Multidirectional Associative Memory Neural Network Circuit Based on Memristor.

Multidirectional associative memory neural network(MAMNN) is a direct extension of bidirectional associative memory neural network, which can handle multiple associations. In this work, a circuit of MAMNN based on memristor is proposed, which simulates the complex associative memory behavior more in line with the brain mechanism. Firstly, the basic associative memory circuit is designed, which is mainly composed of memristive weight matrix circuit, adder module and activation circuit. It realizes the associative memory function of single-layer neurons input and single-layer neurons output, so that the information can be transmitted unidirectionally between double-layer neurons. Secondly, on this basis, an associative memory circuit with multi-layer neurons input and single-layer neurons output is realized, which makes information transfer unidirectionally between multi-layer neurons. Finally, several identical circuit architectures are extended, and they are combined into a MAMNN circuit through the feedback connection from the output to the input, which realizes the bidirectional transmission of information between multi-layer neurons. Pspice simulation shows that: 1) When single-layer neurons are selected to input data, the circuit can associate data from other multi-layer neurons, realizing one-to-many associative memory function in the brain. 2) When multi-layer neurons are selected to input data, the circuit can associate the target data and realize the many-to-one associative memory function in the brain. The MAMNN circuit is applied to the field of image processing, which can associate and restore damaged binary images, showing strong robustness.

Dynamic analysis and application in medical digital image watermarking of a new multi-scroll neural network with quartic nonlinear memristor.

Memristor is widely used in various neural bionic models because of its excellent characteristics in biological neural activity simulation. In this paper, a piecewise nonlinear function is used to transform the quartic memristor, which is introduced into the ternary Hopfield neural network (HNN) with self-feedback, and a piecewise quartic memristive chaotic neural network model with multi-scroll is constructed. Through simulation analysis, the number of scroll layers changes with memristor parameters and has significant coexistence of multi-scroll attractors and high initial value sensitivity has been found. Using its excellent unpredictability, a digital watermarking algorithm based on wavelet transform is improved and used in the protection of personal medical data. The results show that it not only improves the confidentiality and convenience, but also ensures its robustness and has good encryption effect.

Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking.

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

The GLP-1 analogue exenatide improves hepatic and muscle insulin sensitivity in diabetic rats: tracer studies in the basal state and during hyperinsulinemic-euglycemic clamp.

OBJECTIVE: Glucagon-like peptide-1 (GLP-1) analogues (e.g., exenatide) increase insulin secretion in diabetes but less is known about their effects on glucose production or insulin-stimulated glucose uptake in peripheral tissues. METHODS: Four groups of Sprague-Dawley rats were studied: nondiabetic (control, C); nondiabetic + exenatide (C + E); diabetic (D); diabetic + exenatide (D + E) with diabetes induced by streptozotocin and high fat diet. Infusion of 3-(3)H-glucose and U-(13)C-glycerol was used to measure basal rates of appearance (Ra) of glucose and glycerol and gluconeogenesis from glycerol (GNG). During hyperinsulinemic-euglycemic clamp, glucose uptake into gastrocnemius muscles was measured with 2-deoxy-D-(14)C-glucose. RESULTS: In the diabetic rats, exenatide reduced the basal Ra of glucose (P < 0.01) and glycerol (P < 0.01) and GNG (P < 0.001). During the clamp, Ra of glucose was also reduced, whereas the rate of disappearance of glucose increased and there was increased glucose uptake into muscle (P < 0.01) during the clamp. In the nondiabetic rats, exenatide had no effect. CONCLUSION: In addition to its known effects on insulin secretion, administration of the GLP-1 analogue, exenatide, is associated with increased inhibition of gluconeogenesis and improved glucose uptake into muscle in diabetic rats, implying improved hepatic and peripheral insulin sensitivity.