Robotic technologies are increasingly used in dentistry for their precision in delicate procedures. While most dental robots focus on implant surgery, automating root canal treatment (RCT) remains challenging due to the need to guide a thin, flexible endodontic file through a narrow, curved root canal without causing ledging or file fracture. Patient movements—particularly those that induce additional file bending during insertion—further complicate robot-assisted procedures. This study presents an autonomous approach for root canal cleaning and shaping by combining force admittance and position tracking. A novel Patient Tracking Module, which connects the patient’s dental brace to the robot end-effector via string potentiometers, is developed to estimate real-time robot-patient pose. Additionally, a file flexibility model is proposed to predict and compensate for file deflection during insertion. A hybrid position/force control strategy, which integrates these estimations, autonomously guides file manipulation, minimizes misalignment, and therefore reduces the risk of file fracture. Experimental validation demonstrates the system’s feasibility and potential for clinical application in precision endodontic procedures.

In response to the Grand Challenge on Neural Network-based Video Coding at ISCAS 2024, this paper proposes a learned hierarchical B-frame coding scheme. Most learned video codecs concentrate on P-frame coding for the RGB content, while B-frame coding for the YUV420 content remains largely under-explored. Some early works explore Conditional Augmented Normalizing Flows (CANF) for B-frame coding. However, they suffer from high computational complexity because of stacking multiple variational autoencoders (VAE) and using separate Y and UV codecs. This work aims to develop a lightweight VAE-based B-frame codec in a conditional coding framework. It features (1) extracting multi-scale features for conditional motion and inter-frame coding, (2) performing frame-type adaptive coding for better bit allocation, and (3) a lightweight conditional VAE backbone that encodes YUV420 content by a simple conversion into YUV444 content for joint Y and UV coding. Experimental results confirms its superior compression performance to the CANF-based B-frame codec from the last year’s challenge while having much reduced complexity.

Conditional coding has lately emerged as the mainstream approach to learned video compression. However, a recent study shows that it may perform worse than residual coding when the information bottleneck arises. Conditional residual coding was thus proposed, creating a new school of thought to improve on conditional coding. Notably, conditional residual coding relies heavily on the assumption that the residual frame has a lower entropy rate than that of the intra frame. Recognizing that this assumption is not always true due to dis-occlusion phenomena or unreliable motion estimates, we propose a masked conditional residual coding scheme. It learns a soft mask to form a hybrid of conditional coding and conditional residual coding in a pixel adaptive manner. We introduce a Transformer-based conditional autoencoder. Several strategies are investigated with regard to how to condition a Transformer-based autoencoder for inter-frame coding, a topic that is largely under-explored. Additionally, we propose a channel transform module (CTM) to decorrelate the image latents along the channel dimension, with the aim of using the simple hyperprior to approach similar compression performance to the channel-wise autoregressive model. Experimental results confirm the superiority of our masked conditional residual transformer (termed MaskCRT) to both conditional coding and conditional residual coding. On commonly used datasets, MaskCRT shows comparable BD-rate results to VTM-17.0 under the low delay P configuration in terms of PSNR-RGB and outperforms VTM-17.0 in terms of MS-SSIM-RGB. It also opens up a new research direction for advancing learned video compression.

In this study, the infant Cardiac Robotic Surgical System (iCROSS) is developed to assist a surgeon in performing the patent ductus arteriosus (PDA) closure and other infant cardiac surgeries. The iCROSS is a dual-arm robot allowing two surgical instruments to collaborate in a narrow space while keeping a sufficiently large workspace. Compared with the existing surgical robotic systems, the iCROSS meets the specific requirements of infant cardiac surgeries. Its feasibility has been validated through several teleoperated tasks performed in the experiment. In particular, the iCROSS is able to perform surgical ligation successfully within one minute.

Due to the precise manipulations required in dental surgery, robotic technologies have been applied to dentistry. So far, most dental robots are designed for implant surgery, helping dentists accurately place the implant to the desired position and depth. This paper presents the DentiBot, the first robot designed for dental endodontic treatment. Without visual feedback, the DentiBot is integrated with a force and torque sensor to monitor the contact between the root canal and endodontic file. Additionally, DentiBot is implemented with force-guided alignment and file feedrate control to autonomously adjust surgical path and compensate for patient movement in real-time while protecting against endodontic file fracture. The feasibility of robot-assisted endodontic treatment is verified by the pre-clinical evaluation performed on acrylic root canal models.

