Behavior-based assistive robotics for the home

Due to its emergence as an efficient platform for point-of-care clinical diagnostics, design optimization of digital-microfluidic biochips (DMFBs) has received considerable attention in recent years. In particular, error recoverability is of key interest in medical applications due to the need for system reliability. Errors are likely during droplet manipulation due to defects, chip degradation, and the lack of precision inherent in biochemical experiments. We present an illustrative survey on recently proposed techniques for error recovery. The parameters of the error-recovery design space are shown and evaluated for these schemes. Next, we make use of these evaluations to describe how they can guide error recovery in DMFBs. Finally, an experimental case study is presented to demonstrate how an error-recovery scheme can be applied to real-life biochips.

Applying error recovery monotonously can either compromise the real-time constraint, or worsen the power/energy envelope. Neither of these violations can be realistically accepted in embedded system design, which expects ultra efficient realization of a given application. In this paper, we propose a HW/SW methodology that exploits both application specific characteristics and Spatial/Temporal redundancy. Our methodology combines design-time and runtime optimizations, to enable the resultant embedded processor to perform runtime adaptive error recovery operations, precisely targeting the reliability-wise critical instruction executions. The proposed error recovery functionality can dynamically 1) evaluate the reliability cost economy (in terms of execution-time and dynamic power), 2) determine the most profitable scheme, and 3) adapt to the corresponding error recovery scheme, which is composed of spatial and temporal redundancy based error recovery operations. The experimental results have shown that our methodology at best can achieve fifty times greater reliability while maintaining the execution time and power deadlines, when compared to the state of the art.

Use of error management theory to quantify and characterize residents’ error recovery strategies

Distributed Speech Recognition (DSR) systems rely on efficient transmission of speech information from distributed clients to a centralized server. Wireless or network communication channels within DSR systems are typically noisy and bursty. Thus, DSR systems must utilize efficient Error Recovery (ER) schemes during transmission of speech information. Some ER strategies, referred to as forward error control (FEC), aim to create redundancy in the source coded bitstream to overcome the effect of channel errors, while others are designed to create spread or delay in the feature stream in order to overcome the effect of bursty channel errors. Furthermore, ER strategies may be designed as a combination of the previously described techniques. This chapter presents an array of error recovery techniques for remote speech recognition applications. This chapter is organized as follows. First, channel characterization and modeling are discussed. Next, media-specific FEC is presented for packet erasure applications, followed by a discussion on media-independent FEC techniques for bit error applications, including general linear block codes, cyclic codes, and convolutional codes. The application of unequal error protection (UEP) strategies utilizing combinations of the aforementioned FEC methods is also presented. Finally, framebased interleaving is discussed as an alternative to overcoming the effect of bursty channel erasures. The chapter concludes with examples of modern standards for channel coding strategies for distributed speech recognition (DSR). 8.1. Distributed Speech Recognition Systems Throughout this chapter various error recovery and detection techniques are discussed. It is therefore necessary to present an overview of a complete experimental DSR system, including feature extraction, a noisy channel model, and an automatic speech recognition engine at the server end (Fig. 8.1). 2 Bengt J. Borgstrom, Alexis Bernard, and Abeer Alwan Feature Extraction Source Coding Channel Coding Channel Decoding Source Decoding Speech Recognition Client Server Noisy Channel input speech

The system-level reliability of solid-state drives (SSDs) is investigated with 1X, 2X and 3Xnm NAND flash memories. The reliability degradation of NAND with scaling is an serious issue. Advanced ECC with signal processing such as error-prediction low-density parity-check (EP-LDPC) and error recovery (ER) scheme will be needed in the future SSDs. In this paper, the NAND reliability information used for EP-LDPC and ER is examined. System-level reliability with conventional ECC and EP-LDPC is measured.

Transient bit flips may cause unexpected behaviors in digital arithmetic circuits, and thus computation errors. It is generally believed that stochastic computing (SC) can tolerate bit flips due to its unique number representation format. In SC, a value is encoded by a random bit stream using the probability of 1s in the bit stream. Therefore, one bit flip changes the probability by a small number given a long enough bit stream, and the computation results might not change much. This is verified in previous works mostly through empirical studies. However, we found that bit flips actually change the probability of a stochastic bit stream by a predictable value. This is formulated by considering the bit flips as Bernoulli events. The distorted probability of the bit stream considering bit flips is derived and the SC bit flip models for various stochastic circuits are proposed. The error recovery based on the formulation is also proposed. The SC bit flip model and error recovery scheme are verified on basic SC elements such as the SC multipliers and an FSM-based stochastic circuit. They are then applied to a complex SC system computing a neural network. The results show that the accuracy of MNIST digit recognition can be recovered even with a high bit flip rate of 10%.

We present a navigation system using multiple sensors for unknown and dynamic indoor environments. To achieve the robustness and flexibility of the mobile robot, we propose a new behavior-based architecture with three groups of clustered (reflexive, purposive, and adaptive) agents that realizes both efficiency in attaining the mission of the robot and robustness against the various kinds or failures that may occur in a dynamic environment. Basic behaviors required for navigation, such as, avoiding obstacles, moving towards free space, and following targets, are redundantly developed as agents and combined in the behavior-based system architecture.

