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Inspired by the advantages from natural birds’ shape-morphing adapted flight performance, this work investigates how the bioinspired adaptative morph induced inertia variation affects the flight performance of a morphing quad-rotor. Extensive numerical and experimental evaluations on a custom-built morphing quad-rotor that is steered along a predefined path and hovers in the presence of constant wind disturbance were performed and analyzed. Simulation results demonstrate that the smaller quad-rotor exhibits better agility performance due to the compact volume/size and lower weight, while the bigger one appears more flight robustness in a disturbed environment such as windy testing area. Experimental studies further prove that by adaptatively altering its volume/size, our quad-rotor with the bioinspired in-flight morphing behavior can well execute path following tasks in a constrained space without deviating the trajectory when encountering obstacles, as well it can withstand external wind torque through morphing up its volume to therewith increase relevant moment-of-inertia for improving the flight robustness. Compared to the regular scaled aerial vehicles whose volumes’ change follows a weight change proportionally, our results also indicate that our in-flight morphing aerial vehicle mechanism (i.e. the quad-rotor morphs the volume but without changing weight/mass) shows more advantages in power efficiency and morphologically adaptative flight capability.

Person re-identification (re-ID) aims to robustly measure visual affinities between person images. It has wide applications in intelligent surveillance by associating same persons’ images across multiple cameras. It is generally treated as an image retrieval problem: given a probe person image, the affinities between the probe image and gallery images (P2G affinities) are used to rank the retrieved gallery images. There exist two main challenges for effectively solving this problem. 1) Person images usually show significant variations because of different person poses and viewing angles. The spatial layouts and correspondences between person images are therefore vital information for tackling this problem. State-of-the-art methods either ignore such spatial variation or utilize extra pose information for handling the challenge. 2) Most existing person re-ID methods rank gallery images considering only P2G affinities but ignore the affinities between the gallery images (G2G affinity). Such affinities could provide important clues for accurate gallery image ranking but were only utilized in post-processing stages by current methods. In this article, we propose a unified end-to-end deep learning framework to tackle the two challenges. For handling viewpoint and pose variations between compared person images, we propose a novel Kronecker Product Matching operation to match and warp feature maps of different persons. Comparing warped feature maps results in more accurate P2G affinities. To fully utilize all available P2G and G2G affinities for accurately ranking gallery person images, a novel group-shuffling random walk operation is proposed. Both Kronecker Product Matching and Group-shuffling Random Walk operations are end-to-end trainable and are shown to improve the learned visual features if integrated in the deep learning framework. The proposed approach outperforms state-of-the-art methods on Market-1501, CUHK03 and DukeMTMC datasets, which demonstrates the effectiveness and generalization ability of our proposed approach. Code is available at https://github.com/YantaoShen/kpm_rw_person_reid .

This paper presents our recent development on a portable and refreshable text reading and sensory substitution system for the blind or visually impaired (BVI), called Finger-eye. The system mainly consists of an opto-text processing unit and a compact electro-tactile based display that can deliver text-related electrical signals to the fingertip skin through a wearable and Braille-dot patterned electrode array and thus delivers the electro-stimulation based Braille touch sensations to the fingertip. To achieve the goal of aiding BVI to read any text not written in Braille through this portable system, in this work, a Rapid Optical Character Recognition (R-OCR) method is firstly developed for real-time processing text information based on a Fisheye imaging device mounted at the finger-wearable electrotactile display. This allows real-time translation of printed text to electro-Braille along with natural movement of user’s fingertip as if reading any Braille display or book. More importantly, an electro-tactile neuro-stimulation feedback mechanism is proposed and incorporated with the R-OCR method, which facilitates a new opto-electrotactile feedback based text line tracking control approach that enables text line following by user fingertip during reading. Multiple experiments were designed and conducted to test the ability of blindfolded participants to read through and follow the text line based on the opto-electrotactile-feedback method. The experiments show that as the result of the opto-electrotactile-feedback, the users were able to maintain their fingertip within a 2mmdistance of the text while scanning a text line. This research is a significant step to aid the BVI users with a portable means to translate and follow to read any printed text to Braille, whether in the digital realm or physically, on any surface.

