Various forms of biohybrid robots have been developed; however, creating robots with multiple degrees of freedom remains a challenging task. In this paper, we developed a multi-joint biohybrid robot by using skeletal muscle tissue. To achieve this, we first developed a modular bio-actuator actuated by skeletal muscle tissues. The objective of this study was to enhance the contraction force of the actuator and establish optimal experimental conditions for creating high-performance robots. By applying continuous electrical stimulation for five days during culture of bio-actuator, we were able to increase the contraction force by more than threefold. Additionally, we determined the appropriate electric field based on the electrode distance, which enabled us to establish an optimal experimental setup. We also confirmed that connecting the actuators in series can significantly increase the moving distance. Connecting two actuators in series resulted in a total movement distance equivalent to the sum of the distances of each actuator. This finding suggests the potential to create robots with a larger operational workspace. Using these actuators, we first constructed a manipulator with a rotational joint. This research is expected to contribute not only to the development of various robots utilizing bio-actuators but also to advancements in biology technology.

In this paper, we fabricated a soft sensor based on PEDOT:PSS for thin film structure. The developed soft sensor can measure the contraction force at real time to be embedded in a modular bio-actuator [1]. The modular actuator generated contraction forces at 0.3 mN when applying electric pulse stimulation. To measure millinewton contraction forces and make a built in sensor, we fabricated a soft sensor using PEDOT:PSS-PDMS film. To verify that the sensor can measure the force of the actuator and can be integrated to the actuator, we analyzed characteristic of the sensor. First, we measure Young's modulus of the sensor and compare them with the bio-actuator. From the previous research [2], the Young's modulus of the bio-actuator and sensor were 45.8 kPa and 165 kPa, respectively. In addition, we simulated the sensors to estimate the change of the displacement according to the applied force. Next, we have experiments by stretching sensors using stepping motor to measure the resistance change of the sensor. From the simulation data, the displacement change is 23 µm when applying 0.3 mN of forces and then we detect the displacement change smaller than is 20 µm from the experiments. Finally, we analyzed the movement of the bio-actuator when applying stimulation using high speed camera and time response of the developed sensor. The actuator was contracted to the maximum after 150 ms from the electrical stimulation and the sensor detected the repeated motion at 10 Hz without time delay. As a result, the proposed sensor can measure the force of bioactuator at real time.

Recently, requirement of city monitoring and maintenance using ICT techniques increases with the help of transportation system. In addition, the spread of COVID-19 has increased the demand for managing pedestrian traffic volume. To contribute to these trends, in this paper, we propose a new pedestrian radar map system in order to estimate pedestrian density on streets and sidewalks. Our system uses e-bikes to collect 360-degree images and visualize pedestrian positions as a radar map. In evaluations, we confirm the accuracies of the radar maps and pedestrian density by using KITTI dataset and by carrying out a field experiment.

Microinjection technology is applied widely in biomedical research for the purposes of gene manipulation and microinsemination. Generally, microinjection is performed under an optical microscope environment through image presentation of the targets. To perform the microinjection process, it is necessary to place multiple cells in the same droplet and perform multiple injections. This process requires observation at different magnifications for the injection and embryo transfer processes, with the operator required to change the magnification and light intensity each time. The complexity of the process can lead to variations in the accuracy, reproducibility, and productivity of the course of multiple microinjections. To simplify the microinjection process and reduce the workload on the operator, we propose a micromanipulation system that enables both wide-range and high-resolution video shooting with free viewpoint selection. The effectiveness of the proposed system is verified through microinjection experiments using porcine embryos.

In spite of recent high attention of the biohybrid robot system, the previous researches focused on actuation system depend on simple on/off control without feedback control. To solve this problem, we proposed a soft sensor for feedback control of a bio-actuator driven by skeletal muscle. The proposed soft sensor can measure contraction forces of the proposed bio-actuator [1]. The bio-actuator was constructed with tendon structure and culture template made by polydimethylsiloxane (PDMS). It generated contraction forces at 0.3 mN when applying electrical stimulation. To measure that kind of small amount of contraction forces (0.3 mN), we fabricated a soft sensor using liquid metal, Galinstan, and HTV-2000. At first, we measured the Young’s modulus of the bioactuator and sensor and then fabricated the soft sensor having 68.52 kPa of Young’s modulus that is similar the bioactuator (45.8 kPa). Next, we simulated the sensor to estimate the resistance change according to the applied force. Since the resistance change is too small, we design the circuit to amplify the signal. Then, we detect very small resistance at milli-ohm. In addition, we analyzed time response to detect signal of actuator faster than 200 ms. As a result, the proposed sensor can measure the force of bioactuator without time delay.

