Ring artifacts in CT are caused by uncalibrated variations in detector pixels and are especially prevalent with emerging photon counting detectors (PCDs). Control of ring artifacts is conventionally accomplished by improving either hardware manufacturing or software correction algorithms. An alternative solution is detector autocalibration, in which two redundant samples of each line integral are acquired and used to dynamically calibrate the PCD. Autocalibration was first proposed by Hounsfield in 1977 and was demonstrated on the EMI Topaz prototype scanner in 1980, but details surrounding this implementation are sparse. We investigate a form of autocalibration that requires just two redundant acquisitions, which could be acquired using flying focal spot on a clinical scanner but is demonstrated here with a detector shift. We formulated autocalibration as an optimization problem to determine the relative gain factor of each pixel and tested it on scans of a chicken thigh specimen, resolution phantom, and a cylindrical phantom. Ring artifacts were significantly reduced. Some residual artifacts remained but could not be discriminated from the intrinsic temporal instability of our PCD modules. Autocalibration could facilitate the adoption of widespread photon counting CT by reducing ring artifacts, thermal management requirements, or stability requirements that are present today. Demonstration of autocalibration on a rotating gantry with flying focal spot remains future work.

The large-scale retirement of electric vehicle traction batteries poses a huge challenge to environmental protection and resource recovery since the batteries are usually replaced well before their end of life. Direct disposal or material recycling of retired batteries does not achieve their maximum economic value. Thus, the second-life use of EV batteries has become the most economical and environmentally friendly solution. However, there are still many issues facing second-life batteries (SLBs). To better understand the current research status, this article reviews the research progress of second-life lithium-ion batteries for stationary energy storage applications, including battery aging mechanisms, repurposing, modeling, battery management, and optimal sizing. Energy management strategies are reviewed to maximize the economic benefits for SLBs, and the less-demanding applications of SLBs are presented. The technical challenges and future development trends of battery reusing technologies are also discussed. Finally, the conclusions and relevant recommendations for future studies are summarized.

We propose a complete hardware-based architecture of multilayer neural networks (MNNs), including electronic synapses, neurons, and periphery circuitry to implement supervised learning (SL) algorithm of extended remote supervised method (ReSuMe). In this system, complementary (a pair of n- and p-type) memtransistors (C-MTs) are used as an electrical synapse. By applying the learning rule of spike-timing-dependent plasticity (STDP) to the memtransistor connecting presynaptic neuron to the output one whereas the contrary anti-STDP rule to the other memtransistor connecting presynaptic neuron to the teacher one, extended ReSuMe with multiple layers is realized without the usage of those complicated supervising modules in previous approaches. In this way, both the C-MT-based chip area and power consumption of the learning circuit for weight updating operation are drastically decreased comparing with the conventional single memtransistor (S-MT)-based designs. Two typical benchmarks, the linearly nonseparable benchmark XOR problem and Mixed National Institute of Standards and Technology database (MNIST) recognition have been successfully tackled using the proposed MNN system while impact of the nonideal factors of realistic devices has been evaluated.