Artificial Intelligence enabled Smart Refrigeration Management System using Internet of Things Framework

Design of an intelligent refrigeration management system using artificial intelligence and Internet of Things (IoT) technology is presented in this paper. This system collects the real-time temperature inside the refrigeration implement, record the information of products and enhance function of refrigerators through the application of Internet of Things technology to facilitate people in managing their refrigerated and frozen groceries smartly. The proposed system is divided into two parts, On-board sub-system and Internet based sub-system. An Arduino Leonardo board is used in onboard sub-system to control other components including low power machine vision OpenMV module, temperature & Humidity sensor, and GY-302 light intensity sensor. OpenMV camera module is used for recognizing types of food, reading barcodes and OCR (optical character recognition) through convolution neural network (CNN) algorithm and tesseract-ocr. The food type identification model is trained by the deep learning framework Caffe. GY-302 light intensity sensor works as a switch of camera module. DHT11 sensor is used to monitor the environmental information inside the freezer. The internet based sub-system works on the things network. It saves the information and uploads it from onboard sub-system and works as an interface to food suppliers. The system demonstrates that the combination of existing everyday utility systems and latest Artificial Intelligence (AI) and Internet of Things (IoT) technologies could help develop smarter applications and devices

Intelligent Beam Management for Millimeter-Wave Cellular Networks in IoT System

No abstract available

Trustworthy VANET: Hierarchical DAG-Based Blockchain Solution With Proof of Reputation Consensus Algorithm

No abstract available

Distinction between critical current effects and intrinsic anomalies in the point-contact Andreev reflection spectra of unconventional superconductors

In this work, we discuss the origin of several anomalies present in the point-contact Andreev reflection spectra of (Li1-xFex)OHFeSe, LiTi2O4, and La2-xCexCuO4. While these features are similar to those stemming from intrinsic superconducting properties, such as Andreev reflection, electron-boson coupling, multigap superconductivity, d-wave and p-wave pairing symmetry, they cannot be accounted for by the modified Blonder–Tinkham–Klapwijk (BTK) model, but require to consider critical current effects arising from the junction geometry. Our results point to the importance of tracking the evolution of the dips and peaks in the differential conductance as a function of the bias voltage, in order to correctly deduce the properties of the superconducting state

Kaplan-Meier survival curve for 66 pancreatic cancers with galectin-1 staining intensity in tumor associated stromal cells.

<p>Kaplan-Meier survival curve for 66 pancreatic cancers with galectin-1 staining intensity in tumor associated stromal cells.</p

PSCs derived Galectin-1 promotes the proliferative activity (S-phase fraction) of CFPAC-1 cells.

<p>(a–c) Quantitative graphical representation of Apoptosis, G1, G2 and S cell population. Points, average of three independent experiments. a, CFPAC-1; b, CFPAC-1 + hNPSC; c, CFPAC-1 + hCaPSC. (d) A bar-graphical representation S-Phase Fraction cells in each group. *<i>p</i><0.01 <i>vs</i>. CFPAC-1, #<i>p</i><0.05 <i>vs</i>. hNPSC+CFPAC-1, ^△<i>p</i>>0.05 <i>vs</i>. hNPSC +β-Lactose.</p

Galectin-1 intensity of staining and clinicopathologic characteristics of the 18 patients with chronic pancreatitis and 66 patients with pancreatic cancer.

<p>UICC, International Union Against Cancer; PNI, perineural invasion.</p