Piezoelectric nanogenerators (PENGs) provide a viable solution to convert the mechanical energy generated by body movement to electricity. One-dimensional yarns offer a platform for flexible wearable textile PENGs, which can conform to body for comfort and efficient energy harvesting. In this context, we report a flexible piezoelectric yarn, assembled by one-step cocentric deposition of cesium lead halide perovskite decorated polyvinylidene fluoride (PVDF) nanofibers, on a stainless-steel yarn. Perovskite crystals were formed in situ during electrospinning. Our work demonstrates a nanofiber morphology in which perovskite crystals spread over the nanofiber, leading to a rough surface, and complementing piezoelectric nanocomposite formation with PVDF for superior stress excitation. We investigated how the halide anions of perovskite affect the piezoelectric performance of PENG yarns by comparing CsPbBr 3 and CsPbI 2 Br. Effects of the perovskite concentration, annealing temperature, and deposition time on the piezoelectric properties of PENG yarns were investigated. Devices assembled with a single yarn of CsPbI 2 Br decorated PVDF nanofibers yield the optimal performance with an output voltage of 8.3 V and current of 1.91 μA in response to pressing from an actuator and used to charge capacitors for powering electronics. After aging in the ambient environment for 3 months, the device maintained its performance during 19,200 cycles of mechanical stresses. The excellent and stable electrical performance can be ascribed to the optimized crystallization of CsPbI 2 Br crystals, their complementing performance with PVDF, and formation of nanofibers with uniformity and strength. The flexibility of piezoelectric yarns enables them to be bent, twisted, braided, and woven for different textile integrations while harvesting energy from body movements, demonstrating the potential for wearable mechanical energy harvesting.

Millions of people around the world currently suffer from kidney stone diseases. While ureteral stenting is an unmistakably effective treatment of these patients, their long‐term adverse effects can result in the build‐up of crystals around the stent. This, in turn, can lead to new ureter blockages that can dangerously increase kidney pressure, a condition known as hydronephrosis, which, if severe and prolonged, can cause irreversible kidney damage. Toward enabling early detection of hydronephrosis, this paper investigates the first intelligent ureteral stent with an integrated radiofrequency antenna and micro pressure sensor for resonance‐based wireless tracking of kidney pressure. Prototyping is conducted using a commercial ureteral stent as the substrate for microfabrication of the device. The packaged device is experimentally assessed for electrical characterizations and wireless pressure sensing using an in vitro test model. Preliminary telemetry testing demonstrates the fundamental ability of the device with its approximately linear responses of up to 1.7 kHz mmHg−1 over a pressure range of up to 120 mmHg in air, water, and artificial urine. These findings verify the efficacy of the device design and the approach to kidney pressure monitoring through indwelling stents, paving the way for the transfer of this technology to today's ureteral stent products.

The backscatter signal analysis, as the landmine material could vary, has to be as much advanced as possible. One major problem with the conventional methods is that they are not able to detect new plastic landmines. In the recent research, the classification techniques and neural networks (NNs) were exploited for detection. In NNs-based method, a network is trained based on the feature extracted from the data, which leads to landmine detection. Other conventional classification methods, attempts to classify the objects sharing common characteristics. In this letter, an algorithm is introduced based on classification, data reduction and neural networks. Indeed, this algorithm employs neural network and classification method, simultaneously. The simple methods using either neural network or classification separately usually suffer from high rate of risk. In this letter, a novel classifier is proposed such that the data is classified based on similarity. It will be shown that the similarity between signals in a class is more than 90%, which proves the method's efficiency. Moreover, the scattering parameter, having magnitude and phase parts, is used to create an algorithm with parallel process.

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