A vision chip with complementary pathways for open-world sensing.

Image sensors face substantial challenges when dealing with dynamic, diverse and unpredictable scenes in open-world applications. However, the development of image sensors towards high speed, high resolution, large dynamic range and high precision is limited by power and bandwidth. Here we present a complementary sensing paradigm inspired by the human visual system that involves parsing visual information into primitive-based representations and assembling these primitives to form two complementary vision pathways: a cognition-oriented pathway for accurate cognition and an action-oriented pathway for rapid response. To realize this paradigm, a vision chip called Tianmouc is developed, incorporating a hybrid pixel array and a parallel-and-heterogeneous readout architecture. Leveraging the characteristics of the complementary vision pathway, Tianmouc achieves high-speed sensing of up to 10,000 fps, a dynamic range of 130 dB and an advanced figure of merit in terms of spatial resolution, speed and dynamic range. Furthermore, it adaptively reduces bandwidth by 90%. We demonstrate the integration of a Tianmouc chip into an autonomous driving system, showcasing its abilities to enable accurate, fast and robust perception, even in challenging corner cases on open roads. The primitive-based complementary sensing paradigm helps in overcoming fundamental limitations in developing vision systems for diverse open-world applications.

Bioaccumulation and human health risk assessment of trace metals in the freshwater mussel Cristaria plicata in Dongting Lake, China.

Trace metal contamination in water and bioaccumulation in aquatic organisms are human health risks of increasing concern. However, the bioaccumulation of trace metals in the organs of the mussel Cristaria plicata in Dongting Lake, China and the human health risks of mussel consumption are largely unknown. We investigated the concentrations of 15 trace metals and metalloids in surface water, sediments, and C. plicata organs (foot, gill, mantle, and visceral mass) and quantified the bioaccumulation and human health risk of these trace metals in specimens collected from Dongting Lake. The concentrations of most metals in surface water exceeded previously published background values. In contrast, the concentrations of most metals in sediments showed a decreasing trend. Overall, the metal concentrations in the gill and visceral masses of C. plicata were higher than those in the foot and mantle, and higher bioaccumulation capacities were observed for essential metals than for nonessential metals. The mean concentrations of the trace elements Zn, Pb, Cd, As, Cu, and Cr in C. plicata foot samples were lower than the threshold values established by international and Chinese organizations. The estimated daily intake (EDI) values of the essential metal Mn in C. plicata foot was higher than the recommended tolerable daily intake (TDI) values for juveniles. Only Mn for juveniles and As for both juveniles and adults may pose noncarcinogenic health risks through foot consumption. The hazard index (HI) values for adults and juveniles were higher than 1, suggesting significant risks of noncarcinogenic effects to humans by exposure to multiple metals.

Implementation of High-Performance Blockchain Network Based on Cross-Chain Technology for IoT Applications.

With the development of technology, the network structure has changed a lot. Many people regard the Internet of Things as the next-generation network structure, which means all the embedded devices can communicate with each other directly. However, some problems remain in IoT before it can be applied in a large scale. Blockchain, which has become a hot research topic in recent years, may be one of the solutions. However, currently, the transaction speed of blockchain is still a disadvantage compared to traditional transaction methods. This paper focuses on to implement a high-performance blockchain platform. After investigation of the current blockchain consensus algorithm and blockchain architecture, we propose: (1) an improved blockchain consensus algorithm, which is implemented based on the mortgage model instead of probability model; (2) a cross-chain protocol with transverse expansion capacity, which would support the message transmission among chains; (3) a high-performance cross-chain blockchain network structure, which could handle more than 1000 transactions per second per chain by verification. Experiments have been carried out, and shown that the cross-chain blockchain network structure we provided is feasible to meet the requirement of large-scale distributed IoT applications.

Brain-inspired multimodal hybrid neural network for robot place recognition.

