This study proposes a self-powered food monitoring system that benefits from far-field RF Energy harvesting and deep learning techniques. Recent smart IoT systems for food quality management mainly focused on the appearance of Total Volatile Organic Compounds (TVOCs) during food preservation, which has the limitations of large power consumption, complex configuration, and low accuracy. Different from these methods, we aim to measure the gradual increase in air pressure inside food packages caused by gas emissions during food quality deterioration. With this new approach, the designed sensor circuit's energy consumption is reduced significantly than in conventional systems. The sensor module's operation power is supplied by an RF energy harvester capable of converting electromagnetic waves in space into electrical energy. We adopted a Yagi three elements as a receiver antenna in the RF energy scavenging module to enhance the harvested power and transmission distance. The designed antenna has a high gain of 6.54dBi in the direction of maximum radiation and voltage standing wave ratio (VSWR) better than 1.3 in approximately 60 MHz band (890-950 MHz). To demonstrate the feasibility of the proposed system, a set of experiments were conducted with different sorts of food such as pork, chicken, and fish. Raw data of food storage temperature, air pressure, and storage time obtained from the battery-less sensor module were analyzed and utilized to assess food quality changes. Several classification models, including LSTM, 1D-CNN, MLP, and SVM were developed and trained on the pork, chicken, and fish datasets to predict different food quality states. Experimental results show that the LSTM classifier, which can extract temporal characteristics from the dataset, achieves the best accuracy of above 99% on all three datasets. The food classification performance is investigated based on training accuracy and confusion matrix.

A new tactile sensing module was proposed to sense the contact force and location of an object on a robot hand, which was attached on the robot finger. Three air pressure sensors are installed at the tip of the finger to detect the contacting force at the points. To obtain a nominal contact force at the finger from data from the three air pressure sensors, a force estimation was developed based upon the learning of a deep neural network. The data from the three air pressure sensors were utilized as inputs to estimate the contact force at the finger. In the tactile module, the arrival time of the air pressure sensor data has been utilized to recognize the contact point of the robot finger against an object. Using the three air pressure sensors and arrival time, the finger location can be divided into 3 × 3 block locations. The resolution of the contact point recognition was improved to 6 × 4 block locations on the finger using an artificial neural network. The accuracy and effectiveness of the tactile module were verified using real grasping experiments. With this stable grasping, an optimal grasping force was estimated empirically with fuzzy rules for a given object.

Automated monitoring technology is necessary to ensure the safe consumption and management of meat. Here we propose a system for determining food freshness on the basis of data collected by embedded cameras and gas sensors using artificial intelligence of things (AIoT). Using the DeepLab V3+ model, images of meat obtained from embedded cameras underwent semantic segmentation into lean meat and fat, revealing the nature of rotting over time. Using convolutional neural network (CNN) learning, we classified meat into fresh, semi-fresh, and rotten and detected the transition among these. The Raspberry Pi server collected online images and gas sensor data, which was then processed by a mobile application. The sensor data were collected at 1-minute intervals, and the means and standard deviations of the valuable features were extracted. Meanwhile, the camera images were preprocessed, and their features were obtained using the transfer-learning approach and MobileNet as the feature extractor. Next, a proposed one-dimensional CNN was trained on the combined features for predicting different meat quality states. The accuracy of meat freshness detection using the proposed deep learning model reaches up to 99.44%. Our Internet of Things (IoT)−connected monitoring platform, including an embedded device, database server, and smartphone application, allows for quick and easy monitoring of meat quality. The experimental results confirmed the viability of our deep-learning−based meat-monitoring system with IoT applications in the real world.

The current profile of a $Z$-pinch plasma is investigated using a one-dimensional magnetohydrodynamic code. Simulation results reveal the formation of a reversed current profile, its propagation, and an ejection of plasma at boundary region, which have been observed in previous experiments. A new physical mechanism is proposed to account for such phenomena. The physical mechanism involves the propagation of a shock wave. It is found that a reversed current profile appears when a shock wave reflected at axis expands in a compressing plasma column.