Image compression is a fundamental research field and many well-known compression standards have been developed for many decades. Recently, learned compression methods exhibit a fast development trend with promising results. However, there is still a performance gap between learned compression algorithms and reigning compression standards, especially in terms of widely used PSNR metric. In this paper, we explore the remaining redundancy of recent learned compression algorithms. We have found accurate entropy models for rate estimation largely affect the optimization of network parameters and thus affect the rate-distortion performance. Therefore, in this paper, we propose to use discretized Gaussian Mixture Likelihoods to parameterize the distributions of latent codes, which can achieve a more accurate and flexible entropy model. Besides, we take advantage of recent attention modules and incorporate them into network architecture to enhance the performance. Experimental results demonstrate our proposed method achieves a state-of-the-art performance compared to existing learned compression methods on both Kodak and high-resolution datasets. To our knowledge our approach is the first work to achieve comparable performance with latest compression standard Versatile Video Coding (VVC) regarding PSNR. More importantly, our approach generates more visually pleasant results when optimized by MS-SSIM.

Lossless image compression is an important task in the field of multimedia communication. Traditional image codecs typically support lossless mode, such as WebP, JPEG2000, FLIF. Recently, deep learning based approaches have started to show the potential at this point. HyperPrior is an effective technique proposed for lossy image compression. This paper generalizes the hyperprior from lossy model to lossless compression, and proposes a L2-norm term into the loss function to speed up training procedure. Besides, this paper also investigated different parameterized models for latent codes, and propose to use Gaussian mixture likelihoods to achieve adaptive and flexible context models. Experimental results validate our method can outperform existing deep learning based lossless compression, and outperform the JPEG2000 and WebP for JPG images.

Learned image compression (LIC) has reached the traditional hand-crafted methods such as JPEG2000 and BPG in terms of the coding gain. However, the large model size of the network prohibits the usage of LIC on resource-limited embedded systems. This paper presents a LIC with 8-bit fixed-point weights. First, we quantize the weights in groups and propose a non-linear memory-free codebook. Second, we explore the optimal grouping and quantization scheme. Finally, we develop a novel weight clipping fine tuning scheme. Experimental results illustrate that the coding loss caused by the quantization is small, while around 75% model size can be reduced compared with the 32-bit floating-point anchor. As far as we know, this is the first work to explore and evaluate the LIC fully with fixed-point weights, and our proposed quantized LIC is able to outperform BPG in terms of MS-SSIM.

In this paper, we have constructed actively perfusable multiple hepatic lobule-like vascular networks in a 3D cellular structure by using magnetic tweezers. Without well-organized channel networks, cells in a large 3D tissue cannot receive nutrients and oxygen from the channel, and therefore, the cells will be dead after few days. To construct well-organized channel networks, we fabricated a hepatic lobule like vascular networks by using magnetic fields in our previous works. However, the size of the hepatic lobule like vascular network was more than five times larger than real hepatic tissue. To improve the previous research, we have proposed several things. First, we have constructed the vascular network having similar size of the real thing in this step. Second, we have cultured the constructed structure for a long-time (more than two weeks) to verify the biocompatible condition. Third, we assemble the constructed hepatic tissues to make a large size of organ, liver. Finally, an actively perfusable system have been adopted to implement a bioreactor system by adding micro pump.

Image compression has been an important research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and its use in image compression has gradually been increasing. In this paper, we present an energy compaction-based image compression architecture using a convolutional autoencoder (CAE) to achieve high coding efficiency. Our main contributions include three aspects: 1) we propose a CAE architecture for image compression by decomposing it into several down(up)sampling operations; 2) for our CAE architecture, we offer a mathematical analysis on the energy compaction property and we are the first work to propose a normalized coding gain metric in neural networks, which can act as a measurement of compression capability; 3) based on the coding gain metric, we propose an energy compaction-based bit allocation method, which adds a regularizer to the loss function during the training stage to help the CAE maximize the coding gain and achieve high compression efficiency. The experimental results demonstrate our proposed method outperforms BPG (HEVC-intra), in terms of the MS-SSIM quality metric. Additionally, we achieve better performance in comparison with existing bit allocation methods, and provide higher coding efficiency compared with state-of-the-art learning compression methods at high bit rates.

This paper enhances the intra prediction by using multiple neural network modes (NM). Each NM serves as an end-to-end mapping from the neighboring reference blocks to the current coding block. For the provided NMs, we present two schemes (appending and substitution) to integrate the NMs with the traditional modes (TM) defined in high efficiency video coding (HEVC). For the appending scheme, each NM is corresponding to a certain range of TMs. The categorization of TMs is based on the expected prediction errors. After determining the relevant TMs for each NM, we present a probability-aware mode signaling scheme. The NMs with higher probabilities to be the best mode are signaled with fewer bits. For the substitution scheme, we propose to replace the highest and lowest probable TMs. New most probable mode (MPM) generation method is also employed when substituting the lowest probable TMs. Experimental results demonstrate that using multiple NMs will improve the coding efficiency apparently compared with the single NM. Specifically, proposed appending scheme with seven NMs can save 2.6%, 3.8%, and 3.1% BD-rate for Y, U, and V components compared with using single NM in the state-of-the-art works.