Place recognition is an essential spatial intelligence capability for robots to understand and navigate the world. However, recognizing places in natural environments remains a challenging task for robots because of resource limitations and changing environments. In contrast, humans and animals can robustly and efficiently recognize hundreds of thousands of places in different conditions. Here, we report a brain-inspired general place recognition system, dubbed NeuroGPR, that enables robots to recognize places by mimicking the neural mechanism of multimodal sensing, encoding, and computing through a continuum of space and time. Our system consists of a multimodal hybrid neural network (MHNN) that encodes and integrates multimodal cues from both conventional and neuromorphic sensors. Specifically, to encode different sensory cues, we built various neural networks of spatial view cells, place cells, head direction cells, and time cells. To integrate these cues, we designed a multiscale liquid state machine that can process and fuse multimodal information effectively and asynchronously using diverse neuronal dynamics and bioinspired inhibitory circuits. We deployed the MHNN on Tianjic, a hybrid neuromorphic chip, and integrated it into a quadruped robot. Our results show that NeuroGPR achieves better performance compared with conventional and existing biologically inspired approaches, exhibiting robustness to diverse environmental uncertainty, including perceptual aliasing, motion blur, light, or weather changes. Running NeuroGPR as an overall multi-neural network workload on Tianjic showcases its advantages with 10.5 times lower latency and 43.6% lower power consumption than the commonly used mobile robot processor Jetson Xavier NX.

Tissue-specific distribution and bioaccumulation pattern of trace metals in fish species from the heavily sediment-laden Yellow River, China.

The Yellow River is one of the largest contributors to the global riverine sediment flux from the land to the ocean. Tissue-specific bioaccumulation of trace metals in fish from heavily sediment-laden rivers remains unclear to date. The concentrations and distributions of trace metals in water, suspended matters, sediments, and various fish tissues were investigated in the mainstem of the Yellow River were investigated. The concentrations of most metals in abiotic media were high in the Gan-Ning-Meng of upstream and downstream segments, and were highest in fine-sized suspended matters. The highest concentrations of most metals were in the gill and liver, followed by the gonad, and lowest in the muscle, and there were a significant overall differences among the tissues. The concentrations of metals in some tissues (e.g., muscle and gill) significantly differed among regions and feeding habits. The highest values of the bioaccumulation factor for suspended matters (BFSPM) were observed in the midstream region (e.g., reaching to 19.0 for Se in the liver). This was determined by metal type and tissue specificity, food composition, and concentration of metals in abiotic media. The results highlight the significance of suspended matters for the distribution of trace metals in abiotic and biotic media.

[Water Environmental Characteristics and Water Quality Assessment of Lakes in Tibetan Plateau].

Lakes are an important water resource and biological habitat in the Tibetan Plateau. Owing to the combined influence of climate, topography, and other natural factors as well as human factors, the water environment of the lakes on the Tibetan Plateau is facing more and more severe problems and challenges. To clarify the present status, distribution pattern, main characteristic factors of water quality, and important factors affecting the water quality of lakes on the Tibetan Plateau, the water environment of 12 typical lakes on the Tibet Plateau was investigated in summer (July-August) and autumn (October-November) in 2020. The field sampling and laboratory test data comprehensive analysis showed that:① several physical and chemical parameters of typical lakes on the Tibetan Plateau differed in spatiotemporal distribution. ② Salinity was the main characteristic of water quality in the typical lakes on the Tibetan Plateau. ③ The spatiotemporal distribution of lake eutrophication index showed little diversity and basically ranged from poor nutrition to moderate nutrition. The spatial and temporal distributions in the lake water quality index (WQI) were significantly different. The lake WQI grade decreased from "Moderate" to "Very poor" with the increase in salinity area, and the lake water quality in autumn was better than that in summer. ④ The spatiotemporal differences in lake water quality on the Tibetan Plateau were mainly controlled by precipitation, evapoconcentration, and human activities. This study will provide scientific basis for water environment protection and improvement of water ecosystems on the Tibetan Plateau.

Preparation and use of wireless reprogrammable multilateral optogenetic devices for behavioral neuroscience.