This paper presents a detailed discussion of a number of important issues associated with an integrated intelligent control system (IICS) using a knowledge-based approach for a flexible manufacturing cell (FMC). The architecture of the system consists of several functional modules for design, planning, sensing, control, error recovery and the actions of the cell's devices. These modules are: the CAD-based user interface; the product data model; a process planner module; a knowledge based system; a cell organiser module; the sensory system; the control module; and an error detection and recovery module. All are linked together in a structure which is based on a three level, hierarchical, intelligent-machine architecture. The developed system in total is capable of performing various manufacturing tasks without requiring any human support, even under the influence of uncertain or unexpected conditions.

In wireless sensor networks, it has been proved that the reliable transmission protocols sending redundant packets to the upstream neighbour hop-by-hop have advantage on energy efficiency compared with those using end-to-end error recovery and control scheme. It provides an opportunity for applications to find a trade off point regarding transmission probability and energy consumption. The problem is, though, this kind of protocol loses a bit of its charm when considering burst and deep channel errors. According to this, the burst and deep Fading Avoid Transmission Protocol (FATP) is proposed. The novelty of this protocol is that, by using channel model technology and hidden Markov model (HMM) based algorithm, it can evade the burst and deep fading period of the wireless channel artfully with high probability. A detailed theoretic performance analysis process is presented in this paper. Compare with previous hop-by-hop reliable transmission protocols, the numerical results reveal that FATP can improve the energy efficiency significantly. Furthermore, if the channel bit error rate filter threshold is tuned in appropriate range, the delivery delay could be controlled in an acceptable value or even lower than other hop-by-hop reliable transmission protocols, especially in high BER conditions.

The author describes techniques for planning multistep error detection and recovery strategies for robots operating in the presence of uncertainty. He introduces two approaches for their synthesis: the push-forward algorithm and failure mode analysis. He has implemented the theory in the form of a planner, called LIMITED, in the domain of planar assemblies. >

It is difficult to archive task sequences in daily life environment for home assistant robots without any faults. In this paper, an integrated robot system toward fault tolerant in task level is presented. In this system, we utilize rule-based, knowledge-based descriptions for error detection and error recovery. Goal States of operators of task planner is used in these descriptions in order to increse reusability of error detection and error recovery rules. The key features of this system are: 1) modular and layered system configuration. 2) error recovery rule and detection rule using state descriptions in task-level. 3) definition of error recovery as list operations to action sequences generated by a task planner.

Cache data errors read by a processor may cause CPU control flow error and force the system to enter a CPU-cache reintegration process in redundant processor systems. The reintegration process degrades the system performance and reliability. To reduce the occurrences of such an event, we propose a real-time error recovery scheme that provides effective fault-containment for data errors in cache memories. The scheme is based on cache data broadcasting of a dirty line after modification. It effectively exploits the redundancy of a fault-tolerant system using hardware voting. The scheme recovers from erroneous cache data written by a processor with full coverage. This error recovery feature remedies the insufficiency of error-correcting codes that are unable to prevent such an error. In addition, more than 60 percent of cache lines are fully covered for recovery due to errors originated from the cache itself, including unrecoverable ECC errors. The protocol can also be used to speedup the CPU-cache reintegration process for a temporarily failed processor. The performance overhead of the protocol is to broadcast only 2-3 percent of the total memory references.

With the development of intelligent home appliance technology, real-time home appliance status information is now generated in large quantities. New technology is necessary in order to process the large amount of status information that is generated every day. An innovative technology that has recently been used to process large amounts of data is cloud computing. Therefore, in this paper, we propose a system model to control and monitor home appliances using home network and cloud computing technologies in a smart home environment. UPnP technology is used to extract status information from home appliances. Cloud computing technology analyzes and processes the information and also provides virtualization services to users. In the proposed method, the gateway collects and stores home appliance information using home network technologies and sends the information to the cloud server for storage and management.

This paper describes an attempt to develop an automatic unmanned aerial vehicle (UAV) control system for emergency rescue and investigation. A cascade classifier system using Haar-like features for object recognition from a free viewpoint is also presented. Conventional emergency rescue systems have a shortcoming in terms of their performance. Thus, we introduce an automatic UAV control system in order to overcome the shortcoming of conventional emergency rescue systems in an emergency situation. The proposed system involves two processing stages. One involves a capturing and recognition of target objects, and the other involves controling the UAV using digital intelligence. Object detection in many types of images as well as real-time simulation methods continue to be one of the most common techniques for information processing and applications. The fundamental concepts of the proposed system and some experimental results obtained by using related processing techniques are also described.

Vitrectomy is that portion of retinal surgery in which the vitreous gel is removed either as a definitive treatment or to provide direct tool access to the retina. This procedure should be conducted prior to several eye surgeries in order to provide better access to the eyeball posterior. It is a relatively repeatable and straight forward procedure that lends itself to robotic assistance or potentially autonomous performance if tool contact with critical structures can be avoided. One of the detrimental incidences that can occur during the robot-assisted vitrectomy is when the robot penetrates the tool more than allowed boundaries into the eyeball toward retina. In this paper, we provide filtering and control to guide instrument insertion depth in order to avoid tool-to-retina contact. For this purpose, first the tool insertion depth measurement is improved using a Kalman filtering (KF) algorithm. This improved measurement is then used in an adaptive control strategy by which the robot reduces the tool insertion depth based on a predefined and safe trajectory for it, when safe boundaries are overstepped. The performance of the insertion depth safety control system is then compared to one in which the insertion depth is not passed through a Kalman filter prior to being fed to the control system. Our results indicate that applying KF in the adaptive control of the robot enhances procedure safety and enables the robot to always keep the tool insertion depth under the safe levels.