Effects of Permafrost and Seasonally Frozen Ground on the Seismic Response of Transportation Infrastructure Sites

INE/AUTC 11.0

Acoustic leakage in electromagnetic waveguides made from piezoelectric materials

We study the propagation of coupled acoustic and electromagnetic waves in a piezoelectric plate waveguide embedded in two half-spaces of another dielectric material. It is shown that certain waves are guided electromagnetically but not so acoustically and that these waves are effectively damped waves with acoustic leakage of energy. An estimate of the acoustic radiation damping is given. © 2007 American Institute of Physics.published_or_final_versio

Effectiveness of HT-assisted Sinkhole and Blackhole Denial of Service Attacks Targeting Mesh Networks-on-chip

There are ample opportunities at both design and manufacturing phases to meddle in a many-core chip system, especially its underlining communication fabric, known as the networks-on-chip (NoC), through the inclusion of malicious hardware Trojans (HT). In this paper, we focus on studying two specific HT-assisted Denial-of-Service (DoS) attacks, namely the sinkhole and blackhole attacks, that directly target the NoC of a many-core chip. As of the blackhole attacks, those intermediate routers with inserted HTs can stop forwarding data packets/flits towards the packets’ destination; instead, packets are either dropped from the network or diverted to some other malicious nodes. Sinkhole attacks, which exhibit similar attack effects as blackhole attacks, can occur when the NoC supports adaptive routing. In this case, a malicious node actively solicits packets from its neighbor nodes by pretending to have sufficient free buffer slots. Effects and efficiencies of both sinkhole and blackhole DoS attacks are modeled and quantified in this paper, and a few factors that influence attack effects are found to be critical. Through fine-tuning of these parameters, both attacks are shown to cause more damages to the NoC, measured as over 30% increase in packet loss rate. Even with current detection and defense methods in place, the packet loss rate is still remarkably high, suggesting the need of new and more effective detection and defense methods against the enhanced blackhole and sinkhole attacks as described in the paper

A Mechanical Mass Sensor with Yoctogram Resolution

Nanoelectromechanical systems (NEMS) have generated considerable interest as inertial mass sensors. NEMS resonators have been used to weigh cells, biomolecules, and gas molecules, creating many new possibilities for biological and chemical analysis [1-4]. Recently, NEMS-based mass sensors have been employed as a new tool in surface science in order to study e.g. the phase transitions or the diffusion of adsorbed atoms on nanoscale objects [5-7]. A key point in all these experiments is the ability to resolve small masses. Here we report on mass sensing experiments with a resolution of 1.7 yg (1 yg = 10^-24 g), which corresponds to the mass of one proton, or one hydrogen atom. The resonator is made of a ~150 nm long carbon nanotube resonator vibrating at nearly 2 GHz. The unprecedented level of sensitivity allows us to detect adsorption events of naphthalene molecules (C10H8) and to measure the binding energy of a Xe atom on the nanotube surface (131 meV). These ultrasensitive nanotube resonators offer new opportunities for mass spectrometry, magnetometry, and adsorption experiments.Comment: submitted version of the manuscrip

Brain Dynamics of Spatial Reference Frame Proclivity in Active Navigation.

Recent research into navigation strategy of different spatial reference frames (self-centered egocentric reference frame and environment-centered allocentric reference frame) has revealed that the parietal cortex plays an important role in processing allocentric information to provide a translation function between egocentric and allocentric spatial reference frames. However, most studies merely focused on a passive experimental environment, which is not truly representative of our daily spatial learning/navigation tasks. This study investigated the factor associated with brain dynamics that causes people to switch their preferred spatial strategy in both active and passive navigations to bridge the gap. Virtual reality (VR) technique and Omni treadmill are applied to realize actively walking for active navigation, and for passive navigation, participants were sitting while conducting the same task. Electroencephalography (EEG) signals were recorded to monitor spectral perturbations on transitions between egocentric and allocentric frames during a path integration task. Forty-one right-handed male participants from authors' university participated this study. Our brain dynamics results showed navigation involved areas including the parietal cortex with modulation in the alpha band, the occipital cortex with beta and low gamma band perturbations, and the frontal cortex with theta perturbation. Differences were found between two different turning-angle paths in the alpha band in parietal cluster event-related spectral perturbations (ERSPs). In small turning-angle paths, allocentric participants showed stronger alpha desynchronization than egocentric participants; in large turning-angle paths, participants for two reference frames had a smaller difference in the alpha frequency band. Behavior results of homing errors also corresponded to brain dynamic results, indicating that a larger angle path caused the allocentric to have a higher tendency to become egocentric navigators in the active navigation environment

Job Shop Planning and Scheduling for Manufacturers with Manual Operations

Job shop scheduling systems are widely employed to optimise the efficiency of machine utilisation in the manufacturing industry, by searching the most cost-effective permutation of job operations based on the cost of each operation on each compatible machine and the relations between job operations. Such systems are paralysed when the cost of operations are not predictable led by the involvement of complex manual operations. This paper proposes a new genetic algorithm-based job shop scheduling system by integrating a fuzzy learning and inference sub-system in an effort to address this limitation. In particular, the fuzzy sub-system adaptively estimates the completion time and thus cost of each manual task under different conditions based on a knowledge base which is initialised by domain experts and then constantly updated based on its built-in learning ability and adaptability. The manufacturer of Point of Sale and Point of Purchase products is taken in this paper as an example case for both theoretical discussion and experimental study. The experimental results demonstrate the promising of the proposed system in improving the efficiency of manual manufacturing operations