Wireless battery-free optogenetic devices enable behavioral neuroscience studies in groups of animals with minimal interference to natural behavior. Real-time independent control of optogenetic stimulation through near-field communication dramatically expands the realm of applications of these devices in broad contexts of neuroscience research. Dissemination of these tools with advanced functionalities to the neuroscience community requires protocols for device manufacturing and experimental implementation. This protocol describes detailed procedures for fabrication, encapsulation and implantation of recently developed advanced wireless devices in head- and back-mounted forms. In addition, procedures for standard implementation of experimental systems in mice are provided. This protocol aims to facilitate the application of wireless optogenetic devices in advanced optogenetic experiments involving groups of freely moving rodents and complex environmental designs. The entire protocol lasts ~3-5 weeks.

Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots.

Recent advances in artificial intelligence have enhanced the abilities of mobile robots in dealing with complex and dynamic scenarios. However, to enable computationally intensive algorithms to be executed locally in multitask robots with low latency and high efficiency, innovations in computing hardware are required. Here, we report TianjicX, a neuromorphic computing hardware that can support true concurrent execution of multiple cross-computing-paradigm neural network (NN) models with various coordination manners for robotics. With spatiotemporal elasticity, TianjicX can support adaptive allocation of computing resources and scheduling of execution time for each task. Key to this approach is a high-level model, "Rivulet," which bridges the gap between robotic-level requirements and hardware implementations. It abstracts the execution of NN tasks through distribution of static data and streaming of dynamic data to form the basic activity context, adopts time and space slices to achieve elastic resource allocation for each activity, and performs configurable hybrid synchronous-asynchronous grouping. Thereby, Rivulet is capable of supporting independent and interactive execution. Building on Rivulet with hardware design for realizing spatiotemporal elasticity, a 28-nanometer TianjicX neuromorphic chip with event-driven, high parallelism, low latency, and low power was developed. Using a single TianjicX chip and a specially developed compiler stack, we built a multi-intelligent-tasking mobile robot, Tianjicat, to perform a cat-and-mouse game. Multiple tasks, including sound recognition and tracking, object recognition, obstacle avoidance, and decision-making, can be concurrently executed. Compared with NVIDIA Jetson TX2, latency is substantially reduced by 79.09 times, and dynamic power is reduced by 50.66%.

Wireless multilateral devices for optogenetic studies of individual and social behaviors.

Advanced technologies for controlled delivery of light to targeted locations in biological tissues are essential to neuroscience research that applies optogenetics in animal models. Fully implantable, miniaturized devices with wireless control and power-harvesting strategies offer an appealing set of attributes in this context, particularly for studies that are incompatible with conventional fiber-optic approaches or battery-powered head stages. Limited programmable control and narrow options in illumination profiles constrain the use of existing devices. The results reported here overcome these drawbacks via two platforms, both with real-time user programmability over multiple independent light sources, in head-mounted and back-mounted designs. Engineering studies of the optoelectronic and thermal properties of these systems define their capabilities and key design considerations. Neuroscience applications demonstrate that induction of interbrain neuronal synchrony in the medial prefrontal cortex shapes social interaction within groups of mice, highlighting the power of real-time subject-specific programmability of the wireless optogenetic platforms introduced here.

Inverse Design Strategies for 3D Surfaces Formed by Mechanically Guided Assembly.

Deterministic transformations of 2D patterns of materials into well-controlled 3D mesostructures serve as the basis for manufacturing methods that can bypass limitations of conventional 3D micro/nanofabrication. Here, guided mechanical buckling processes provide access to a rich range of complex 3D mesostructures in high-performance materials, from inorganic and organic semiconductors, metals and dielectrics, to ceramics and even 2D materials (e.g., graphene, MoS2 ). Previous studies demonstrate that iterative computational procedures can define design parameters for certain targeted 3D configurations, but without the ability to address complex shapes. A technical need is in efficient, generalized inverse design algorithms that directly yield sets of optimized parameters. Here, such schemes are introduced, where the distributions of thicknesses across arrays of separated or interconnected ribbons provide scalable routes to 3D surfaces with a broad range of targeted shapes. Specifically, discretizing desired shapes into 2D ribbon components allows for analytic solutions to the inverse design of centrally symmetric and even general surfaces, in an approximate manner. Combined theoretical, numerical, and experimental studies of ≈20 different 3D structures with characteristic sizes (e.g., ribbon width) ranging from ≈200 µm to ≈2 cm and with geometries that resemble hemispheres, fire balloons, flowers, concave lenses, saddle surfaces, waterdrops, and rodents, illustrate the essential ideas.

