How Inexpensive RFID Is Revolutionizing the Supply Chain: (Innovations Case Narrative: The Electronic Product Code)

Like the electric, water, and information networks most of modern society relies on, there is another network, one far less visible, that makes modern life possible: the global supply chain. Almost every physical product that is grown, manufactured, or packaged arrives at a store or at our home through a series of transfers involving ships, planes, trains, and trucks. In between, products may be aggregated into pallets and containers; moved with cranes or forklifts; stored in ports, warehouses, or on shelves; kept secure in armored vehicles or vaults; kept fresh in refrigerated storage or “reefer” transportation units; and packaged, repackaged, or finished before they get into our hands. Supply-chain management, which involves everything from the sourcing and procurement of materials to logistics, is a major part of the U.S. economy. In 2011, U.S. business logistics costs totaled $1.28 trillion and accounted for 8.5 percent of the GDP

Light scattering and transmission measurement using digital imaging for online analysis of constituents in milk

Milk is an emulsion of fat globules and casein micelles dispersed in an aqueous medium with dissolved lactose, whey proteins and minerals. Quantification of constituents in milk is important in various stages of the dairy supply chain for proper process control and quality assurance. In field-level applications, spectrophotometric analysis is an economical option due to the low-cost of silicon photodetectors, sensitive to UV/Vis radiation with wavelengths between 300 - 1100 nm. Both absorption and scattering are witnessed as incident UV/Vis radiation interacts with dissolved and dispersed constituents in milk. These effects can in turn be used to characterize the chemical and physical composition of a milk sample. However, in order to simplify analysis, most existing instrument require dilution of samples to avoid effects of multiple scattering. The sample preparation steps are usually expensive, prone to human errors and unsuitable for field-level and online analysis. This paper introduces a novel digital imaging based method of online spectrophotometric measurements on raw milk without any sample preparation. Multiple LEDs of different emission spectra are used as discrete light sources and a digital CMOS camera is used as an image sensor. The extinction characteristic of samples is derived from captured images. The dependence of multiple scattering on power of incident radiation is exploited to quantify scattering. The method has been validated with experiments for response with varying fat concentrations and fat globule sizes. Despite of the presence of multiple scattering, the method is able to unequivocally quantify extinction of incident radiation and relate it to the fat concentrations and globule sizes of samples

PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention

Generating 3D point clouds is challenging yet highly desired. This work presents a novel autoregressive model, PointGrow, which can generate diverse and realistic point cloud samples from scratch or conditioned on semantic contexts. This model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points, allowing inter-point correlations to be well-exploited and 3D shape generative processes to be better interpreted. Since point cloud object shapes are typically encoded by long-range dependencies, we augment our model with dedicated self-attention modules to capture such relations. Extensive evaluations show that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to realism and diversity. Several important applications, such as unsupervised feature learning and shape arithmetic operations, are also demonstrated

On the Forgetting of College Academice: at "Ebbinghaus Speed"?

How important are Undergraduate College Academics after graduation? How much do we actually remember after we leave the college classroom, and for how long? Taking a look at major University ranking methodologies one can easily observe they consistently lack any objective measure of what content knowledge and skills students retain from college education in the long term. Is there any rigorous scholarly published evidence on retention of long-term unused academic content knowledge? We have found no such evidence based on a preliminary literature review. Furthermore, findings in all research papers reviewed in this study were consistent with the following assertion: the Ebbinghaus forgetting curve [Ebbinghaus 1880-1885] is a fundamental law of human nature – in fact, of the whole animal kingdom and applies to memory of all types: verbal, visual, abstract, social and autobiographical. This fundamental law of nature, when examined within the context of academic learning retention, manifests itself as an exponential curve halving memory saliency about every two years (what we call "Ebbinghaus Speed"). This paper presents the research group’s initial hypothesis and conjectures for college level education programming and curriculum development, suggestions for instructional design enhancing learning durability, as well as future research directions.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216

A novel communication method for semi-passive RFID based sensors

This paper presents a novel communication method for semi-passive RFID based sensors. The new method uses a digitally reconfigurable UHF RFID tag antenna to modulate sensed information at an RFID tag on to the received signal strength indicator (RSSI) response perceived at an RFID reader. This technique is completely compatible with the existing class 1 generation 2 UHF air interface protocol thereby enabling the use of existing RFID reader infrastructure to decode the additional sensed information. The effect of read distance, environment and bit duration on the performance of the communication method is examined through measurements obtained from a prototype. Through experimental verification, it is demonstrated that error free transmission of sensor information can be achieved up to 3.5 meters in different environments with a bit duration of 500 ms. Prospective future research directions are also discussed

Proof of Travel for Trust-Based Data Validation in V2I Communication Part I: Methodology

Previous work on misbehavior detection and trust management for Vehicle-to-Everything (V2X) communication can identify falsified and malicious messages, enabling witness vehicles to report observations about high-criticality traffic events. However, there may not exist enough "benign" vehicles with V2X connectivity or vehicle owners who are willing to opt-in in the early stages of connected-vehicle deployment. In this paper, we propose a security protocol for the communication between vehicles and infrastructure, titled Proof-of-Travel (POT), to answer the research question: How can we transform the power of cryptography techniques embedded within the protocol into social and economic mechanisms to simultaneously incentivize Vehicle-to-Infrastructure (V2I) data sharing activities and validate the data? The key idea is to determine the reputation of and the contribution made by a vehicle based on its distance traveled and the information it shared through V2I channels. In particular, the total vehicle miles traveled for a vehicle must be testified by digital signatures signed by each infrastructure component along the path of its movement. While building a chain of proofs of spatial movement creates burdens for malicious vehicles, acquiring proofs does not result in extra cost for normal vehicles, which naturally want to move from the origin to the destination. The proof of travel for a vehicle can then be used to determine the contribution and reward by its altruistic behaviors. We propose short-term and long-term incentive designs based on the POT protocol and evaluate their security and performance through theoretical analysis and simulations

A study on the ephemeral nature of knowledge shared within multiagent systems

Achieving knowledge sharing within an artificial swarm system could lead to significant development in autonomous multiagent and robotic systems research and realize collective intelligence. However, this is difficult to achieve since there is no generic framework to transfer skills between agents other than a query-response-based approach. Moreover, natural living systems have a "forgetfulness" property for everything they learn. Analyzing such ephemeral nature (temporal memory properties of new knowledge gained) in artificial systems has never been studied in the literature. We propose a behavior tree-based framework to realize a query-response mechanism for transferring skills encoded as the condition-action control sub-flow of that portion of the knowledge between agents to fill this gap. We simulate a multiagent group with different initial knowledge on a foraging mission. While performing basic operations, each robot queries other robots to respond to an unknown condition. The responding robot shares the control actions by sharing a portion of the behavior tree that addresses the queries. Specifically, we investigate the ephemeral nature of the new knowledge gained through such a framework, where the knowledge gained by the agent is either limited due to memory or is forgotten over time. Our investigations show that knowledge grows proportionally with the duration of remembrance, which is trivial. However, we found minimal impact on knowledge growth due to memory. We compare these cases against a baseline that involved full knowledge pre-coded on all agents. We found that knowledge-sharing strived to match the baseline condition by sharing and achieving knowledge growth as a collective system.Comment: In Proceedings of the Fifth International Symposium on Swarm Behavior and Bio-Inspired Robotics 2022 (SWARM 5th 2022