Compression has been an important research topic for many decades, to produce a significant impact on data transmission and storage. Recent advances have shown a great potential of learning based image and video compression. Inspired from related works, in this paper, we present an image compression architecture using a convolutional autoencoder, and then generalize image compression to video compression, by adding an interpolation loop into both encoder and decoder sides. Our basic idea is to realize spatial-temporal energy compaction in learning image and video compression. Thereby, we propose to add a spatial energy compaction-based penalty into loss function, to achieve higher image compression performance. Furthermore, based on temporal energy distribution, we propose to select the number of frames in one interpolation loop, adapting to the motion characteristics of video contents. Experimental results demonstrate that our proposed image compression outperforms the latest image compression standard with MS-SSIM quality metric, and provides higher performance compared with state-of-the-art learning compression methods at high bit rates, which benefits from our spatial energy compaction approach. Meanwhile, our proposed video compression approach with temporal energy compaction can significantly outperform MPEG-4, and is competitive with commonly used H.264. Both our image and video compression can produce more visually pleasant results than traditional standards.

Recently, Ultra HDTV (UHDTV) services become popular over satellite and on the internet. On the contrary, there are tremendously huge volume of High Definition Television (HDTV) and Standard Definition Television (SDTV) contents stored in broadcasting companies and storage devices. In this paper, we propose a color space conversion (also known as gamut mapping) method from BT. 709 (used for current HDTV broadcast) to BT. 2020 (used for UHDTV broadcast), which estimates and restores lost color information. It learns an end-to-end conversion method from BT. 709 image to BT. 2020 image with restoring lost color information using Convolutional Neural Network (CNN). By experiments, we confirm that our method can achieve 2.31dB gain against the conventional method on average.

In this paper, we have fabricated a multilayered hepatic lobule-like vascular network in a 3D tissue using a heptapole magnetic tweezer. The tissue consists of cell-laden hydrogels with 3D channel networks. To fabricate multilayered channel system, magnetic hydrogel fibers were manipulated by a magnetic tweezer. The hepatic lobule tissue shows a hexagonal structure with different sizes of veins. Six portal veins transfer the blood including nutrients and oxygen to a central vein by sinusoids. The portal and central veins are made by steel rods, whereas the magnetic hydrogel fibers has a role of sinusoids. An important point of this research is to connect two veins - portal and central vein - by magnetic fibers. For this, we used magnetic tweezer with seven poles to magnetize the steel rods. In order to generate high magnetic fields, we design magnetic tweezer with a flat tip and additional lower tweezer based on simulation data. The manipulation was performed in fibrin gel inside rat liver cells. By applying high magnetic fields, we attracted magnetic fibers to the steel rods and constructed 3D channel network in cellular structure. To verify the efficiency of the channel, we supply culture medium to the channel and then analyze the cell viability according to the distance from the channel. As a result, the cells located at close to the channel show higher cell viability than others.

In this paper, a dual learning-based method in intra coding is introduced for PCS Grand Challenge. This method is mainly composed of two parts: intra prediction and reconstruction filtering. They use different network structures, the neural network-based intra prediction uses the full-connected network to predict the block while the neural network-based reconstruction filtering utilizes the convolutional networks. Different with the previous filtering works, we use a network with more powerful feature extraction capabilities in our reconstruction filtering network. And the filtering unit is the block-level so as to achieve a more accurate filtering compensation. To our best knowledge, among all the learning-based methods, this is the first attempt to combine two different networks in one application, and we achieve the state-of-the-art performance for AI configuration on the HEVC Test sequences. The experimental result shows that our method leads to significant BD-rate saving for provided 8 sequences compared to HM-16.20 baseline (average 10.24% and 3.57% bitrate reductions for all-intra and random-access coding, respectively). For HEVC test sequences, our model also achieved a 9.70% BD-rate saving compared to HM-16.20 baseline for all-intra configuration.

To provide energy-efficient (i.e., longer lifetime of sensors) and network-friendly (i.e., reducing network traffic) field sensing, we propose an edge-centric field monitoring system which applies efficient sensors and camera control. The proposed system detects conditions in a monitoring area and controls sensing frequency (sampling rate) of sensors, and capture rate and encoding rate of surveillance cameras, according to the detected conditions. In addition, the system applies a Multi-access Edge Computing (MEC) platform to provide fast feedback control to the sensors and cameras. In performance evaluations, we assume that the monitoring target is landslide detection and create a miniature “artificial landslide generation” environment in our laboratory. By using the environment, we evaluate the system performance, and evaluation results indicate that the proposed system can reduce network traffic and save energy consumption efficiently.

