Ultra low power mixer with out-of-band RF energy harvesting for wireless sensor networks applications

An ultra low power mixer with out-of-band radio frequency (RF) energy harvesting suitable for the wireless sensors network (WSN) application is proposed in this paper. The presented mixer is able to harvest the out-of-band RF energy and keep it working in ultra low power condition and extend the battery life of the WSN. The mixer is designed and simulated with Global Foundries ’ 0.18 μ m CMOS RF process, and it operates at 2.4GHz industrial, scientific, and medical (ISM) band. The Cadence IC Design Tools post-layout simulation results demonstrate that the proposed mixer consumes 248 μ W from a 1V supply voltage. Furthermore, the power consumption can be reduced to 120.8 μ W by the out-of-band RF energy harvesting rectifier

Innovation outcomes of knowledge-seeking Chinese foreign direct investment

Purpose The purpose of this paper is to investigates how organizational learning, absorptive capacity, cultural integration, specialization of the acquired firm and characteristics of transferred knowledge impact innovation performance subsequent to overseas acquisitions. Design/methodology/approach Survey responses from 222 Chinese multinational enterprises engaged in overseas acquisitions. Findings Differences between acquiring and acquired firms’ capabilities, while having a positive direct influence, suppress the positive impact of organizational learning and absorptive capacity, suggesting that multinationals require some basic level of capabilities to appropriate value from overseas acquisitions. Research limitations/implications This paper investigates the impact of knowledge-seeking overseas acquisition of Chinese multinationals on innovation performance, as this appears to be the primary motive for making such acquisitions. Practical implications Knowledge-seeking overseas acquisition should be based upon the absorptive capacity of the acquiring firm and complementarity between both firms. In knowledge-seeking overseas acquisitions, establishing an effective organizational learning mechanism is necessary for improving innovation performance. Originality/value This paper reports on the behaviour and innovation performance of Chinese multinationals through analysis of primary data

Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach

Recent progress in single-image super-resolution (SISR) has achieved remarkable performance, yet the computational costs of these methods remain a challenge for deployment on resource-constrained devices. Especially for transformer-based methods, the self-attention mechanism in such models brings great breakthroughs while incurring substantial computational costs. To tackle this issue, we introduce the Convolutional Transformer layer (ConvFormer) and the ConvFormer-based Super-Resolution network (CFSR), which offer an effective and efficient solution for lightweight image super-resolution tasks. In detail, CFSR leverages the large kernel convolution as the feature mixer to replace the self-attention module, efficiently modeling long-range dependencies and extensive receptive fields with a slight computational cost. Furthermore, we propose an edge-preserving feed-forward network, simplified as EFN, to obtain local feature aggregation and simultaneously preserve more high-frequency information. Extensive experiments demonstrate that CFSR can achieve an advanced trade-off between computational cost and performance when compared to existing lightweight SR methods. Compared to state-of-the-art methods, e.g. ShuffleMixer, the proposed CFSR achieves 0.39 dB gains on Urban100 dataset for x2 SR task while containing 26% and 31% fewer parameters and FLOPs, respectively. Code and pre-trained models are available at https://github.com/Aitical/CFSR.Comment: submitting to TI

The Impact of Exchange Rate Depreciation on Economic and Business Growth in Pakistan

Depreciation remained a common factor in Pakistani economic history in different regimes, which affected different economic variables, especially the growth and business sector. We have linked depreciation with economic and business growth for Pakistan in this paper. Using time series data from 1976 to 2010 and employing cointegration followed by the Error Correction Model, we find that exchange rate depreciation has adversely affected growth in the business sector, notably Investment and FDI, while net export has a positive association with the exchange rate. All these findings reveal that depreciation is not a good practice because it has negative impact for growth in the business sector. The present scenario of the flexible exchange rate doesn't allow the corresponding authorities to set desirable exchange rates, however, the government must reinforce the real sector in order to ensure a stable exchange rate and hence macroeconomic stability. Keywords: Foreign Exchange; General; Open Economy Macroeconomics; Economic Growth of Open Economie

Study on Xiangyang's population and aging trend prediction based on discrete population development equation model

Abstract Population problem is an important factor that influences economy and social development of China. This paper takes the statistic data of 6th census in 2010 in Xiangyang as the accordance to establish a discrete model of population development equation, to analyse the population aging trend in the future in Xiangyang from a short period, and further to predict the long-term population development trend and aging population change condition in Xiangyang in the case of different total fertility rate to provide reference accordance for the government to make relevant social and economic decisions

CAT:Collaborative Adversarial Training

Adversarial training can improve the robustness of neural networks. Previous methods focus on a single adversarial training strategy and do not consider the model property trained by different strategies. By revisiting the previous methods, we find different adversarial training methods have distinct robustness for sample instances. For example, a sample instance can be correctly classified by a model trained using standard adversarial training (AT) but not by a model trained using TRADES, and vice versa. Based on this observation, we propose a collaborative adversarial training framework to improve the robustness of neural networks. Specifically, we use different adversarial training methods to train robust models and let models interact with their knowledge during the training process. Collaborative Adversarial Training (CAT) can improve both robustness and accuracy. Extensive experiments on various networks and datasets validate the effectiveness of our method. CAT achieves state-of-the-art adversarial robustness without using any additional data on CIFAR-10 under the Auto-Attack benchmark. Code is available at https://github.com/liuxingbin/CAT.Comment: Tech repor