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

As an effective tool, roadside digital billboard advertising is widely used to attract potential customers (e.g., drivers and passengers passing by the billboards) to obtain commercial profit for the advertiser, i.e., the attracted customers’ payment. The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers’ implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three realworld datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies

Cataract surgery in uveitis: a multicentre database study

BACKGROUND/AIMS: Cataract is an important source of visual loss in patients with uveitis. Whether or not outcomes of cataract surgery in eyes with uveitis are worse compared with non-uveitic eyes have to date been compromised by lack of reliable estimates of benefit and harm, which require data from large cohorts. METHODS: Electronic medical record data were extracted from eight independent UK clinical sites for eyes undergoing cataract extraction between January 2010 and December 2014. 1173 eyes with a recorded diagnosis of uveitis were compared with a reference group of 95 573 eyes from the same dataset. RESULTS: Uveitic eyes represented 1.2% of all eyes undergoing cataract surgery. Eyes in the uveitic group had worse preoperative visual acuity (0.87 vs 0.65 logarithm of the minimum angle of resolution (logMAR) units), were from younger patients and had shorter axial lengths and a higher incidence of ocular copathology including glaucoma. A greater number had documented small pupils, required additional surgical procedures, developed more intraoperative complications and had poorer postoperative visual acuity at all time points measured up to 6 months (0.41 vs 0.27 logMAR units at 12-24 weeks). CONCLUSIONS: This large study cohort of eyes with a diagnosis of uveitis undergoing cataract surgery highlights more precisely the complex surgical demands, copathology and worse visual outcomes in this group. These data will allow more accurate preoperative counselling and planning. Although improvement in visual acuity is achieved in most cases, prognosis should be guarded, so that patient expectations are met. Compared with the non-uveitic population, the mean postoperative visual acuity is between one and two lines worse at all time points

Fingerprint Recognition with Identical Twin Fingerprints

Fingerprint recognition with identical twins is a challenging task due to the closest genetics-based relationship existing in the identical twins. Several pioneers have analyzed the similarity between twins' fingerprints. In this work we continue to investigate the topic of the similarity of identical twin fingerprints. Our study was tested based on a large identical twin fingerprint database that contains 83 twin pairs, 4 fingers per individual and six impressions per finger: 3984 (83*2*4*6) images. Compared to the previous work, our contributions are summarized as follows: (1) Two state-of-the-art fingerprint identification methods: P071 and VeriFinger 6.1 were used, rather than one fingerprint identification method in previous studies. (2) Six impressions per finger were captured, rather than just one impression, which makes the genuine distribution of matching scores more realistic. (3) A larger sample (83 pairs) was collected. (4) A novel statistical analysis, which aims at showing the probability distribution of the fingerprint types for the corresponding fingers of identical twins which have same fingerprint type, has been conducted. (5) A novel analysis, which aims at showing which finger from identical twins has higher probability of having same fingerprint type, has been conducted. Our results showed that: (a) A state-of-the-art automatic fingerprint verification system can distinguish identical twins without drastic degradation in performance. (b) The chance that the fingerprints have the same type from identical twins is 0.7440, comparing to 0.3215 from non-identical twins. (c) For the corresponding fingers of identical twins which have same fingerprint type, the probability distribution of five major fingerprint types is similar to the probability distribution for all the fingers' fingerprint type. (d) For each of four fingers of identical twins, the probability of having same fingerprint type is similar

Improved defect detection using adaptive leaky NLMS filter in guided-wave testing of pipelines

Ultrasonic guided wave (UGW) testing of pipelines allows long range assessments of pipe integrity from a single point of inspection. This technology uses a number of arrays of transducers, linearly placed apart from each other to generate a single axisymmetric wave mode. The general propagation routine of the device results in a single time domain signal, which is then used by the inspectors to detect the axisymmetric wave for any defect location. Nonetheless, due to inherited characteristics of the UGW and non-ideal testing conditions, non-axisymmetric (flexural) waves will be transmitted and received in the tests. This adds to the complexity of results’ interpretation. In this paper, we implement an adaptive leaky normalized least mean square (NLMS) filter for reducing the effect of non-axisymmetric waves and enhancement of axisymmetric waves. In this approach, no modification in the device hardware is required. This method is validated using the synthesized signal generated by a finite element model (FEM) and real test data gathered from laboratory trials. In laboratory trials, six different sizes of defects with cross-sectional area (CSA) material loss of 8% to 3% (steps of 1%) were tested. To find the optimum frequency, several excitation frequencies in the region of 30–50 kHz (steps of 2 kHz) were used. Furthermore, two sets of parameters were used for the adaptive filter wherein the first set of tests the optimum parameters were set to the FEM test case and, in the second set of tests, the data from the pipe with 4% CSA defect was used. The results demonstrated the capability of this algorithm for enhancing a defect’s signal-to-noise ratio (SNR).NSIRC and Brunel Universit