Discovering New Avenues: Ultrasonic Waves as the Key to Object Steering

 

 

About Topic In Short:
	Who: University of Minnesota Twin Cities, Ognjen Ilic (senior author), Matthew Stein (first author).
	What: Researchers discovered a novel method using ultrasound waves for contactless manipulation, enabling movement of objects without the need for a built-in power source, with applications in manufacturing and robotics.
	How: By employing metamaterial physics and placing a metamaterial pattern on an object's surface, researchers steer it in a desired direction using sound waves, without physical contact.

  

Introduction:

In the realm of contactless manipulation, a groundbreaking revelation emerges from the University of Minnesota Twin Cities researchers who harness the power of ultrasound waves to propel objects. This newfound technique unlocks vast possibilities for industries like manufacturing and robotics, enabling devices to move autonomously without the constraints of a built-in power source.

 

Background:

The research, unveiled in Nature Communications, unveils the art of object manipulation using the artistry of light and sound waves—a phenomenon known before. Yet, in previous cases, the confines of sound and light wavelengths limited the objects to diminutive scales, mere millimeters to nanometers in magnitude. Surmounting this challenge, the research team delves into the complexities of metamaterial physics, transcending boundaries to manipulate grander entities.

 

Metamaterials and Contactless Manipulation:

Metamaterials, ingeniously designed materials engrossed in wave interactions, emerge as the heart of this study. Implementing a metamaterial pattern upon an object's surface, researchers steer it with sound waves, all devoid of any physical contact.

 

Key Discoveries:

The study's esteemed author, Ognjen Ilic, a professor in the College of Science and Engineering, stresses the distinctiveness of their research, wielding prowess to manipulate and ensnare more substantial entities through the marvel of "metasurfaces." By rendering an object's surface a metamaterial sanctuary, they achieve wondrous feats in object manipulation.

 

Contactless Actuation and Future Prospects:

The research pioneers a novel approach to contactless actuation, furnishing unmatched advantages over existing methods. Its implications reverberate throughout industries demanding precision and hands-free dexterity. The realm of possibilities evokes echoes of science fiction sagas, wherein objects advance and gravitate towards predetermined sources, akin to the wondrous tractor-beam technologies heralded in Star Trek's narratives.

 

Future Research and Implications:

Though the study primarily demonstrates the concept, a vibrant trajectory lies ahead as researchers venture into the realm of higher wave frequencies and diverse materials and object scales. The unfurling research promises a future wherein contactless manipulation becomes an inseparable constituent of numerous industries.

 

Insights from the Experts:

Ognjen Ilic, the study's venerable senior author, reverberates the groundbreaking essence of their work, illuminating the potency of sound and light in manipulating more substantial entities—a revelation that engenders novel mechanisms for contactless operations. Matthew Stein, the paper's first author and an erudite graduate student in the College of Science and Engineering, emphasizes the burgeoning domain of contactless manipulation in the realm of optics and electromagnetism. Their groundbreaking approach transcends conventional methods, unveiling an auspicious pathway for deeper exploration.

 

Conclusion:

The research illuminates a captivating horizon, with ultrasound waves as the conduit for contactless object manipulation, catapulting the University of Minnesota Twin Cities researchers into the vanguard of scientific breakthroughs. Embracing the complexities of metamaterial physics, they demonstrate the astonishing ability to maneuver objects of unprecedented scale. In the churning cauldron of innovation, the technique's potential cascades across industries, engendering a future replete with possibilities for contactless manipulation.

 

Image Gallery

College of Science and Engineering students with metamaterial object. Credit: University of Minnesota

By placing a metamaterial pattern on the surface of an object, the University of Minnesota researchers were able to use sound to steer it in a certain direction without physically touching it. (CREDIT: Olivia Hultgren)

All Images Credit: from References/Resources sites [Internet]

Video credit: Laboratory for Nano Optics and Mechanics, University of Minnesota

Hashtag/Keyword/Labels:

#UltrasoundWaves #ContactlessManipulation #MetamaterialPhysics #ScienceAndEngineering #Robotics #Manufacturing #Research #UniversityOfMinnesota

 

References/Resources:

1. University of Minnesota News Release: "Researchers use ultrasound waves to move objects hands-free" (Link: https://twin-cities.umn.edu/news-events/researchers-use-ultrasound-waves-move-objects-hands-free)

2. Nature Communications Journal: "Study on contactless manipulation using metamaterial physics" (Link: https://www.nature.com/ncomms)

 

For more such blog posts visit Index page or click InnovationBuzz label. 