In this paper, we investigate the perception-aware path finding, planning and following for a class of snake robots autonomously serpentining in an unmodeled and unknown environment. In the work, the onboard LiDAR sensor mounted on the head of the snake robot is utilized to reconstruct the local environment, by which and the modified rapidly-exploring random tree method, a feasible path from the current position of the robot to a local selected target position can be obtained. Next, the parametric cubic spline interpolation path-planning method and potential functions are applied to make the path more smooth so as to prevent the multi-link and elongated robot body from hitting obstacles. To steer, a time-varying line-of-sight control law is designed to ensure that the robot moves to the local target position along the generated path by the perception-aware method. The robot will repeatedly perform the above search-find-move strategy until it reaches the final predefined target point. Simulation and experimental results demonstrate a good performance of the proposed perception-aware approach, that is, the elongated and underactuated snake robot is capable of autonomously navigating in an unknown environment.

This paper focuses on designing, modeling and validating a novel scalable multicopter whose deformation mechanism, called SNIAE-SSE, relies on a combination of simple non-intersecting angulated elements (SNIAEs) and straight scissor-like elements (SSEs). The proposed SNIAE-SSE mechanism has the advantages of single degree-of-freedom, fast actuation capability and large deformation ratio. In this work, enabled by the SNIAE-SSE mechanism, a quadcopter prototype with symmetrical and synchronous deformation is firstly developed, which facilitates a novel and controllably scalable multicopter system for us to analyze its modeling, as well as to validate its flight performance and dynamics during the deformation in several flight missions including hover, throwing, and morphing flying through a narrow window. Experimental results demonstrate that the developed scalable multicopter can maintain its stable flight behavior even both the folding and unfolding body deformations are fast performed, which indicates an excellent capability of the scalable multicopter to rapidly adapt to complex and dynamically changed environments.

In this paper, we investigate the consensus problem of multiple unmanned aerial vehicles (UAVs) in the presence of environmental constraints under a general communication topology containing a directed spanning tree. First, based on a position transformation function, we propose a novel dynamic reference position and yaw angle for each UAV to cope with both the asymmetric topology and the constraints. Then, the backstepping-like design methodology is presented to derive a local tracking controller for each UAV such that its position and yaw angle can converge to the reference ones. The proposed protocol is distributed in the sense that, the input update of each UAV dynamically relies only on local state information from its neighborhood set and the constraints, and it does not require any additional centralized information. It is demonstrated that under the proposed protocol, all UAVs reach consensus without violation of the environmental constraints. Finally, simulation and experimental results are provided to demonstrate the performance of the protocol.

This letter investigates the straight path following problem for a class of underactuated bioinspired snake robots on ground with unknown and varied friction coefficients. Existing works usually design control input requiring the exact values of these friction coefficients, which however rely on the specific operating terrain and may not always be known a priori . By virtue of backstepping technique, we present a novel adaptive controller that can perform the path following tasks and meanwhile it can compensate for unknown and varied friction coefficients in real time. Moreover, it is proved via LaSalle–Yoshizawa theorem that the path following errors converge to zero asymptotically and all the parameter estimates are bounded. Simulations and experiments on an eight-link snake robot are carried out to demonstrate the effectiveness of the proposed control strategy.

We propose a way to learn visual features that are compatible with previously computed ones even when they have different dimensions and are learned via different neural network architectures and loss functions. Compatible means that, if such features are used to compare images, then "new" features can be compared directly to "old" features, so they can be used interchangeably. This enables visual search systems to bypass computing new features for all previously seen images when updating the embedding models, a process known as backfilling. Backward compatibility is critical to quickly deploy new embedding models that leverage ever-growing large-scale training datasets and improvements in deep learning architectures and training methods. We propose a framework to train embedding models, called backward-compatible training (BCT), as a first step towards backward compatible representation learning. In experiments on learning embeddings for face recognition, models trained with BCT successfully achieve backward compatibility without sacrificing accuracy, thus enabling backfill-free model updates of visual embeddings., Comment: Accepted to CVPR 2020 as oral, revised version

In this paper, a simple tracking control scheme is proposed for quadrotor systems with uncertain dynamics. It precludes the necessity for prohibitive analytic computation of the derivatives of the desired (virtual) attitude that is typically employed in controlling quadrotor systems. Moreover, this control scheme is approximation-free in the sense that it does not incorporate any adaptive laws, observers, or command filters to compensate for unknown parameters in the dynamics and the absence of the analytic differentiation, thus exhibiting remarkably low complexity levels and making its implementation straightforward. The thrust saturation is approached in the position control design which also enables the singularity in desired attitude extraction to be avoided entirely. It is demonstrated that based on the proposed scheme, the tracking errors can be made arbitrarily small by appropriately selecting design parameters. Extensive simulations and experiments are performed to verify the effectiveness of our scheme.

