The transition of bacteria from an individualistic to a biofilm lifestyle profoundly alters their biology. During biofilm development, the bacterial cell-cell adhesions are a major determinant of initial microcolonies, which serve as kernels for the subsequent microscopic and mesoscopic structure of the biofilm, and determine the resulting functionality. In this study, the significance of bacterial cell-cell adhesion dynamics on bacterial aggregation and biofilm maturation is elucidated. Using photoswitchable adhesins between bacteria, modifying the dynamics of bacterial cell-cell adhesions with periodic dark-light cycles is systematic. Dynamic cell-cell adhesions with liquid-like behavior improve bacterial aggregation and produce more compact microcolonies than static adhesions with solid-like behavior in both experiments and individual-based simulations. Consequently, dynamic cell-cell adhesions give rise to earlier quorum sensing activation, better intermixing of different bacterial populations, improved biofilm maturation, changes in the growth of cocultures, and higher yields in fermentation. The here presented approach of tuning bacterial cell-cell adhesion dynamics opens the door for regulating the structure and function of biofilms and cocultures with potential biotechnological applications.

@en

Body Channel Communication (BCC) offers a low-loss signal transmission medium for ultra-low-power wearable devices on human body [1]. However, the effective communication range on human body is limited to less than 1m in the state-of-the-art BCC transceivers [2], where the signal loss at the interface of body surface and BCC receiver remains to be one of the main bottlenecks. The limited communication range has blocked the popularization in many WBAN applications, such as signal transmission from to an intelligent insole to smart watch [3]. Relative to the high impedance of human body [4], the lower input impedance of BCC receiver induces significant signal loss. To reduce the interface loss, conventional interface front end (IFE) of BCC receivers was designed to be with a high input impedance [5], but the DC voltage bias decreased both the input impedance and signal gain of IFE.@en

The size of wireless systems is required to be reduced in many applications, such as ultra-low-power sensor nodes and wearable/implantable devices, where battery and crystal are the two main bottlenecks in system miniaturization. In recent years, battery-free radios based on wireless power transfer (WPT) have shown great potential in miniature wireless systems, while a reliable on-chip clock without a crystal remains a design challenge. Conventional methods utilized the RF WPT tone as the reference for clock generation, but the high RF frequency leads to high power consumption. In comparison, using a lower WPT frequency results in an antenna with a larger size. In this work, the 2nd-order inter-modulation (IM2) component of the two RF WPT tones is extracted to lock an on-chip oscillator, providing a low-jitter PVT-robust clock. In this way, the wireless systems can benefit from: 1) The clock recovery circuits operate at a low IM2 frequency, reducing the power consumption. 2) The WPT can be set to a high RF frequency to minimize the antenna. Fabricated in 65 nm CMOS process, the proposed crystal-less clock generator takes a small area of 0.023 mm2 in a wireless system chip. Measured results show -92 dBc/Hz@10 kHz phase noise and 6.8 μ W power.

@en

It is vital to detect the safety state and identify faults of the battery pack for the safe operation of electric vehicles. The voltage faults such as over-voltage and under-voltage imply more serious battery faults including short-circuit and thermal runaway. The voltage abnormal fluctuation is a warning signal of short-circuit, over-voltage and under-voltage. This paper proposes a scheme of three-layer fault detection method for lithium-ion batteries based on statistical analysis. The first layer fault detection is based on the thresholds of over-charge and over-discharge of a battery pack. In the second layer, confidence interval estimation is applied to identify risky cells. In the third layer, correlation and variability of all cells in one battery pack are analyzed by using an improved K-means method to identify abnormal voltage fluctuation over a certain period. The validity and feasibility of the proposed method are verified by real vehicle data from the National Big Data Alliance of New Energy Vehicles.

@en

It is well known that backstepping method suffer from the problem of "explosion of terms", which is solved by dynamic surface control (DSC) method. This paper presents an improved dynamic surface control (IDSC) method, which constructs the state errors by directly using virtual control signals, rather than using the signals produced by first-order filters in DSC method. The signals produced by first-order filters will only be used to construct the virtual control laws and actual control law. This modification makes the state errors to be more free from the influence of first-order filters. The stability of systems controlled by the proposed IDSC method is proved based on Lyapunov theorem. Finally, the advantage of IDSC method has been shown by simulation results, and it can be seen in the simulation results that IDSC method has better tracking performance and is more stable than DSC method.

@en

This work focuses on adaptive neural dynamic surface control (DSC) for an extended class of nonlinear MIMO strict-feedback systems whose control gain functions are continuous and possibly unbounded. The method is based on introducing a compact set which is eventually proved to be an invariant set: thanks to this set, the restrictive assumption that the upper and lower bounds of control gain functions must be bounded is removed. This method substantially enlarges the class of systems for which DSC can be applied. By utilizing Lyapunov theorem and invariant set theory, it is rigorously proved that all signals in the closed-loop systems are semi-globally uniformly ultimately bounded (SGUUB) and the output tracking errors converge to an arbitrarily small residual set. A simulation example is provided to demonstrate the effectiveness of the proposed approach.

@en

Adaptive slicing is an important computational task required in the layer-based manufacturing process. Its purpose is to find an optimal trade-off between the fabrication time (number of layers) and the surface quality (geometric deviation error). Most of the traditional adaptive slicing algorithms are computationally expensive or only based on local evaluation of errors. To tackle these problems, we introduce a method to efficiently generate slicing plans by a new metric profile that can characterize the distribution of deviation errors along the building direction. By generalizing the conventional error metrics, the proposed metric profile is a density function of deviation errors, which measures the global deviation errors rather than the in-plane local geometry errors used in most prior methods. Slicing can be efficiently evaluated based on metric profiles in contrast to the expensive computation on models in boundary-representation. An efficient algorithm based on dynamic programming is proposed to find the best slicing plan. Our adaptive slicing method can also be applied to models with weighted features and can serve as the inner loop to search the best building direction. The performance of our approach is demonstrated by experimental tests on different examples.

@en