Fabrication of vascular network is an important research for transporting required nutrients and oxygen to the artificial tissues. In this paper, we propose a novel method to construct a hepatic lobule-like vascular network in a 3D cellular structure. The network is simply constructed by three types of veins, central vein, portal vein, and sinusoids. To realize these kinds veins, we utilize two different sizes of steel rods and magnetic fibers for delivering nutrients in 3D cellular structure. Alginate gel fibers embedding ferrite particles are prepared as the same length and are magnetized by magnetizer at 3T. A magnetic tweezer with seven poles is proposed to generate sufficient forces that can manipulate magnetized fibers. Here, two types of rods are magnetized to different magnetic poles in order to attract opposite the end of fibers. This manipulation process is performed in fibrinogen and thrombin solution with liver cells (RLC-18). After solidification of the solution, we deposit solutions with cells and fibers repeatedly, and therefore, a multi-layered structure can be constructed. In addition, we investigate cell a viability in fibrin gel according to the depth of the gel. The result is that the deeper the depth of the gel is, the lower the cell viability is. The cell viability is conducted in several condition. As a result, at the low temperature (here at 22 °C), the viability of cell is increased.

In video streaming on the internet, effective encoding recipes (i.e. bitrate-resolution pairs) are a main obstacle to deliver high-quality video streams. We developed a method to generate an encoding recipe that considers subjective visual quality with one just-noticeable difference (JND) distance. However, this method requires excessive computation time, which is not directly applicable for live streaming. In this paper, in order to provide a light-weight method for live streaming, we developed three acceleration techniques: resolution extrapolation, VMAF skipping and sampled objective measure calculation. These techniques are heuristic, but greatly contribute to reducing computational cost. Experimental results demonstrate that the proposed method achieves a significant reduction in computation time without significant effects on rate-JND characteristics.

Image compression has been investigated as a fundamental research topic for many decades. Recently, deep learning has achieved great success in many computer vision tasks, and is gradually being used in image compression. In this paper, we present a lossy image compression architecture, which utilizes the advantages of convolutional autoencoder (CAE) to achieve a high coding efficiency. First, we design a novel CAE architecture to replace the conventional transforms and train this CAE using a rate-distortion loss function. Second, to generate a more energy-compact representation, we utilize the principal components analysis (PCA) to rotate the feature maps produced by the CAE, and then apply the quantization and entropy coder to generate the codes. Experimental results demonstrate that our method outperforms traditional image coding algorithms, by achieving a 13.7% BD-rate decrement on the Kodak database images compared to JPEG2000. Besides, our method maintains a moderate complexity similar to JPEG2000.

We introduce a perceptual video quality driven video encoding solution for optimized adaptive streaming. By using multiple bitrate/resolution encoding like MPEG-DASH, video streaming services can deliver the best video stream to a client, under the conditions of the client's available bandwidth and viewing device capability. However, conventional fixed encoding recipes (i.e., resolution-bitrate pairs) suffer from many problems, such as improper resolution selection and stream redundancy. To avoid these problems, we propose a novel video coding method, which generates multiple representations with constant Just-Noticeable Difference (JND) interval. For this purpose, we developed a JND scale estimator using Support Vector Regression (SVR), and designed a pre-encoder which outputs an encoding recipe with constant JND interval in an adaptive manner to input video.

This study presents the high-precision microinjection of fluorescent micro-gel beads into Caenorhabditis elegans trapped in a suction microchannel. In our previous works, we demonstrated survival microinjection by a conventional micromanipulation technique. However, the focal planes differed between the tip of the microinjection tool and the target axon inside the C. elegans body. To resolve this problem, we here propose a suction microchannel that traps C. elegans during the microinjection operation. The focal plane of the target nerve axon matches that of the fluorescent microbead in the microinjection tool, enabling high-precision microinjection into the interior of the C. elegans body under a microscopic view. In an experimental evaluation, the positioning accuracy of the injection into C. elegans was within the target accuracy (15 μm). The head-flrst navigation alignment of C. elegans along the microchannel was controlled by electrotaxis. Injection of the fluorescent micro-gel bead into the C. elegans body was quantitatively confirmed by confocal microscopy.

