Problems (TSPs) for minimizing the overall distance. On the other hand, VRPs for drone delivery have been aware of energy consumption due to the consideration of battery capacity. Despite hovering motions with loading packages accounting for a large portion of energy consumption since delivery drones need to hover with several packages, little research has been conducted on drone routing problems that aim at the minimization of overall flight times. In addition, flight time is strongly influenced by windy conditions such as headwinds and tailwinds. In this paper, we propose a VRP for drone delivery in which flight time is dependent on the weight of packages in a windy environment, called Flight Speed-aware Vehicle Routing Problem with Load and Wind (FSVRPLW). In this paper, flight speed changes depending on the load and wind. Specifically, a heavier package slows down flight speeds and a lighter package speeds up flight speeds. In addition, a headwind slows down flight speeds and a tailwind speed up flight speeds. We mathematically derived the problem and developed a dynamic programming algorithm to solve the problem. In the experiments, we investigate how much impact both the weight of packages and the wind have on the flight time. The experimental results indicate that taking loads and wind into account is very effective in reducing flight times. Moreover, the results of comparing the effects of load and wind indicate that flight time largely depends on the weight of packages.

Vision-based fall-detection methods have been previously studied but many have limitations in terms of practicality. Due to differences in rooms, users do not set the camera or sensors at the same height. However, few studies have taken this into consideration. Moreover, some fall-detection methods are lacking in terms of practicality because only standing, sitting and falling are taken into account. Hence, this study constructs a data set consisting of various daily activities and fall events and studies the effect of camera/sensor height on fall-detection accuracy. Each activity in the data set is carried out by eight participants in eight directions and taken with the depth camera at five different heights. Many related studies heavily depended on human segmentation by using Kinect SDK but this is not reliable enough. To address this issue, this study proposes Enhanced Tracking and Denoising Alex-Net (ETDA-Net) to improve tracking and denoising performance and classify fall and non-fall events. Experimental results indicate that fall-detection accuracy is affected by camera height, against which ETDA-Net is robust, outperforming traditional deep learning based fall-detection methods.

Background and Aims: Similar clinical and pathological features have been observed in Alzheimer's disease (AD) and Parkinson's disease with dementia (PDD). Both the peroxisome proliferator-activated receptor-γ (PPAR-γ) gene and the peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) gene are candidates modifying the risk for both diseases. The aim of this study was to clarify whether common single nucleotide polymorphisms (SNPs) of the PPAR-γ gene and the PGC-1α gene affect the onset of AD and PDD genetically. Methods: Four exonic SNPs of both genes (rs1801282 and rs3856806 of the PPAR-γ gene, rs3736265 and rs8192678 of the PGC-1α gene) were genotyped in 171 AD patients, 136 age-matched controls and 53 PDD patients. Haplotype analysis and logistic regression analysis with apolipoprotein E (APO E) status were performed for AD. Results: There was no statistical difference between AD cases and controls for the 4 SNPs, nor was there any statistical difference between PDD cases and controls for the 4 SNPs. We could not find any synergetic associations between these SNPs, APO E4 and AD. Conclusions: The 4 SNPs studied here did not influence the risk for AD in a Japanese population. As the number of PDD cases was small, comprehensive genetic studies considering diabetes would be needed.

Dynamic Partial Reconfiguration technology coupled with an Operating System for Reconfigurable Systems (OS4RS) allows for implementation of a hardware task concept, that is, an active computing object which can contend for reconfigurable computing resources and request OS services in a way software task does in a conventional OS. In this work, we show a complete model and implementation of a lightweight OS4RS supporting preemptable and clock-scalable hardware tasks. We also propose a novel, lightweight scheduling mechanism allowing for timely and priority-based reservation of reconfigurable resources, which aims at usage of preemption only at the time it brings benefits to the performance of a system. The architecture of the scheduler and the way it schedules allocations of the hardware tasks result in shorter latency of system calls, thereby reducing the overall OS overhead. Finally, we present a novel model and implementation of a channel-based intertask communication and synchronization suitable for software-hardware multitasking with preemptable and clock-scalable hardware tasks. It allows for optimizations of the communication on per task basis and utilizes point-to-point message passing rather than shared-memory communication, whenever it is possible. Extensive overhead tests of the OS4RS services as well as application speedup tests show efficiency of our approach.