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A new design methodology is presented for detecting spiking signals from complex brain neural potentials, which applies ultra-low-power technology to implement a nonlinear energy operator (NEO)-based spike detector. The NEO spike detector achieves a differentiator with a differential structure and a multiplier based on the dynamic translinear principle using a sub-threshold technique. As is demonstrated by the simulation results, the proposed circuit has detected the instantaneous energy of the input signals well, which focus in the range 5 Hz to 10 kHz.Introduction: Today, engaging in neuroscience research involves many domains, for example, medicine biology, microelectronics, robotics engineering, etc., among which neural signal recording is the most fundamental. Neuroscientists have implemented the possibility of using brain signals to control artificial devices, that is a prototype of a brain -machine interface (BMI) [1]. For highly-efficient and high-accuracy application of implantable BMI based on wireless transmission of information, real-time spike detection is an important requirement.Several spike detectors have been reported by researchers [2, 3], but the designs are limited to low power, low complexity and high accuracy for multichannel neural signal detection. These spike detectors have to meet several challenging performance requirements imposed by the environments of the applications. First, the detectors on the algorithms implemented in the hardware must be accurate, automatic and computationally simple. Secondly, the detectors should achieve ultra-low-power operation and operate at as low complexity as possible to reduce the size of the implantable chip. Thirdly, programmability of gain and bandwidth is necessary owing to the wide range of the neural harvest signal, since different kinds of neural signal have different signal amplitudes and frequency content.To transmit only the action potential waveforms from the frontreadout array amplifier [4] in real-time, the nonlinear energy operator (NEO) is already used to estimate the instantaneous frequency and amplitude of the signal [5]. It is also used to implement the spike detector. The NEO is defined as:

Increasing workspace and improving dexterity are important tasks for the design of parallel machine tools. The workspace of a crossbar parallel machine tool with constraints is obtained by using a 3D search method based on inverse kinematics. The new Jacobian matrix of the machine is also derived by using the natural coordinate method. Dexterity distribution of the machine tool is obtained on the basis of the workspace and the new Jacobian matrix. Influences of the structural parameters on the workspace volume index (WVI) and global dexterity index (GDI) are analyzed. Structural optimization is conducted by treating the WVI and GDI as the global optimization goals. Unlike the initial data, the optimized results increased by 0.43 and 0.34 times.

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Abstract. The actual movement and the coupled relationship of human fingers are very important for the analysis of human hand grasp, but there are very few effective ways to detect it. The paper introduces a method with wearable measuring sensors to realize the real time measurement and description of human fingers' movement during hand grasping. A data glove for real-time measuring the movement of each joint of all the fingers was designed. The finger movements of three typical human hand grasps were measured, and the coupled relationship and the complete motion trajectory of all five fingers were got and described.