In this research, we fabricated movable toroidal cell structures inside a microfluidic device for tissue engineering applications. A photo-crosslinkable biodegradable hydrogel gelatin methacrylate (GelMA) was employed to encapsulate biological cells for assembling cell structures. The UV light power and the concentration GelMA hydrogel were optimized to achieve both fabrication of microstructures and live condition of cells. The two-layered toroidal cell structures were fabricated which can mimic the multi-layered structure of blood vessels. The movable microstructures were achieved by the water repellent coating on the substrate surface. Finally, on-chip fabrication of GelMA microstructures and peeling off of the fabricated microstructures were achieved using a microfluidic chip. The results indicate that the fabricated movable GelMA microstructures can be used for further three dimensional assembly to achieve vascular-like tube structures.

In recent years, various approaches have been investigated towards blind image quality assessment (IQA) with high accuracy and low complexity. In this paper we develop a pre-saliency map based blind IQA method, which takes advantage of saliency information in prior of quality prediction for performance enhancement by two steps. 1) We split the image into patches and design a convolution neural network (CNN) to predict the patch-wise quality score. Then we explore the relation between image saliency information and CNN prediction error to present a statistical analysis. 2) Based on the analysis, we propose a patch quality aggregation algorithm by removing non-salient patches which are likely to bring large prediction error and assigning large weights for salient patches. Experimental results validate that our method can achieve high accuracy (0.978) with subjective quality scores, which outperforms existing IQA methods. Meanwhile, the proposed method can reduce 52.7% computational time than the IQA without pre-saliency map.

Disaster detection is an important research topic and draws great attentions from both industry and academia. In this paper, we study the hybrid landslide detection system, which utilizes the video surveillance camera and multiple kinds of sensors and can detect the landslide automatically. Edge processing is adopted in this hybrid system to fuse the sensor data based on the Dempster-Shafer (D-S) theory, i.e. utilizing multiple sensors' information to calculate the possibility of the landslide. Edge processing can make faster decisions than the control center, the results of the edge processing are then used to schedule the sensor's transmission frequency and video transmission under the network constraints in this system. The simulation results show that the proposed scheme outperforms the competing schemes in typical network scenarios.

This paper presents a novel In situ nanomanipulation integrated with scanning electron microscope- computed tomography (SEM-CT) imaging system for 3D nanomanipulation. In our previous works, a nanorobotic manipulation system was established inside an environmental-SEM (E-SEM) for water-contained samples, including biological organism, based on a real-time high resolution SEM observation. However, the SEM image is limited in two dimensional (2D) and surficial information from the signals of secondly electrons. For nanosurgery applications, such as nanoinjection, it is needed to evaluate the sample in 3D space with its internal information after manipulation. The SEM-CT imaging system is developed for In situ nanomanipulation based on SEM observation. The CT is an effective method to obtain the internal 3D information as a non-destructive manner. The imaging resolution of our SEM-CT system is in less than 400 nm. A Caenorhabditis elegans (C. elegans) was used as a target of biological sample. To improve the contrast of SEM-CT imaging of C. elegans, the X-ray was tested by generating using brass and copper materials. Finally, the nanoinjection was demonstrated with SEM-CT imaging system to C. elegans using the nanoinjector which was fabricated by focused ion beam (FIB) process.

In this paper, we propose a new method to assemble microstructures with biological cells towards in vitro 3D cellular structures. The proposed assembly method uses self-assembly process of magnetized toroidal microstructures. Biocompatible toroidal hydrogel microstructures are prepared by electrodeposition method, and ferrite particles are put on the fabricated structures using poly-L-lysine (PLL). The microstructures are magnetized by the magnetic field at 3 T, and assembled by the magnetic self-assembly process. Biological cells were encapsulated in the microstructures and cultured to achieve high density of cells inside structures. The magnetized microstructures were assembled automatically. The magnetic force generated from the ferrite embedded microstructures was estimated and compared to the fluid resistance applied to the microstructures. The proposed method can be applied to achieve 3D in vitro cell structures with vascular networks for tissue engineering applications.

In this study, we propose a broadcasting and communication integrated platform that can retransmit 8K broadcasting contents from a TV receiving set to mobile devices. We then evaluate the QoS characteristics and the qualities of 8K contents in this platform. Evaluation results conclude that 8K broadcasting contents can be retransmitted to mobile devices smoothly and their qualities are nearly as high as those of assumed 8K broadcasting service in Japan.

In this paper, we conducted a new method to assemble microstructures inside a droplet named “Lab in a Droplet (LiD)”. The method can realize the assembly of micro-nano structures inside a droplet size. The surface tension is used to assemble microstructures automatically. Micro-scale or nano-scale objects are ejected from an inkjet nozzle. They are gathered at the center of the droplet and assembled by the surface tension. We conducted preliminary experiments to check whether the self-assembly of microstructures can be achieved by LiD. The experimental results show that the self-assembly of microbeads is conducted inside a droplet. The nano-scale objects can be patterned on a substrate depend on their sizes. A microstructure made of photo-crosslinkable resin was prepared, and microbeads were assembled with the microstructure inside a droplet. The method can be used for autonomous assembly of micro-nano structures in high precision.