This study is designed to validate the feasibility of generating identifiable moving patterns using electro-tactile stimulation. An electro-tactile display is built using an array of 16 contacts to deliver the electrical signal to the fingertip skin. This signal can have varying voltages, frequencies or duty cycles to form the most comfortable sensation.Moving patterns can be generated by individually or collectively switching on or off the contacts on the display. This is done to stimulate a moving pattern. In this case, a moving pattern is comparable to a group of frame-by-frame pictures constructing a movie. Similarly, by toggling the contacts in a specific order, a moving pattern can be achieved.A program on a single-board computer (Raspberry Pi) was used to control and generate 6 different patterns. These patterns are delivered to the display and consequently to the fingertip skin of the participants. A total of 8 subjects participated in this study. They filled a questionnaire to indicate the corresponding movement.The results of these experiments were analyzed and a conclusion regarding the direction of the movement was drawn. It became clear that the direction of the movement had a significant impact on the recognition of the patterns.

An electro-tactile display was built in our previous research to deliver the electrical signal to the fingertip skin. It is known that the structure and characteristic of the skin is different for each person and therefore the voltage and current has to be adjusted per each user. This was previously controlled manually. In this work, an adaptive system is discussed and implemented to automatically adjust these parameters for each individual. This is done in two stages. In the first stage, an impedance map is generated by using a constant-voltage approach. This is achieved by monitoring the current needed for each electrode and constructing the map accordingly. In the second stage, an algorithm uses this map as the basis for control feedback to find the appropriate voltage to keep the current at a constant level. The result of these two stages interacting with each other is an adaptive map of the required voltage threshold for each electrode to stimulate that part of the skin. The simulation and the actual result of the implementation support the proposed method.

This paper investigates the curve path following problem for a class of planar underactuated bio-inspired snake robots. The time-varying line-of-sight (LOS) guidance law and the cubic spline interpolation (CSI) path-planning method are employed. Existing studies focus on straight line path following which only gives a solution for snake robot motion control in relatively simple environments. Considering the snake robot’s many degrees of freedom and excellent mobility in terrains, we propose a more applicable solution of curve path following for snake robots on the ground. The improved LOS helps the snake robot to steer aggressively at a sharp turning point. Furthermore, to avoid the sideslip of the snake robot caused by the ground friction change, an integral controller is introduced in the design of the heading reference. Simulations and experiments on an 8-link custom-built snake robot are conducted and the results demonstrate and validate the effectiveness of the proposed curve path following algorithm.

Research in rehabilitation engineering has shown that electrodes can produce tactile sensations with appropriate electrical signals to stimulate the multiple tactile receptors located under the fingertip skin. Numerous equivalent skin–electrode interfaces have been modeled to characterize the electrical properties of the skin; however, the values of these circuit models are continually changing, due both to the nonlinearity associated with human fingertip skin and to individual user differences. As a result, electrical stimulation that is suitable in terms of current or voltage level for tactile sensations cannot be guaranteed for every user. An identification method is then necessary for characterizing the parameters of the skin–electrode interface circuit model so as to improve rendering consistency and comfort for every user regardless of skin condition. In this paper, we introduce a custom-built electro-tactile display terminal, and then using this display terminal for data collection, we present an online identification scheme for determining the bio-impedance parameters of the well-known Cole–Cole circuit model for the skin–electrode interface. For this, a modified Kalman least squares iterative approach is used that relies on measuring only one-port square wave stimulation voltages. The repeatability and reliability of the identification scheme are tested by identifying the resistor–capacitor (RC) load bio-impedance networks of different users with both a dry and slightly damp index fingertip over multiple identification trials. Additionally, because of the inevitable variation in the parameters over multiple measurements, the repeatability of multiple calculated RC models (dry and wet) is further evaluated. The significance of our work is that it greatly improves the tactile rendering performance of electrical stimulation (electro-tactile) systems and will benefit the development of electro-tactile-based rehabilitative robotic devices and human–robot interfaces.