Most alignment algorithms find an initial equivalent residue pair followed by an iterative optimization process to explore better near-optimal alignments in the surrounding solution space of the initial alignment. It plays a decisive role in determining the alignment quality since a poor initial alignment may make the final alignment trapped in an undesirable local optimum even with an iterative optimization. We proposed a vector-based alignment algorithm with a new initial alignment approach accounting for local structure features called MIRAGE-align. The new idea is to enhance the quality of the initial alignment based on encoded local structural alphabets to identify the protein structure pair whose sequence identity falls in or below twilight zone. The statistical analysis of alignment quality based on Match Index (MI) and computation time demonstrated that MIRAGE-align algorithm outperformed four previously published algorithms, i.e., the residue-based algorithm (CE), the vector-based algorithm (SSM), TM-align, and Fr-TM-align. MIRAGE-align yields a better estimate of initial solution to enhance the quality of initial alignment and enable the employment of a non-iterative optimization process to achieve a better alignment.

Obtaining the distance of an object using image capture devices is a convenient and practical approach for intelligent 3D technology. “Depth from defocus” is one of the recovery methods, which has the advantage of using only a single static viewpoint. In this paper, a new method is proposed to represent the defocus blur amount by the spectral energy of the point spread function for depth recovery in a single image. Different from the previous depth from defocus methods, our intuitive approach identifies the blur amount effectively without modeling the defocus blur kernel, and has a high quality of recovery in theory. Experiments using an uncalibrated commercial digital camera have validated the proposed depth recovery technology and shown a considerably good extent of accuracy.

Conditional execution is an important ISA feature of the ARM series of processors. Every instruction can be made to execute conditionally, that is, it is treated as a NOP if the condition is not met. The advantage of conditional execution is that it can maintain high performance while reducing hardware complexity since it can avoid introducing pipeline bubbles even when no branch prediction units are needed. However, conditional execution takes up precious instruction space as conditions are encoded into a 4-bit condition code selector on every 32-bit ARM instruction. Besides, only small percentages of instructions are actually conditionalized in modern embedded applications, and conditional execution might not even lead to performance improvement on modern embedded processors. This paper proposes to trade conditional execution for more ISA registers on ARM processors, and the 4-bit condition field will be used to encode the extra registers. GCC has been ported to generate ARM code with the new instruction format and experimental results have shown that performance can be improved by 6% on average for Media Bench II benchmarks when the number of ISA registers is extended from 16 to 32.

It is necessary to analyze the anti-terrorism performance of Taiwan’s security system after September 11, 2001 attacks. We use three stage data envelopment analysis model that Fried et al. (2002) developed, getting intelligence performance of the Investigation Bureau of the Ministry of Justice, to discuss the ability of anti-terrorism in Taiwan’s national security system. Putting 10 outputs, 2 inputs and 42 environment variables into this model, we find out output slacks and remove environmental impacts to intelligence. As a result, most of local field offices get higher efficiency scores compared with first stage’s DEA result. That means most of local field offices’ intelligence works with effective management. Furthermore, we analyze total factor productivity and find the change of intelligence performance coming from efficiency improving and technology degenerating. It seems Investigation Bureau of the Ministry of Justice works efficiently with traditional human resource management, yet could not deal with technical shock like new type of terrorism. We suggest that security institutions should train employees with advanced technology to overcome Taiwan’s security system deficiency.

The returns and risks of high-yield bond (HYB) lie between the stocks and Treasury bonds. In view of investment opportunities and the rate of return, the advantages of HYB are both lower risks and higher shares. Therefore, HYB has become one of important components in the portfolios. The purpose of this study is to find evaluation factors and their weights to aid the selection of HYB. The primary criteria to evaluate HYB are established by the literatures survey with Fuzzy Delphi Method (FDM), and then Fuzzy Analytic Hierarchy Process (FAHP) is employed to calculate the weights of these criteria, so as to build the Fuzzy Multi-criteria model of HYB investments. The results indicate a greatest weight on the dimension of economic environment, and three primary evaluation criteria are: (1) spread versus Treasuries, (2) callability, and (3) default rate.

