Nenggan Zheng

Static Security Optimization for Real-Time Systems

An increasing number of real-time applications like railway signaling control systems and medical electronics systems require high quality of security to assure confidentiality and integrity of information. Therefore, it is desirable and essential to fulfill security requirements in security-critical real-time systems. This paper addresses the issue of optimizing quality of security in real-time systems. To meet the needs of a wide variety of security requirements imposed by real-time systems, a group-based security service model is used in which the security services are partitioned into several groups depending on security types. While services within the same security group provide the identical type of security service, the services in the group can achieve different quality of security. Security services from a number of groups can be combined to deliver better quality of security. In this study, we seamlessly integrate the group-based security model with a traditional real-time scheduling algorithm, namely earliest deadline first (EDF). Moreover, we design and develop a security-aware EDF schedulability test. Given a set of real-time tasks with chosen security services, our scheduling scheme aims at optimizing the combined security value of the selected services while guaranteeing the schedulability of the real-time tasks. We study two approaches to solve the security-aware optimization problem. Experimental results show that the combined security values are substantially higher than those achieved by alternatives for real-time tasks without violating real-time constraints.

Automatic navigation for rat-robots with modeling of the human guidance

A biorobot system refers to an animal equipped with Brain-Computer Interface (BCI), through which the outer stimulation is delivered directly into the animal’s brain to control its behaviors. The development of biorobots suffers from the dependency on real-time guidance by human operators. Because of its inherent difficulties, there is no feasible method for automatic controlling of bio-robots yet. In this paper, we propose a new method to realize the automatic navigation for bio-robots. A General Regression Neural Network (GRNN) is adopted to analyze and model the controlling procedure of human operations. Comparing to the traditional approaches with explicit controlling rules, our algorithm learns the controlling process and imitates the decision-making of human-beings to steer the rat-robot automatically. In real-time navigation experiments, our method suc-cessfully controls bio-robots to follow given paths automatically and precisely. This work would be significant for future applications of bio-robots and provide a new way to realize hybrid intelligent systems with artificial intelligence and natural biological intelligence combined together.

Scheduling Co-Design for Reliability and Energy in Cyber-Physical Systems

Energy aware scheduling and reliability are both very critical for real-time cyber-physical system design. However, it has been shown that the transient faults of a system will increase when the processor runs at reduced speed to save energy consumption. In this paper, we study total energy and reliability scheduling co-design problem for real-time cyber-physical systems. Total energy refers the sum of static and dynamic energy. Our goal is to minimize total energy while guaranteeing reliability constraints. We approach the problem from two directions based on the two different ways of guaranteeing the reliability of the tasks. The first approach aims at guaranteeing reliability at least as high as that of without speed scaling by reserving recovery job for each scaled down task. Heuristics have been used to guide the speed scaling and shutdown techniques that are used to lower total energy consumption while guaranteeing the reliability. The second way to guarantee the reliability of the tasks is to satisfy a known minimum reliability constraint for the tasks. The minimum reliable speed guarantees the reliability level of tasks, and is used as a constraint in the energy minimization problem. Both static and dynamic co-design methods are explored. Experimental results show that our methods are effective.

Enhancing Battery Efficiency for Pervasive Health-Monitoring Systems Based on Electronic Textiles

Electronic textiles are regarded as one of the most important computation platforms for future computer-assisted health-monitoring applications. In these novel systems, multiple batteries are used in order to prolong their operational lifetime, which is a significant metric for system usability. However, due to the nonlinear features of batteries, computing systems with multiple batteries cannot achieve the same battery efficiency as those powered by a monolithic battery of equal capacity. In this paper, we propose an algorithm aiming to maximize battery efficiency globally for the computer-assisted health-care systems with multiple batteries. Based on an accurate analytical battery model, the concept of weighted battery fatigue degree is introduced and the novel battery-scheduling algorithm called predicted weighted fatigue degree least first (PWFDLF) is developed. Besides, we also discuss our attempts during search PWFDLF: a weighted round-robin (WRR) and a greedy algorithm achieving highest local battery efficiency, which reduces to the sequential discharging policy. Evaluation results show that a considerable improvement in battery efficiency can be obtained by PWFDLF under various battery configurations and current profiles compared to conventional sequential and WRR discharging policies.