In this paper, a new simple tracking control scheme is presented for quadrotor systems with uncertain dynamics. It precludes the necessity for prohibitive analytic computation of the derivatives of the desired (virtual) attitude that is typically employed in controlling quadrotor systems. Moreover, this control scheme is approximation-free in the sense that it does not incorporate any adaptive laws, observers, or command filters to compensate for unknown parameters in the dynamics and the absence of the analytic differentiation, thus exhibiting remarkably low complexity levels and making its implementation straightforward. The thrust saturation is approached in the position control design, which also enables the singularity in desired attitude extraction to be avoided entirely. It is demonstrated that based on the proposed scheme, the tracking errors can be made arbitrarily small by appropriately selecting design parameters. Extensive simulations and experiments are performed to verify the effectiveness of our method.

The paper presents the development of a novel deformable quad-rotor enabled aerial gripper. The mechanism of our deformable quad-rotor is based on simultaneous expansion or contraction of the quad-rotor body, which is generated by controlling a rigid elements based morphing structure (REMS). Such deformation results in a highly deformable quad-rotor that can not only perform morphological adaptation in response to environmental changes and obstacles, but also improve the flight performance by contracting to facilitate the agility/maneuverability or by expanding to enhance the stability. Meanwhile, inspired by the wasp grasping behavior, such controllable expansion and contraction from the REMS ingeniously enable a new function of aerial gripper. In this paper, we start to detail the mechanism and design of the REMS based deformable quad-rotor, then present the quad-rotor deformation enabled aerial gripper design, its dynamics modeling, the grasping function and analysis. The simulation was conducted in order to graphically show the elicited aerodynamic flow situation during expansion or contraction of the quad-rotor with and without carrying payload. Experiments were further implemented to validate the grasping function of the gripper and the flight performance of the quad-rotor. Finally, two case studies on the new aerial gripper were performed. All results demonstrate the excellent performance of the deformable quad-rotor enabled aerial gripper, that is, it has the advantages of both flight maneuverability and grasping capability during performing tasks.

This paper investigates the straight path following problem for a class of underactuated bio-inspired snake robots on ground with unknown and varied friction coefficients. Existing works usually design control input requiring the exact values of these friction coefficients, which however rely on the specific operating terrain and may not always be known a priori. By virtue of backstepping technique, we present a novel adaptive controller that can compensate for unknown and varied friction coefficients in real-time. Moreover, it is proved via LaSalle-Yoshizawa theorem that the path following errors converge to zero asymptotically and all the parameter estimates are bounded. Simulations and experiments on an 8-link snake robot are carried out to illustrate the effectiveness of the proposed controller.

Person re-identification aims at finding a person of interest in an image gallery by comparing the probe image of this person with all the gallery images. It is generally treated as a retrieval problem, where the affinities between the probe image and gallery images (P2G affinities) are used to rank the retrieved gallery images. However, most existing methods only consider P2G affinities but ignore the affinities between all the gallery images (G2G affinity). Some frameworks incorporated G2G affinities into the testing process, which is not end-to-end trainable for deep neural networks. In this paper, we propose a novel group-shuffling random walk network for fully utilizing the affinity information between gallery images in both the training and testing processes. The proposed approach aims at end-to-end refining the P2G affinities based on G2G affinity information with a simple yet effective matrix operation, which can be integrated into deep neural networks. Feature grouping and group shuffle are also proposed to apply rich supervisions for learning better person features. The proposed approach outperforms state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets by large margins, which demonstrate the effectiveness of our approach., CVPR 2018 poster

This paper focuses on designing, kinematically and dynamically characterizing a novel deformable quad-rotor that is based on the scissor-like foldable mechanisms. Inspired by morphological adaptation of birds during flight, the quad-rotor allows that its volume can be varied to dynamically adapt complex environments and spaces. The advantages of such mechanism are twofold following the scenario, that is, the quad-rotor can stably fly with a big volume/size and can also switch to a smaller volume for a swift flight in response to the changes of the environments and spaces. It therefore is capable of efficiently avoiding obstacles, stably passing through narrow spaces, and resisting certain-extent wind effects. To generate the controllable deformation, the actuated angulated scissor elements in the structure play an important role. In this paper, the scissor element design, its actuation mechanism, and volume deformation of the new quad-rotor are presented in detail. Simulations and experiments are then conducted to validate the controlled deformation as well as to investigate the deformation elicited effects to the activated quad-rotor airframe and its aerodynamics. The results demonstrate the effectiveness of the proposed deformable quad-rotor, and prove that it enables excellent volume deformation performance, good flight adaptation, as well as minimal aerodynamics influences during deforming.

