Automatic modulation classification (AMC) plays a vital role in Internet of Things (IoT) and Cognitive Radio (CR) to ensure communication security, but the AMC technology is suffering from low signal-to noise ratio (SNR), non-ideal defects in communication systems, and so forth. In order to suppress the impact of interferences on AMC and improve classification performance, we propose a novel multi-dimensional image-based AMC method: Multi-dimensional Image Information Fusion Network (MIIF-Net). Based on the analysis of signal model, this work focuses on suppressing noise and carrier frequency offset (CFO) to improve the classification accuracy of AMC. Motivated by multi-dimensional feature strategy, modulation signals are converted and encoded into multi-dimensional images to introduce multi-domain information and suppress CFO. From the perspective of image enhancement, a novel noise suppression method is proposed, which does not rely on any prior information about signals. In addition, residual network (ResNet) is utilized as encoder to extract distinctive feature in different dimensions, and channel attention mechanism (CAM) is employed to fuse multi-dimensional features. The multi-layer perceptron is utilized for modulation classification in MIIF-Net. MIIF-Net is evaluated in public dataset. Simulation results demonstrate that the proposed MIIF-Net achieves superior performance, especially under low SNR levels. MIIF-Net is further evaluated in practical acquired data. The experimental result shows that MIIF-Net exhibits the consistent advantage under low SNR levels.

The estimation model-based alternate test strategy for analog integration circuits (ICs) offers an effective way to reduce test costs. However, as a data-driven method, the estimation process of alternate test is invisible, leading to low test reliability. Hence, to address this problem, we propose an adaptive confidence interval-based alternate test (ACIT) to enhance the reliability of alternate test. Multiestimators are implemented to generate target parameters of each circuit synchronously. All estimations for the same sample are averaged to get the final result. The reliability of each final estimation is evaluated by comparing its adaptive confidence interval to the correlated parameter boundary. The proposed adaptive confidence interval is obtained from the variance of multioutputs and estimation-boundary distance. Estimations with confidence intervals crossing boundaries are identified as suspect results and returned to repeat testing by the conventional approach. The remaining results are classified as “pass” (entire confidence intervals within the qualified range) or “fail” (entire confidence intervals outside of the qualified range). Our approach is studied with simulation data and verified on commercial ICs. Results demonstrate that the ACIT can eliminate the misclassification circuits effectively by identifying unreliable estimations.

Machine learning-based alternate test of analog/mixed-signal integrated circuits (ICs) has been widely studied in the last decade, which has the benefits of simplifying test equipment and decreasing test costs. However, due to low reliability and accuracy, it is hard to adopt the alternate test technique in the industry. In this article, a model splitting approach (MDSP approach) is proposed to improve the reliability and accuracy of the alternate test. The machine learning-based estimation model is “split” into two models with “complementary” performance (a “positive” model and a “negative” model). The “positive” model generates estimations that are no smaller than label values, while the “negative” model outputs estimations that are no larger than label values. Estimations with excessive differences between two models are identified as suspected estimations with large errors and filtered out. The rest results of “complementary” models are averaged to generate the final estimations. By comparing estimations of two models, the estimations with large error are filtered out effectively, and the estimation accuracy is improved significantly by fusing the results of two estimators. The MDSP approach is investigated with data from the commercial analog-to-digital converter and operational amplifier (OP). Results demonstrated that the proposed approach can improve test reliability and accuracy significantly.

LED-to-camera communication for smartphones promises to enable low-cost, small-size, and intuitive data access to Internet-of-things (IoT) devices. However, for fast and reliable communication under the rolling shutter effect, the packet losses caused by inter-frame gaps and frame drops must be dealt with. In this paper, we introduce LED-to-camera communication between a commercial smartphone and an IoT device with LED flashlight, where the user can intuitively acquire the desired data transmitted from the IoT device to the smartphone. Specifically, we present IoTorch, a fast and reliable LED-to-camera communication that efficiently prevents the packet losses. IoTorch consists of two core mechanisms: 1) a minimum-repetition one-way reliable transmission focusing on the periodicity of inter-frame gaps and 2) an acknowledgement mechanism to overcome frame drops by using a smartphone's built-in flash focusing on its delay characteristics. Additionally, we propose an optimization method to increase the throughput up to 2.92 kbps (i.e., 2.43 times faster than that of the state-of-the-art method that overcomes the inter-frame gaps). Furthermore, we realize a remote wake-up function by using the smartphone's flash as the data transmission trigger. To demonstrate the benefit of IoTorch, we present a sensor data viewer using Arduino and a smartphone console system on TelosB.

LoRa networks achieve a remarkably wide coverage as compared to that of the conventional wireless systems by employing the chirp spread spectrum (CSS). However, single-hop LoRa networks have limited penetration in indoor environments and cannot efficiently utilize the multiple-access dimensions arising from different spreading factors (SF) because the star topology is typically used for LoRa. On the other hand, a multi-hop LoRa network has the potential to realize extensive network coverage by utilizing packet relaying and improving the network capacity by utilizing different SFs. We evaluated the LoRa receiver performance under co-technology interference via simulation and experiments to realize these potentials. The results show that LoRa can survive interference from time-synchronized packets transmitted by multiple transmitters with an identical SF. Furthermore, we examined the orthogonality between different SFs by evaluating the required signal-to-interference ratio (SIR). Finally, we demonstrated the possibility of employing different SFs to construct the pipeline in a multi-hop relay network to increase network efficiency.