In this study, we describe a novel method of fabricating shape-controlled calcium alginate gel microcapsules. Alginate-poly-L-lysine (PLL) hydrogel microcapsules with predefined shapes were constructed based on electrodeposition method. Firstly, electrolysis of water in alginate solutions with calcium carbonate particles induced alginate gelation on micro-patterned electrode to form 2D gel structures. Then, these structures will be detached from the electrode surface and treated with the alginate-PLL microcapsules system. By passive control based on the micro-pattern geometric confinement and electrodeposition parameters, we succeeded in producing calcium alginate-PLL microcapsules with diverse shapes (such as sphere rod and cubic). The shape and size of the calcium alginate microcapsules could be tuned by adjusting the geometric design of micro-pattern on electrode and the apply voltage of electrodepostion. The preparation conditions of size- and shape-controlled calcium alginate-PLL microcapsules and influence factors were studied. This proposed method can lead to more accurate and creative studies of fabricating biocompatibility scaffold for tissue engineering.

In this study, cell-laden alginate-poly-L-lysine (PLL) hydrogel structures with arbitrary shapes were constructed based on electrodeposition method. Electrolysis of water in alginate solutions with calcium carbonate particles induced alginate gelation on micro-patterned anode electrode, and cell-laden alginate gel structures were formed. The several different shapes of gel were generated at one time by predefined micro-patterns. The micro-patterned electrode was fabricated by coating photoresist on Fluorine-doped tin oxide (FTO) glass slide. Alginate-PLL encapsulation technique was introduced to this platform forming alginate-PLL hydrogel structures. This proposed method can lead to more accurate and creative studies of fabricating cell-laden scaffold for tissue engineering.

This study presents the survival microinjection into Caenorhabditis elegans (C. elegans) with in vivo observation based on micromanipulation. The microinjections were achieved with micro-gel beads which are enable to encapsulate chemicals for injection. In this study, a fluorescent material was used to evaluate the injection positional precision inside the C. elegans. The fluorescent microbead was picked up at the tip of a micropipette injection tool and injected by a piezo actuated microinjector. The distance between the injected micro-gel bead and closest nerve axon was measured as 20.3 μm and 16.5 μm by in vivo observation of a confocal microscopy. The two types of pipette tools were used to evaluate the success and survival rates of microinjection, and the smaller pipette (pipette A, 0.8 μm in diameter) showed higher rates as 50 % and 67 % respectively.

3D assembly of cellular structures is important for the fabrication of biological substitute in tissue engineering. In this paper, a novel rail-guided multi-microrobotic system was proposed for the assembly of cellular structure. The cellular 2-dimensional (2D) module was fabricated by UV illumination of the crosslinkable hydrogel. The coordinated manipulation among the micromanipulators was performed with newly designed concentric movement along the rail, which realized the arbitrary change of micromanipulator posture. Through the rotation of the end-effectors around the specimen without swapping out the visual field, the manipulation flexibility was improved. The distance information between the micromanipulator and the module was acquired from vision feedback system and utilized for the automatic pick-up of the microstructure. Through the cooperation among multi-manipulators with hybrid motors, the micromanipulation to assemble the 3D structure with 30 nm operation resolution was achieved. Finally, the rail-guided DeSCom system realized the bottom-up fabrication of cellular vascular-like microtube with vision feedback.

Microfluidic devices provide efficient approaches for building cellular tubular structures for in vitro tissue models in tissue engineering. In this paper, we report a novel method of constructing three-dimensional (3D) multilayered tubular structures based on axis translation of two-dimensionally (2D) microstructures inside microfluidic devices. The on-chip fabrication of movable 2D microstructures embedding fibroblasts (NIH/3T3) based on Poly (ethylene glycol) Diacrylate (PEGDA) was reported. Novel two-layered microfluidic devices were fabricated by Polydimethylsiloxane (PDMS), for conducting the fluidic self-assembly of the 2D microstructures. The self-assembly process was experimentally demonstrated. For improving the assembly results, a funneled structure and 3 micro grooves were added inside the microfluidic channel. Improved self-assembly result of constructing a multilayered tubular microstructure with higher efficiency was demonstrated.