Infrastructure and Reliability Analysis of Electric Networks for E-Textiles

Electronic textiles (e-textiles), known as computational fabrics, offer an emerging platform for constructing ambient intelligent applications. Computational nodes in e-textiles are driven by batteries. Unlike wireless sensor networks, not each computational node in e-textiles has its own battery. Instead, many computational nodes in e-textiles share a battery. Existing e-textiles use one fixed battery to drive a fixed set of computation nodes (or power consuming electronic components). The fixed battery-component connection may result in electronic components stopping functioning and/or energy waste in batteries when link connection problems occur. In this paper, we propose a new infrastructure of the power networks for e-textiles: flexible power network (FPN). Under the FPN infrastructure, a power consuming node (PCN) is not just connected to one single fixed battery. Instead, it is connected to multiple batteries and can obtain power energy from one of the available battery nodes (BNs) with the help of a battery selector. The electrical features of battery selectors and overcurrent protectors that protect the batteries from wasting the charge when short-circuit faults occur are illustrated. Moreover, by modeling the number of fault occurrence at conductive wires and nodes stochastically, an evaluation algorithm is proposed to analyze the reliability of FPN and to compare the metrics of different design schemes under the perspective of both the BNs and the PCNs. Experimental results show that our FPN is more dependable than some common e-textile electric networks published before with the occurrence of short- and/or open-circuit faults.

Intelligence-Augmented Rat Cyborgs in Maze Solving

Cyborg intelligence is an emerging kind of intelligence paradigm. It aims to deeply integrate machine intelligence with biological intelligence by connecting machines and living beings via neural interfaces, enhancing strength by combining the biological cognition capability with the machine computational capability. Cyborg intelligence is considered to be a new way to augment living beings with machine intelligence. In this paper, we build rat cyborgs to demonstrate how they can expedite the maze escape task with integration of machine intelligence. We compare the performance of maze solving by computer, by individual rats, and by computer-aided rats (i.e. rat cyborgs). They were asked to find their way from a constant entrance to a constant exit in fourteen diverse mazes. Performance of maze solving was measured by steps, coverage rates, and time spent. The experimental results with six rats and their intelligence-augmented rat cyborgs show that rat cyborgs have the best performance in escaping from mazes. These results provide a proof-of-principle demonstration for cyborg intelligence. In addition, our novel cyborg intelligent system (rat cyborg) has great potential in various applications, such as search and rescue in complex terrains.

Cyborg Intelligence: Recent Progress and Future Directions

The combination of biological and artificial intelligence is a promising methodology to construct a novel intelligent modality, proposed as cyborg intelligence. The hierarchical conceptual framework is based on the interaction and combination of comparable components of biological cognitive units and computing intelligent units. The authors extend the previous conceptual framework and focus on sensorimotor circuits to explore the representation and integration of sensation. They then present a cognitive computing model for brain-computer integration and design efficient machine-learning algorithms for neural signal decoding. They also propose biological reconstruction methods for sensorimotor circuits to not only restore but enhance functionalities with AI. They develop a series of demonstrating systems to validate the conceptual framework of cyborg intelligence and possibly herald bright prospects and high values in diversified aspects of theoretical research, engineering techniques, and clinical applications. Finally, the authors summarize the latest research trends and challenges, which they believe will further boost new scientific frontiers in cyborg intelligence.

Attribute Selection for Partially Labeled Categorical Data By Rough Set Approach

Attribute selection is considered as the most characteristic result in rough set theory to distinguish itself to other theories. However, existing attribute selection approaches can not handle partially labeled data. So far, few studies on attribute selection in partially labeled data have been conducted. In this paper, the concept of discernibility pair based on rough set theory is raised to construct a uniform measure for the attributes in both supervised framework and unsupervised framework. Based on discernibility pair, two kinds of semisupervised attribute selection algorithm based on rough set theory are developed to handle partially labeled categorical data. Experiments demonstrate the effectiveness of the proposed attribute selection algorithms.

Flight control of tethered honeybees using neural electrical stimulation

This paper presents an insect-machine interface for controlling the flight behavior of tethered honeybees. Flight initiation and cessation can be reproducibly generated using electrical pulses between two wire electrodes implanted into the honeybees brain. Experiments are conducted to compare the effect of different stimulus patterns on the honeybees behavior by parameters including success rate, response time and flight duration. The preliminary results enable us to carry out further research works on the control of complex flight behaviors or the neural mechanism of insect flight.

Maze learning by a hybrid brain-computer system

The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.