…till next post, bye-bye and take-care.

An Innovative System for Enhanced Deep Learning: Utilizing Memristor Crossbars for Efficient and Sustainable AI

 

 

About Topic In Short:
	Who: Texas A&M University, Rain Neuromorphics, and Sandia National Laboratories. Authors: Suhas Kumar, Suin Yi, Jack Kendall, Stanley Williams.
	What: A memristor crossbar-based learning system for scalable and energy-efficient AI.
	How: The system reduces the carbon footprint and costs associated with AI training by using new hardware (memristor crossbars) and innovative algorithms, leading to power-efficient AI training.

 

Introduction:

This fascinating article delves into a groundbreaking research study conducted by experts from Texas A&M University, Rain Neuromorphics, and Sandia National Laboratories. The study unveils a novel system designed to optimize the training of deep learning models, addressing the challenges posed by energy-intensive and costly conventional AI training practices. This cutting-edge approach synergizes advanced hardware, memristor crossbars, with brain-like efficient algorithms, leading to remarkable advancements in AI training.

 

Background:

Deep-learning models have proven exceptionally effective in real-world tasks, encompassing data analysis and predictive capabilities. Nevertheless, training these models in physical data centers demands substantial time and energy before they can be effectively deployed in devices like cell phones. The costs associated with AI model training in large data centers present considerable hurdles for scalability and long-term viability.

 

The New System:

To transcend the limitations of conventional AI training techniques, the researchers have devised a unique system that capitalizes on the power of memristor crossbar hardware and novel training algorithms. Memristor crossbars, a highly parallel alternative to GPUs, enable simultaneous execution of multiple operations, significantly improving efficiency. The crux of this system's success lies in the development of an innovative co-optimized learning algorithm, inspired by the brain's intricate neuronal activity, fostering error tolerance and the ability to learn from sparse and noisy information.

 

Implementation and Benefits:

Leveraging memristor crossbars and the pioneering learning algorithm, the research team achieved substantial enhancements in energy efficiency and scalability. Demonstrating remarkable potential for complex tasks, the system accurately reconstructed Braille representations of renowned computer scientists from heavily distorted inputs.

 

Thus Speak Authors/Experts:

Suhas Kumar, the esteemed senior author of the study, highlights the energy-intensive nature of AI training and the imperative to foster sustainable and cost-effective large-scale implementation. The amalgamation of advanced hardware and algorithms in this system unlocks highly power-efficient AI training.

 

Suin Yi, the distinguished lead author of the study, accentuates the significance of memristor crossbars, which seamlessly embed synaptic weight where computing takes place, minimizing data movement. This compatibility with analog hardware successfully surmounts the limitations of traditional backpropagation algorithms, ultimately facilitating more efficient AI training.

 

Jack Kendall, another esteemed author of the paper, passionately discusses the far-reaching implications of this approach. Enabling deployment of AI models even on smaller devices such as cellphones and smartwatches, this system allows on-the-fly learning, adapting to dynamic environments without compromising user data security by sending it to the cloud for training.

 

Conclusion:

The pioneering research introduces a memristor crossbar-based learning system that not only conquers the challenges of energy-intensive and costly AI training but also presents an exciting step towards a sustainable and accessible AI future. From untethered devices to reducing data center carbon footprints, the potential of this innovative approach promises to revolutionize AI implementation.

 

Image Gallery

A chip consisting of memristor crossbars was trained using a local on-chip learning algorithm. The team demonstrated that their approach could accurately reconstruct Braille representations of nine famous computer scientists from highly distorted inputs. Credit: Yi et al.

ll Images Credit: from References/Resources sites [Internet]

 

Hashtag/Keyword/Labels:

#AItraining #memristorcrossbar #energyefficientAI #scalableAI #deeplearning #hardwarealgorithm #edgeAI

 

References/Resources:

1.       https://www.pressreader.com/india/electronics-for-you-express/20230203/282789245589032

2.       https://www.electronicsforu.com/news/whats-new/memristor-crossbar-based-deep-learning-for-efficient-ai

3.       https://techxplore.com/news/2022-12-memristor-crossbar-based-scalable-energy-efficient-ai.html

 

For more such blog posts visit Index page or click InnovationBuzz label. 

…till next post, bye-bye and take-care.

