Harmful algal blooms threaten drinking water safety. Although ultrafiltration can effectively retain algae cells and algal-derived pollutants, such as extracellular polymeric substances, it still faces challenges such as severe membrane fouling and insufficient contaminant removal. To address these issues, this study developed an integrated “membrane material–flocculant–magnetic regulation” process to enhance algae-laden water treatment and mitigate fouling. Nanoparticles of sulfonated polydopamine (SPDA)-modified Materials of Institute Lavoisier-101 (MIL-101), denoted SPDA@MIL-101, were utilized to functionalize a polyethersulfone (PES) ultrafiltration membrane, enhancing its hydrophilicity and negative surface charge. Meanwhile, a magnetic flocculant (PCNF), composed of polyferric sulfate (PFS), sodium carboxymethyl cellulose (CMC-Na), and iron(II,III) oxide (Fe₃O₄) nanoparticles, was synthesized. Under an external magnetic field, a combined coagulation–ultrafiltration process was employed to actively regulate the cake layer, forming a magnetically responsive cake layer with a graded structure of lower density at the bottom and higher density at the top. The SPDA@MIL-101/PES membrane exhibited significantly improved hydrophilicity, indicated by the reduced contact angle of 54.89°, as well as excellent removal efficiency toward algae-derived fluorescent organic matter. Guided by the magnetic field, the PCNF flocculant formed a loose and porous cake layer, leading to a membrane flux recovery rate of 89% and a reduction in irreversible fouling to 11%. Mechanistic analysis revealed that the magnetic field-mediated dynamic reconstruction of the cake layer optimized the pore network, simultaneously improving contaminant retention and flux recovery performance. This study provides a feasible strategy for the treatment of high-algae water by combining material innovation and process regulation.

Eye tracking has become a key technology for gaze-based interactions in Extended Reality (XR). However, conventional frame-based eye-tracking systems often fall short of XR’s stringent requirements for high accuracy, low latency, and energy efficiency. Event cameras present a compelling alternative, offering ultra-high temporal resolution and low power consumption. In this paper, we present JaneEye, an energy-efficient event-based eye-tracking hardware accelerator designed specifically for wearable devices, leveraging sparse, high-temporal-resolution event data. We introduce an ultra-lightweight neural network architecture featuring a novel ConvJANET layer, which simplifies the traditional ConvLSTM by retaining only the forget gate, thereby halving computational complexity without sacrificing temporal modeling capability. Our proposed model achieves high accuracy with a pixel error of 2.45 on the 3ET+ dataset, using only 17.6 K parameters, with up to 1250 Hz event frame rate. To further enhance hardware efficiency, we employ custom linear approximations of activation functions (HardSigmoid and Hard-Tanh) and fixed-point quantization. Through software-hardware co-design, our 12-nm ASIC implementation operates at 400 MHz, delivering an end-to-end latency of 0.5 ms (equivalent to 2000 Frames Per Second (FPS)) at an energy efficiency of 18.9 μJ/frame. JaneEye sets a new benchmark in low-power, high-performance eye-tracking solutions suitable for integration into next-generation XR wearables.

The increasing adoption of Deep Neural Network (DNN)-based Digital Pre-distortion (DPD) in modern communication systems necessitates efficient hardware implementations. This paper presents DPD-NeuralEngine, an ultra-fast, tiny-area, and power-efficient DPD accelerator based on a Gated Recurrent Unit (GRU) neural network (NN). Leveraging a co-designed software and hardware approach, our 22 nm CMOS implementation operates at 2 GHz, capable of processing I/Q signals up to 250 MSps. Experimental results demonstrate a throughput of 256.5 GOPS and power efficiency of 1.32 TOPS/W with DPD linearization performance measured in Adjacent Channel Power Ratio (ACPR) of -45.3 dBc and Error Vector Magnitude (EVM) of -39.8 dB. To our knowledge, this work represents the first AI-based DPD application-specific integrated circuit (ASIC) accelerator, achieving a power-area efficiency (PAE) of 6.6

Digital predistortion (DPD) enhances signal quality in wideband radio frequency (RF) power amplifiers (PAs). As signal bandwidths expand in modern radio systems, DPD's energy consumption increasingly impacts overall system efficiency. Deep neural networks (DNNs) offer promising advancements in DPD, yet their high complexity hinders their practical deployment. This article introduces open-source mixed-precision (MP) neural networks that employ quantized low-precision fixed-point parameters for energy-efficient DPD. This approach reduces computational complexity and memory footprint, thereby lowering power consumption without compromising linearization efficacy. Applied to a 160-MHz-BW 1024-QAM OFDM signal from a digital RF PA, MP-DPD gives no performance loss against 32-bit floating-point precision DPDs, while achieving -43.75 (L)/-45.27 (R) dBc in the adjacent channel power ratio (ACPR) and -38.72 dB in error vector magnitude (EVM). A 16-bit fixed-point-precision MP-DPD enables a 2.8× reduction in estimated inference power. The DPD code in PyTorch is publicly available on GitHub.