A wireless, implantable optoelectrochemical probe for optogenetic stimulation and dopamine detection.

Physical and chemical technologies have been continuously progressing advances in neuroscience research. The development of research tools for closed-loop control and monitoring neural activities in behaving animals is highly desirable. In this paper, we introduce a wirelessly operated, miniaturized microprobe system for optical interrogation and neurochemical sensing in the deep brain. Via epitaxial liftoff and transfer printing, microscale light-emitting diodes (micro-LEDs) as light sources and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS)-coated diamond films as electrochemical sensors are vertically assembled to form implantable optoelectrochemical probes for real-time optogenetic stimulation and dopamine detection capabilities. A customized, lightweight circuit module is employed for untethered, remote signal control, and data acquisition. After the probe is injected into the ventral tegmental area (VTA) of freely behaving mice, in vivo experiments clearly demonstrate the utilities of the multifunctional optoelectrochemical microprobe system for optogenetic interference of place preferences and detection of dopamine release. The presented options for material and device integrations provide a practical route to simultaneous optical control and electrochemical sensing of complex nervous systems.

The Design and Implementation of Cardiotocography Signals Classification Algorithm Based on Neural Network.

Mobile medical care is a hot issue in current medical research. Due to the inconvenience of going to hospital for fetal heart monitoring and the limited medical resources, real-time monitoring of fetal health on portable devices has become an urgent need for pregnant women, which helps to protect the health of the fetus in a more comprehensive manner and reduce the workload of doctors. For the feature acquisition of the fetal heart rate (FHR) signal, the traditional feature-based classification methods need to manually read the morphological features from the FHR curve, which is time-consuming and costly and has a certain degree of calibration bias. This paper proposes a classification method of the FHR signal based on neural networks, which can avoid manual feature acquisition and reduce the error caused by human factors. The algorithm will directly learn from the FHR data and truly realize the real-time diagnosis of FHR data. The convolution neural network classification method named "MKNet" and recurrent neural network named "MKRNN" are designed. The main contents of this paper include the preprocessing of the FHR signal, the training of the classification model, and the experiment evaluation. Finally, MKNet is proved to be the best algorithm for real-time FHR signal classification.

Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research.

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction.

A novel sensor based on electrochemical polymerization of diglycolic acid for determination of acetaminophen.

Diglycolic acid (DA) polymer was coated on glassy carbon (GC) electrode by cyclic voltammetry (CV) technique for the first time. The electrochemical performances of the modified electrode were investigated by CV and electrochemical impedance (EIS). The obtained electrode showed an excellent electrocatalytic activity for the oxidation of acetaminophen (ACOP). A couple of well-defined reversible electrochemical redox peaks were observed on the ploy(DA)/GC electrode in ACOP solution. Compared with bare GC electrode, the oxidation peak potential of ACOP on ploy(DA)/GC electrode moved from 0.289 V to 0.220 V. Meanwhile, the oxidation peak current was much higher on the modified electrode than that on the bare GC electrode, indicating DA polymer modified electrode possessed excellent performance for the oxidation of ACOP. This kind of capability of the modified electrode can be enlisted for the highly sensitive and selective determination of ACOP. Under the optimized conditions, a wide linear range from 2 × 10(-8) to 5.0 × 10(-4)M with a correlation coefficient 0.9995 was obtained. The detection limit was 6.7 × 10(-9)M (at the ratio of signal to noise, S/N=3:1). The modified electrode also exhibited very good stability and reproducibility for the detection of ACOP. The established method was applied to the determination of ACOP in samples. An average recovery of 100.1% was achieved. These results indicated that this method was reliable for determining ACOP.