Distributed object-oriented environments have become important platforms for parallel and distributed service frameworks. Among distributed object-oriented software, .NET Remoting provides a language layer of abstractions for performing parallel and distributed computing in .NET environments. In this paper, we present our methodologies in supporting .NET Remoting over meta-clustered environments. We take the advantage of the programmability of network processors to develop the content-based switch for distributing workloads generated from remote invocations in .NET. Our scheduling mechanisms include stateful supports for .NET Remoting services. In addition, we also propose scheduling policy to incorporate workflow models as the models are now incorporated in many of tools of grid architectures. Experiments done at clusters with IXP 1200 network processors show that our scheme can significantly enhance the system throughput (up to 55%) compared to NLB method when the traffic is heavy. Our schemes are effective in supporting the switching of .NET Remoting computations over meta-cluster environments.

Abstract Distributed object‐oriented platforms are increasingly important over wireless environments for providing frameworks for collaborative computations and for managing a large pool of distributed resources. Due to limited bandwidths and heterogeneous architectures of wireless devices, studies are needed into supporting object‐oriented frameworks over heterogeneous wireless environments and optimizing system performance. In our research work, we are working towards efficiently supporting object‐oriented environments over heterogeneous wireless environments. In this paper, we report the issues and our research results related to the efficient support of Java RMI over a Bluetooth environment. In our work, we first implement support for Java RMI over Bluetooth protocol stacks, by incorporating a set of protocol stack layers for Bluetooth developed by us (which we call JavaBT) and by supporting the L2CAP layer with sockets that support the RMI socket. In addition, we model the cost for the access patterns of Java RMI communications. This cost model is used to guide the formation and optimizations of the scatternets of a Java RMI Bluetooth environment. In our approach, we employ the well‐known BTCP algorithm to observe initial configurations for the number of piconets. Using the communication‐access cost as a criterion, we then employ a spectral‐bisection method to cluster the nodes in a piconet and then use a bipartite matching scheme to form the scatternet. Experimental results with the prototypes of Java RMI support over a Bluetooth environment show that our scatternet‐formation algorithm incorporating an access‐cost model can further optimize the performances of such as system. Copyright © 2005 John Wiley & Sons, Ltd.

Distributed object-oriented platforms are increasingly important over wireless environments to provide frameworks for collaborative computations and for managing a large pool of distributed resources. For beyond 3G environments, distributed object-oriented platforms can provide the framework and toolkits for application developments with heterogeneous wireless environments. In this paper, we present our support for Java RMI over Bluetooth, GPRS, and WLAN environments. We propose a software mechanism which can be used to dynamically adapt RMI over different networks with optimization-related strategies. This is an important middleware for component communications. We will show how to employ Java Dynamic Proxy and exception handling techniques to help perform roaming and resource scheduling among heterogeneous wireless environments. Java Grande benchmarks are used to demonstrate that our RMI implementations over GPRS, WLAN, and Bluetooth environments are effective in supporting parallel and distributed control of Java layers over heterogeneous wireless environments.

Summary form only given. With the arrival of the new computing paradigm in addressing autonomous systems for heterogeneous distributed architectures, distributed component technologies face challenges ahead. One of the key issues is how one can have component models adapt and respond to environment changes autonomously. We argue that additional annotation specifications for components are needed to advance this process. We first present additional annotation specifications for components. This information can then be retrieved by Java introspection and represented in DAML+OIL language, which is based on the RDF schema and the XML syntax. Based on the specifications, we then present a component management service (CMS) model and architecture to address the specification and composition issues of components on heterogeneous distributed architectures. Experimental results show significant performance improvements with our support of component adaptations in all cases. Our work presents a major advance for areas related to specifications and compositions of distributed components.

We present a generic matrix class facility in Java and an on-going project for a runtime environment with continuous compilation aiming to support automatic parallelization of sparse computation on distributed environments. Our package comes with a collection of matrix classes with a uniform interface for operations on dense and sparse matrices. These matrix operations are implemented both for sequential and parallel executions on distributed memory environments. In our environment, a program such as the conjugate gradient solver is written by users using high-level generic matrix notations in Java. At runtime the generic notations are mapped to specific implementations. Our approach is particularly useful for optimizing sparse computation for distributed environments because, with the help of profiling information and a cost model, it can automatically select suitable compression and distribution schemes according to access patterns of the programs and non-zero structures of the matrices. Our testbed is currently based on Java and PVM on an IBM SP2 workstation cluster. Preliminary experimental results show that our approach is promising in speeding up sparse matrix computations on distributed memory environments. © 1997 John Wiley & Sons, Ltd.