Spray-on Sensory System Makes Hands Smarter: A Breakthrough in Smart Skin Technology

 

 

About Topic In Short:
	Who: Stanford University, Authors: Kyun Kyu "Richard" Kim, Zhenan Bao, and others.
	What: Spray-on smart skin with an electrically active mesh network that uses AI to rapidly understand hand tasks and gestures.
	How: A stretchable, biocompatible material is sprayed on the back of the hand, integrated with a mesh network of nanowires made of silver coated with gold. The mesh captures changes in electrical conductivity as the hand moves, which are then interpreted by AI algorithms to recognize specific tasks and gestures.

 

In the realm of cutting-edge exploration, a groundbreaking innovation has surfaced from the hallowed halls of Stanford University—an astounding "spray-on sensory system" with the power to transform human hands into remarkably intelligent and adaptable tools. This revolutionary intelligent skin, crafted by a team of ingenious researchers led by Professor Zhenan Bao, holds the potential to revolutionize our interaction with technology and the world at large. By leveraging the prowess of Artificial Intelligence (AI) and an exceptional mesh network, this remarkable "Spray-on Sensory System" can swiftly decode intricate hand movements and gestures, granting users the ability to type, communicate, and identify objects with unparalleled precision, even from scant data.

 

The birth of the "Spray-on Sensory System" finds its origins in a recently published paper within the esteemed pages of Nature Electronics. The team's pioneering investigation revolves around a flexible, biocompatible substance that can be sprayed directly onto the back of the hand, akin to the application of suntan spray. This pliable material incorporates a finely interwoven electrical network, comprising countless nanowires made of silver coated with gold. When in contact with each other, these nanowires form dynamic electrical pathways, responsive to the nuanced movements of the human hand.

 

At the core of this intelligent skin lies the electrically sensitive mesh network embedded within polyurethane—a highly versatile and robust material employed in various applications. The mesh adapts seamlessly to the creases and folds of the human finger, ensuring comfort and reliability during use. As the hand flexes and contorts, the nanowires within the mesh compress and stretch, leading to alterations in the electrical conductivity of the mesh.

 

With the aid of sophisticated AI algorithms, the system diligently observes the fluctuating patterns in electrical conductivity and maps them to specific physical tasks and gestures. For instance, merely typing an "X" on an invisible keyboard enables the AI algorithm to discern the task based on the distinct electrical patterns produced during the gesture. The mesh adeptly captures subtle nuances in its signals, facilitating more efficient computational processing with lesser data. This revolutionary characteristic empowers the system to rapidly assimilate new hand tasks and users with minimal trials—a concept aptly termed "meta-learning."

 

The potential applications of this groundbreaking technology are as vast as they are diverse. From invisibly typing on keyboards to identifying objects through touch alone, this intelligent skin possesses the ability to redefine the manner in which we interact with computers, transforming immersive environments along the way. In spheres ranging from gaming and sports to telemedicine and robotics, the profound impact of this technology could be truly transformative.

 

The leading minds behind this innovative research, Professor Zhenan Bao and Kyun Kyu "Richard" Kim—a post-doctoral scholar in Professor Bao's lab and the first author of the study, respectively—offer their unique perspectives. According to Professor Bao, "As the fingers bend and twist, the nanowires in the mesh get squeezed together and stretched apart, changing the electrical conductivity of the mesh. These changes can be measured and analyzed to tell us precisely how a hand or a finger or a joint is moving."

 

Meanwhile, Kyun Kyu "Richard" Kim accentuates the simplicity of their approach to this intricate challenge. He affirms, "We brought the aspects of human learning that rapidly adapt to tasks with only a handful of trials known as 'meta-learning.' This allows the device to rapidly recognize arbitrary new hand tasks and users with a few quick trials."

 

In conclusion, the birth of the "Spray-on Sensory System" marks a remarkable triumph in intelligent skin technology. By harmoniously integrating AI and a finely interwoven mesh network, this intelligent skin can decipher intricate hand movements and gestures with extraordinary precision. Its boundless potential applications in various industries have the potential to redefine the way we interact with technology, offering us a future of immersive and intuitive experiences. As this trailblazing technology advances, we may witness a time when intelligent skin becomes an inseparable facet of our daily lives, empowering us to engage with the world in unimaginable ways.

 

Image Gallery

Spray-on sensory system which consists of printed, bio-compatible nanomesh directly connected with wireless Bluetooth module and further trained through meta-learning (Image credit: Kyun Kyu “Richard” Kim, Bao Group, Stanford U.)