The person re-identification task requires to robustly estimate visual similarities between person images. However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs. As a result, the similarity estimation of some hard samples might not be accurate. In this paper, we propose a novel deep learning framework, named Similarity-Guided Graph Neural Network (SGGNN) to overcome such limitations. Given a probe image and several gallery images, SGGNN creates a graph to represent the pairwise relationships between probe-gallery pairs (nodes) and utilizes such relationships to update the probe-gallery relation features in an end-to-end manner. Accurate similarity estimation can be achieved by using such updated probe-gallery relation features for prediction. The input features for nodes on the graph are the relation features of different probe-gallery image pairs. The probe-gallery relation feature updating is then performed by the messages passing in SGGNN, which takes other nodes’ information into account for similarity estimation. Different from conventional GNN approaches, SGGNN learns the edge weights with rich labels of gallery instance pairs directly, which provides relation fusion more precise information. The effectiveness of our proposed method is validated on three public person re-identification datasets.

Person re-identification is an important task that requires learning discriminative visual features for distinguishing different person identities. Diverse auxiliary information has been utilized to improve the visual feature learning. In this paper, we propose to exploit natural language description as additional training supervisions for effective visual features. Compared with other auxiliary information, language can describe a specific person from more compact and semantic visual aspects, thus is complementary to the pixel-level image data. Our method not only learns better global visual feature with the supervision of the overall description but also enforces semantic consistencies between local visual and linguistic features, which is achieved by building global and local image-language associations. The global image-language association is established according to the identity labels, while the local association is based upon the implicit correspondences between image regions and noun phrases. Extensive experiments demonstrate the effectiveness of employing language as training supervisions with the two association schemes. Our method achieves state-of-the-art performance without utilizing any auxiliary information during testing and shows better performance than other joint embedding methods for the image-language association.

Person re-identification aims to robustly measure similarities between person images. The significant variation of person poses and viewing angles challenges for accurate person re-identification. The spatial layout and correspondences between query person images are vital information for tackling this problem but are ignored by most state-of-the-art methods. In this paper, we propose a novel Kronecker Product Matching module to match feature maps of different persons in an end-to-end trainable deep neural network. A novel feature soft warping scheme is designed for aligning the feature maps based on matching results, which is shown to be crucial for achieving superior accuracy. The multi-scale features based on hourglass-like networks and self-residual attention are also exploited to further boost the re-identification performance. The proposed approach outperforms state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets, which demonstrates the effectiveness and generalization ability of our proposed approach., Comment: CVPR 2018 poster

This paper develops and experimentally validates a 3D-printed snake robot prototype. Its structure is designed to allocate limited room for each functional module (including an external power module, battery power module, the wireless control and transmission module and some detective sensors), so as to ensure the snake robot works in different environments. In order to control and track the snake robot, a low-cost MEMS-IMU (micro-electro-mechanical systems inertial measurement unit)-based snake robot motion tracking system is developed. Three algorithms (low-pass filter, baseline calibration, and Kalman filter) are used to eliminate noise from IMU’s acceleration data, thus minimizing the noise influence to tracking accuracy. Through signal processing, the IMU acceleration data can be effectively used for motion tracking. The result from the video tracking software is employed as a reference for comparison, so as to evaluate the motion tracking algorithm efficiency. The comparison results demonstrate high efficiency of the proposed IMU-based motion tracking algorithm.

