摘要：In clinical magnetic resonance imaging （MRI）, the design of the radiofrequency （RF） coil is very important. For certain applications, the appropriate coil can produce an improved image quality. However, it is difficult to achieve a uniform B1 field and a high signal-to-noise ratio （SNR） simultaneously. In this article, we design an interventional transmitter-and-receiver RF coil for cerebral surgery. This coil adopts a disassembly structure that can be assembled and disassembled repeatedly on the cerebral surgery gantry to reduce the amount of interference from the MRI during surgery. The simulation results and the imaging experiments demonstrate that this coil can produce a uniform RF field, a high SNR, and a large imaging range to meet the requirements of the cerebral surgery.

摘要：As the huge computation and easily trapped local optimum in remanufacturing closed-loop supply chain network (RCSCN) design considered, a genetic particle swarm optimization algorithm was proposed. The total cost of closed-loop supply chain was selected as fitness function, and a unique and tidy coding mode was adopted in the proposed algorithm. Then, some mutation and crossover operators were introduced to achieve discrete optimization of RCSCN structure. The simulation results show that the proposed algorithm can gain global optimal solution with good convergent performance and rapidity. The computing speed is only 22.16 s, which is shorter than those of the other optimization algorithms.

摘要：Lorenz systems family unifying Lorenz system, Chen system and Lü system is a typical chaotic family. In this paper, we consider impulsive control Lorenz chaotic systems family with time-varying impulse intervals. By establishing an effective tool of a set of inequalities, we analyze the asymptotic stability of impulsive control Lorenz systems family and obtain some new less conservative conditions. Based on the stability analysis, we design a novel impulsive controller with time-varying impulse intervals. Illustrative examples are provided to show the feasibility and effectiveness of our method. The obtained results not only can be used to design impulsive control for Lorenz systems family, but also can be extended to other chaotic systems.

摘要：Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention ***,with the increasing volume of visual-language pre-training model parameters,direct transfer learning consumes a substantial amount of computational and storage ***,recently proposed parameter-efficient transfer learning methods mainly focus on the reconstruction of channel features,ignoring the spatial features which are vital for modeling key entity *** address these issues,we design an efficient transfer learning framework for RSCIR,which is based on spatial feature efficient reconstruction(SPER).A concise and efficient spatial adapter is introduced to enhance the extraction of spatial *** spatial adapter is able to spatially reconstruct the features in the backbone with few parameters while incorporating the prior information from the channel *** conduct quantitative and qualitative experiments on two different commonly used RSCIR *** with traditional methods,our approach achieves an improvement of 3%-11% in sumR *** with methods finetuning all parameters,our proposed method only trains less than 1% of the parameters,while maintaining an overall performance of about 96%.

摘要：To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy(CE) algorithm is proposed by combining fuzzy c-mean clustering algorithm with traditional cross entropy algorithm, and specific program flow of the algorithm is *** the algorithm, large thinned array(200 elements) given sidelobe level(-10,-19 and-30 d B) problem is solved successfully. Compared with the traditional statistical algorithms, the optimization results of the algorithm validate that the number of feed array elements reduces by 51%, 11% and 6% respectively. In addition, compared with the particle swarm optimization(PSO) algorithm, the number of feed array elements from the algorithm is more similar, but the algorithm is more efficient.

摘要：In the era of Big Data, typical architecture of distributed real-time stream processing systems is the combination of Flume, Kafka, and Storm. As a kind of distributed message system, Kafka has the characteristics of horizontal scalability and high throughput, which is manly deployed in many areas in order to address the problem of speed mismatch between message producers and consumers. When using Kafka, we need to quickly receive data sent by producers. In addition, we need to send data to consumers quickly. Therefore, the performance of Kafka is of critical importance to the performance of the whole stream processing system. In this paper, we propose the improved design of real-time stream processing systems, and focus on improving the Kafka's data loading *** use Kafka cat to transfer data from the source to Kafka topic directly, which can reduce the network transmission. We also utilize the memory file system to accelerate the process of data loading, which can address the bottleneck and performance problems caused by disk I/O. Extensive experiments are conducted to evaluate the performance, which show the superiority of our improved design.

摘要：This paper optimizes the buried channel charge-coupled device(BCCD) structure fabricated by complementary metal oxide semiconductor(CMOS) technology. The optimized BCCD has advantages of low noise, high integration and high image quality. The charge transfer process shows that interface traps, weak fringing fields and potential well between adjacent gates all cause the decrease of charge transfer efficiency(CTE). CTE and well capacity are simulated with different operating voltages and gap sizes. CTE can achieve 99.999% and the well capacity reaches up to 25 000 electrons for the gap size of 130 nm and the maximum operating voltage of 3 V.

摘要：Sweat could be a carrier of informative biomarkers for health status identification;therefore,wearable sweat sensors have attracted significant attention for *** external power source is an important component of wearable sensors,however,the current power supplies,i.e.,batteries,limit further shrinking down the size of these devices and thus limit their application areas and ***,we report a stretchable self-powered biosensor with epidermal electronic format that enables the in situ detec-tion of lactate and glucose concentration in *** biofuel cells serve as self-powered sensing modules allowing the sweat sensor to exhibit a determination coefficient(R2)of 0.98 with a sensitivity of 2.48 mV/mM for lactate detection,and R2 of 0.96 with a sensitivity of 0.11 mV/μM for glucose *** microfluidic channels developed in an ultra-thin soft flexible polydimethylsiloxane layer not only enable the effective collection of sweat,but also provide excellent mechanical properties with stable performance output even under 30%*** presented soft sweat sensors can be integrated at nearly any location of the body for the continuous monitoring of lactate and glucose changes during normal daily activities such as *** results provide a promising approach to develop next-generation sweat sensors for real-time and in situ sweat analysis.

摘要：In order to take into account the computing efficiency and flexibility of calculating transcendental functions, this paper proposes one kind of reconfigurable transcendental function generator. The generator is of a reconfigurable array structure composed of 30 processing elements （PEs）. The coordinate rotational digital computer （CORDIC） algorithm is implemented on this structure. Different functions, such as sine, cosine, inverse tangent, logarithmic, etc., can be calculated based on the structure by reconfiguring the functions of PEs. The functional simulation and field programmable gate array （FPGA） verification show that the proposed method obtains great flexibility with acceptable performance.

摘要：This paper puts forward a novel cognitive cross-layer design algorithms for multihop wireless networks optimization across physical,mediam access control （MAC）,network and transport *** is well known,the conventional layered-protocol architecture can not provide optimal performance for wireless networks,and cross-layer design is becoming increasingly important for improving the performance of wireless *** this study,we formulate a specific network utility maximization （NUM） problem that we believe is appropriate for multihop wireless *** using the dual algorithm,the NUM problem has been optimal decomposed and solved with a novel distributed cross-layer design algorithm from physical to transport *** solution enjoys the benefits of cross-layer optimization while maintaining the simplicity and modularity of the traditional layered *** proposed cross-layer design can guarantee the end-to-end goals of data flows while fully utilizing network *** simulations have evaluated an enhanced performance of the proposed algorithm at both average source rate and network ***,the proposed algorithm has low implementation complexity for practical reality.