In this paper, we achieved three dimensional (3D) assemblies of multi-cell spheroids towards 3D in vitro cell structures. The 3D assembly of cells in vitro is one of the important techniques to reveal cell growth, differentiation and hystogenesis. The techniques can be used for applications in tissue engineering, drug screening, and cell characterization. The multi-cell spheroids were used as building blocks for 3D structure, and thermoresponsive gel was used for spheroids assembly. The mouse myoblast cells C2C12 and mouse fibroblast cells NIH 3T3 were cultured in an ultra-low atachment surface flask. The cells were naturally aggregated in the flask and cell spheroids were prepared in self-assembly process. The rat liver cell RLC-18 was also cultured to prepare spheroids. The thermoresponsive Gel probe (GeT probe) we proposed was employed for assembly of the prepared spheroids in 3D. The handled spheroids by GeT probe were cultured inside the thermoresponsive gel and the cell viability was confirmed. The cultured structure was handled by the GeT probe for further assembly. The results validate that the GeT probe can manipulate spheroids with keeping live condition of cells and achieve 3D cell assembly inside the thermoresponsive gel with arbitrary and precise patterning.

Cellular vascular-like microtubes occupy an important position in tissue engineering for building in vitro tissue models. In this paper, we report a method of constructing three-dimensional (3D) multilayered vascular-like microtubes based on fluidic axis-translation self-assembly of two-dimensional (2D) microstructures inside microfluidic devices. The on-chip fabrication of cell (fibroblasts NIH/3T3) embedded 2D microstructures based on Poly (ethylene glycol) Diacrylate (PEGDA) and biodegradable material Gelatin Methacrylate (GelMa) were reported. A multilayered Polydimethylsiloxane (PDMS) microfluidic device was fabricated for conducting the fluidic self-assembly of 2D microstructures. The fluidic axis-translation self-assembly process was experimentally demonstrated. Multiple hydrogels embedded microtube was constructed. The fabrication of GelMa microstructures was demonstrated. The degradability of cell embedded GelMa microstructures was evaluated by long-term observation, and it shows the great potential of GelMa to be used for constructing cellular vascular-like microtubes.

This paper introduces a novel catalytic mobile micro/nanorobot made only of platinum that realizes nanometer locomotion in hydrogen peroxide solution. The innovative mechanism and principle of the nanorobot are presented. A simple and effective fabrication process by focused ion beam and a stable manipulation method of the nanorobot are demonstrated. The nanorobot can steer or navigate by its finely designed geometry, and keep a stable rotational motion rather than arbitrary and uncertain movement. This paper evaluates the influence of some critical factors on the movement of the nanorobot, such as the concentration and temperature of the hydrogen peroxide solution, and the geometry of a nanorobot. The control of the nanorobot's movement can be realized based on the result of this evaluation. Compared with previous studies, this catalytic platinum nanorobot realizes bidirectional rather than unidirectional movement. The Langevin equation is used to describe the dynamic model of the platinum nanorobot.

In this paper, we propose a new method to assemble microstructures made of a thermoresponsive gel using hysteresis character of the thermoresponsive polymer solution. This method can be used for 3 dimensional cell assembly by embedding cells in the thermoresponsive gel structures. Gel blocks can be maintained the gel condition by the hysteresis character and the microstructures can be formed by assembling the gel blocks. The temperature distribution around a microheater was analyzed to generate the thermoresponsive gel and avoid thermal damages to cells. The generation of thermoresponsive gel was conducted using the microheater which was embedded in a probe tip and manipulated by a micromanipulator. The fabrication of a gel block was achieved using the hysteresis character and the fabricated gel block was picked and placed by the probe. Cells were embedded in the gel by controlling the position of microheater to avoid the influence of thermal convection flow. The positioning of the gel blocks can be precisely controlled by the micromanipulator. The results indicate the method we propose has a great possibility to achieve 3D cell assembly without large stress to cells during the assembly and cell culture.

In this paper, we conducted an assembly of microstructures made of a thermoresponsive gel using hysteresis character of the thermoresponsive polymer solution. This method can be used for 3 dimensional cell assembly by embedding cells in the thermoresponsive gel structures. Gel sheets can be fabricated by micrheaters on a substrate and maintained in the gel condition by the hysteresis character. The microstructures can be formed by assembling the gel sheets using a probe device. The generation of a thermoresponsive gel was conducted using the microheaters made of ITO on a substrate and the generated gel sheets were manipulated by a micromanipulator. The fabrication of a gel sheets was achieved using the hysteresis character and the fabricated gel sheets were picked and placed by the probe. The positioning of the gel blocks can be precisely controlled by the micromanipulator. The results indicate the method we propose has a great possibility to achieve 3D cell assembly without large stress to cells during the assembly and cell culture.