A position-sensitive detector/device (PSD) is a sensor that is capable of tracking the location of a laser beam on its surface. PSDs are used in many scientific instruments and technical applications including but not limited to atomic force microscopy, human eye movement monitoring, mirrors or machine tool alignment, vibration analysis, beam position control and so on. This work intends to propose a new application using the PSD. That is a new microscopy system called scanning PSD microscopy. The working mechanism is about putting an object on the surface of the PSD and fast scanning its area with a laser beam. To achieve a high degree of accuracy and precision, a reliable framework was designed using the PSD. In this work, we first tried to improve the PSD reading and its measurement performance. This was done by minimizing the effects of noise, distortion and other disturbing parameters. After achieving a high degree of confidence, the microscopy system can be implemented based on the improved PSD measurement performance. Later to improve the scanning efficiency, we developed an adaptive local scanning system to scan the whole area of the PSD in a short matter of time. It was validated that our comprehensive and adaptive local scanning method can shorten the scanning time in order of hundreds of times in comparison with the traditional raster scanning without losing any important information about the scanned 2D objects. Methods are also introduced to scan very complicated objects with bifurcations and crossings. By incorporating all these methods, the new microscopy system is capable of scanning very complicated objects in the matter of a few seconds with a resolution that is in order of a few micrometers.

Vehicle re-identification is an important problem and has many applications in video surveillance and intelligent transportation. It gains increasing attention because of the recent advances of person re-identification techniques. However, unlike person re-identification, the visual differences between pairs of vehicle images are usually subtle and even challenging for humans to distinguish. Incorporating additional spatio-temporal information is vital for solving the challenging re-identification task. Existing vehicle re-identification methods ignored or used over-simplified models for the spatio-temporal relations between vehicle images. In this paper, we propose a two-stage framework that incorporates complex spatio-temporal information for effectively regularizing the re-identification results. Given a pair of vehicle images with their spatio-temporal information, a candidate visual-spatio-temporal path is first generated by a chain MRF model with a deeply learned potential function, where each visual-spatio-temporal state corresponds to an actual vehicle image with its spatio-temporal information. A Siamese-CNN+Path-LSTM model takes the candidate path as well as the pairwise queries to generate their similarity score. Extensive experiments and analysis show the effectiveness of our proposed method and individual components., Comment: To appear in ICCV 2017

To improve the obstacle surmounting performance of the quad-rotor vehicle, this paper focuses on designing, kinematically and dynamically characterizing a novel deformable quad-rotor that is based on the scissor-like foldable structures. The foldable structure allows that the volume of the quad-rotor can be tuned to dynamically adapt variously sized obstacles and small spaces. To generate the controllable deformation, the actuated angulated elements that are the essential components of the scissor-like foldable structure play an important role. The element design, its actuation mechanism and the corresponding configuration patterns for the new quad-rotor are presented in the paper in detail. The simulations on deformation properties and obstacle surmounting ability are then performed to verify the deformation capability of the structure. In addition, experiments were extensively conducted to test the controlled deformation of the structure as well to investigate the deformation induced effects to the activated quad-rotor airframe and its aerodynamics. All implementation results validate the effectiveness of the proposed deformable quad-rotor design, that is, it enables the new quad-rotor having excellent obstacle surmounting performance, adaptability, flight maneuverability, as well as minimal aerodynamics influences during deforming.

Inspired by natural earthworm locomotion behavior and segmental muscle motion mechanism, this paper presents our recently developed compliant modular earthwormlike robot with the novel segmental muscle-mimetic design unit that is capable of efficiently mimicking earthworms' segmental muscle contraction and extension functions. The new class of segmental muscle-mimetic design unit relies on the curvature of scissor mechanisms that can be extended and contracted smoothly through controlled servo motors. The paper starts reviewing natural earthworm locomotion behavior, details the bio-inspired concept and design of both the segmental muscle-mimetic unit and the multi-segment earthworm-like robot prototype, and then presents the robot's locomotion models and analysis of the locomotion efficiency of the robot. Simulation and experimental results validate that both the design and the prototyped multi-segment earthworm-like robot have the excellent performance such as its muscle-like contractions behavior, peristaltic locomotion behavior, and highly competitive moving speed.

A wafer-level test system is proposed for a high-g MEMS accelerometer based on a probe station. To simulate a high shock on the packaged sensors, a physical stimulus is loaded on the dies using a micromechanics device. The probe-station controller is controlled by a MFC program developed within the Microsoft Visual Studio Platform. The controller is able to move with three degrees of freedom in performing the automated tests of wafers. A data acquisition board is used to receive output signals from the dies through a reliable contact between the probe card and pads. The test procedure runs a LabVIEW program for post-processing the acquired signals including evaluating computing sensitivity and other parameters. An analysis of the test results determines at the wafer-level whether the die is good. This method may be applied during manufacturing of the high-g MEMS accelerometers where the benefits are seen in reduced production costs and a shorter time to market.