Recently the research about constructing 3 dimensional cell structures is very important for its great potential applications in tissue engineering. In this paper, we report a novel method of constructing multi-layered microstructures embedding cells via microfluidic devices. The on-chip fabrication of movable microstructures embedding fibroblasts (NIH/3T3) based on Poly (ethylene glycol) Diacrylate (PEGDA) was reported. Two approaches for assembling these movable microstructures were presented. One was a manual assembly method based on micromanipulation system and the other one was a self-assembly method based on microfluidic channel. Several manual assembly ways were demonstrated and a tube-shaped microstructure with 17 layers was assembled by an efficient assembly method. A novel microfluidic channel was presented for conducting self-assembly method and a 2-layered experimental microfluidic device was fabricated by Polydimethylsiloxane (PDMS). The self-assembly process of fabricated microstructures via this device was preliminarily demonstrated.

Bit-depth scalable coding method is an approach that generates multiplexed bit-streams that can be decoded to two video sequences, for Standard Dynamic Range (SDR) environment and for High Dynamic Range (HDR) environment. This paper presents a bit-depth scalable coding method that is considered as gradation restoration in interlayer prediction process. Our proposed inter-layer prediction uses a histogram interpolation method that enables to generate more mid-gray brightness levels than traditional inverse tone mapping methods with one-to-one correspondence.

In this paper, we propose new probe device which can realize soft handling of single cells using thermal gel. In general, probe devices are one of the important devices for single cell manipulation. However, conventional probe devices have some problems: damage to the manipulated cells and difficulty to release the manipulated objects by the effect of surface force. As one solution of these problems, we propose "soft handling probe" in this paper. The probe has an electrode as a microheater. The thermoresponsive polymer can be gelled when a current is applied to the electrode in the thermoresponsive polymer solution. The electrode was fabricated by cupper wire and sputtering of gold on the probe. Thermal gelation of the thermoresponsive polymer was realized at the fabricated probe tip. The manipulation of micro objects and assembly of three dimensional structures were demonstrated by the probe. The handling of yeast cell and liposome were also realized by the probe. It is expected that the probe can realize the soft handling by the generated thermal gel and realize the precise positioning by reducing the effect of surface force.

This paper presents a novel homogeneous power management system for heterogeneous self-reconfigurable multi-module systems consisting of active power suppliers, diverse power consumers and hybrids. To optimize functionality of each module, a concept separating functionality from power supply is proposed and applied in immobile payload items which can be combined with mobile modules in varying configurations autonomously. By allowing consumers and power sources to connect to common power bus, the concept enhances flexibility, reusability and performance of modular robotic systems. The power management system based on the concept is implemented and evaluated. The experimental results show that power is efficiently transferred in power bus among individual modules, consumer modules without battery packs can be kept alive during switching, and the power management system can protect individual modules from hardware faults reliably.

In this paper, we propose a semi-closed microchip which can realize the probe type manipulation in the microchip with the internal environment sealed off from the external environment. Long-term experiments can be conducted for in-situ culture and analysis of cells by the semi-closed microchip. The semi-closed microchip has a bath in the middle of microchannel to allow the insertion of micropipettes into the microchip. The bath is sealed off by thin oil film to prevent the evaporation of the solution in the bath. The seal by the oil film can maintain even if micropipettes are inserted into the bath and pulled out from the bath because the oil film can fill the hole by its surface tension. The seal by the oil film was evaluated by measuring the weight of the bath after 12/24 hours. To use the semi-closed microchip for cell culture and cell analysis, the exchange of the solution in the bath was demonstrated. It was also demonstrated that the cell fixation with the thermo sensitive gel in the proposed semi-closed microchip by fabricating the ITO electrodes as heaters in the bath. Cell culture and the target cell harvesting were conducted by the semi-closed microchip. The proposed semi-closed microchip can be used for cell culture, cell analysis and cell harvesting under a microscope as the biological applications for single cell analysis.

In recent years, the number of sign language learners is increasing in Japan. And there are many teaching materials of sign language such as textbooks, videotapes and software for PCs. However, these teaching materials have several problems that learners cannot study sign language sufficiently because the learners can mainly study manual gestures, cannot change the direction to see signed gestures, and cannot check their signed gestures by themselves. We developed a sign language teaching system applying sign language recognition and sign language generation technologies to solve these problems and realize effective sign language study. Further, we carried out an evaluation experiment by several users and confirmed that the developed system was effective.

To automatically interpret Japanese sign language (JSL), the recognition of signed words must be more accurate and the effects of extraneous gestures removed. We describe the parameters and the algorithms used to accomplish this. We experimented with 200 JSL sentences and demonstrated that recognition performance could be considerably improved.

Sign language gestures are inflected in accordance with the context. To recognize such sign language properly, the structure of sign kmgoage must be made clear. It is well known that the structure of sign Iangoage is represented as a combination of basic components of gestures. Sign language can be recognized by using such components. In this paper, a format to describe sign language gestures and a method to recognize the meaning of the gesture based on the components of gestures are discussed.