A 3D printed snakebot prototype is developed and experimentally validated in this paper. The structure is designed to allocate limited room for each functional module (including an external power module, battery power module, the wireless control & transmission module and some detective sensors) to ensure the snakebot work in different environments. For controlling and tracking the snakebot, a MEMS IMU (Micro-Electro-Mechanical-Systems Inertial Measurement Units) based snakebot motion tracking system is developed. To minimize the noise influence to tracking accuracy, three algorithms (low-pass filter, baseline calibration, and Kalman filter) are used to eliminate noise from IMU's acceleration data. Through signal processing, the IMU acceleration data can be effectively used for motion tracking. To evaluate the motion tracking algorithm efficiency, the result from a real-time video tracking is employed as a reference for comparison. The comparison results demonstrate high efficiency of the proposed IMU based motion tracking algorithm.

In previous works, we introduced a microscopy system based on the PSD (Position Sensitive Detector) sensor that was capable of scanning sophisticated objects by employing a PSD sensor and a laser beam moved by micromanipulator acting as an X-Y table. The microscopy system can cover numerous operations including locating an unknown object on the surface of the PSD, measuring the dimensions of the object and adaptively doing a local scanning for objects with bifurcations and intersections. Even though the results from this system indicate significant and noteworthy outcomes, but still, the speed of the scanning of the laser depends on the speed of the movement of the micromanipulator. This research, presents an extension to the microscopy system by introducing a completely novel system to eliminate the limitations of the micromanipulator. The simulations show an ultra fast scanning that does not need laser and can scan the whole area of the PSD in a very short time.

A PSD (Position Sensitive Detector) -based microscopy was introduced previously by same authors to scan sophisticated objects using the PSD sensor and a laser beam connected to an X-Y table (Rahimi et al., 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp 1685–1690 (2014) [1]; Rahimi et al., 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 4955–4960 (2015) [2]). The system has numerous capabilities including but not limited to tracing an unknown object on the PSD, finding the dimensions of the object and adaptive local scanning for objects with intersections and bifurcations. Although promising results were obtained from that system, the speed of the scanning was still dependent on the speed of the movement of the micromanipulator. This work presents an extension to the tracing method by scanning tree-shaped objects. The simulations show a very successful scanning that can map any tree-shaped object.

To facilitate the application of foldable scissor-like element in engineering domain, this article focuses on the design of 3-loop expandable mechanism. The property of angulated element is revealed first to contribute to the modeling of theoretical model with 3-loop, and the concept of four expandable angulated elements sharing one square adaptor is then introduced to build the mechanical system. In order to verify the mechanical model, the kinetic simulation is conducted to make a comparison between theoretical and mechanical model. After validation, system identification method is used to obtain the transfer function of the expandable system based on the measured data. Finally, the primary experiment proves the feasibility of the expandable mechanism.

This paper presents our recent research work in developing a portable and refreshable text reading system, called Finger-eye. In the system, a small camera is added to the fingertip-electrode interface of the current Electro-tactile Braille Display and placed on a blind person's finger to continuously process images using a developed rapid optical character recognition (OCR) method. This will allow translation of text to braille or audio with natural movement as if they were reading any Braille Display or book. The braille system that will be used is a portable electrical-based braille system that will eliminate the problems associated with refreshable mechanical braille displays. The goal of the research is to aid the blind and visually impaired (BVI) with a portable means to translate any text to braille, whether in the digital realm or physically, on any surface.

We present our development of a supermedia interface for telediagnostics of breast pathology via the Internet. Supermedia is the incorporation of multiple media and data streams, such as audio, video, ultrasound images, and tactile and haptic sensor data for enhanced telepresence capabilities. Our supermedia interface comprises an anthropomorphic arm/hand equipped with haptic and tactile sensing, ultrasound imaging capabilities, a physician interface capable of rendering both haptic and tactile information, and two-way audio and video. The supermedia interface was tested via the Internet and enables telediagnostics. Preliminary experiments with silicone breast models have demonstrated the e fectiveness of the supermedia interface for telediagnostics. The interface is expected to enhance and improve the diagnostic performance of breast pathology as well as promote telediagnostics in remote areas and for patients with cultural barriers.