Two-handed QWERTY keyboard typing recognition with nanomesh printed on both hands and real-time recognition of interacting objects (Image credit: Kyun Kyu “Richard” Kim, Bao Group, Stanford U.)

Keyboard typing video

All Images Credit: from References/Resources sites [Internet]

 

Hashtag/Keyword/Labels List:

#SmartSkin #SprayOnSensorySystem #AI #HandTasks #ImmersiveEnvironments #VirtualReality #Gaming #Telemedicine #Robotics #InvisibleKeyboards #SignLanguage #SmartTechnology #StretchableMaterial #Nanowires #MetaLearning #ElectricalConductivity #HumanComputerInteraction #ChemicalEngineering #StanfordUniversity

 

References/Resources List:

1. Andrew Myers, "Spray-on smart skin uses AI to rapidly understand hand tasks," Stanford University, Chemical Engineering Department.

2. Research paper: "A novel, electrically active smart skin for rapid hand motion interpretation" published in Nature Electronics.

3. Kyun Kyu "Richard" Kim, Zhenan Bao, et al., co-authors of the study.

 

For more such blog posts visit Index page or click InnovationBuzz label. 

…till next post, bye-bye and take-care.

Incredible AI Advancement: Discovering Shared Concepts Across Video, Audio, and Text

 

 

About Topic In Short:
	Who: Massachusetts Institute of Technology (MIT); Adam Zewe.
	What: Revolutionary AI System Learns Concepts Shared Across Video, Audio, and Text.
	How: Utilizing a representation learning model, the AI system captures shared concepts between visual and audio modalities in a shared embedding space, enabling cross-modal retrieval.

 

Introduction:

Introducing a mind-blowing research titled "Incredible AI Advancement: Discovering Shared Concepts Across Video, Audio, and Text," undertaken by brilliant minds at the Computer Science and Artificial Intelligence Laboratory (CSAIL). This groundbreaking artificial intelligence technique enables machines to grasp shared concepts spanning various modalities, including videos, audio clips, and images. By comprehending the interconnections between visual and auditory information, the AI system becomes capable of identifying and labeling actions depicted in videos. The ultimate goal of this research is to empower machines to process and interpret data from diverse sources, mimicking the way humans perceive the world around them.

 

Background:

The primary hurdle faced by machines in this context is aligning distinct modalities and forging meaningful links between them. Unlike machines, humans have an innate ability to perceive the world through multiple senses, effortlessly correlating visual cues with corresponding audio stimuli. Nevertheless, machines require intricate learning and encoding mechanisms to grasp these complex intermodal relationships.

 

Researchers:

The masterminds behind this incredible study are a team of experts from the prestigious Computer Science and Artificial Intelligence Laboratory (CSAIL) at the renowned Massachusetts Institute of Technology (MIT). Leading the pack is Alexander Liu, a remarkable graduate student at CSAIL and the first author of the paper. Supporting him are the brilliant minds of postdoc SouYoung Jin, as well as grad students Cheng-I Jeff Lai and Andrew Rouditchenko. At the helm of this talented team stands Aude Oliva, a seasoned research scientist at CSAIL and the esteemed MIT director of the MIT-IBM Watson AI Lab. Guiding the research with profound expertise is James Glass, a revered senior research scientist and the head of the esteemed Spoken Language Systems Group in CSAIL, serving as the senior author of this monumental study.

 

Research Objective:

The primary objective fueling this awe-inspiring research was to engineer an AI system with the capacity to assimilate shared concepts between the captivating realms of visual and auditory modalities. The visionary researchers aspired to create a revolutionary representation learning model, adept at processing data from diverse sources, such as videos, audio clips, and text captions, ultimately encapsulating them within a shared embedding space. Within this space, clusters of analogous data points harmoniously unite as individual vectors, each one uniquely representative of an essential concept within the data.

 

Methodology:

The ingenious researchers diligently focused on representation learning, a captivating form of machine learning that ingeniously simplifies intricate classification or prediction tasks. Pioneering an ingenious algorithm, they ingeniously mapped raw data points—namely videos and their corresponding text captions—into a captivating grid dubbed the embedding space. Within this spatial wonderland, akin data points gracefully congregated as singular entities, each awe-inspiringly embodied within an elegant vector. Remarkably, the model abided by a judicious constraint—merely employing a scant 1,000 words to label these enchanting vectors, thereby elegantly capturing quintessential concepts within the data.

 

Key Findings:

Behold, the AI system unveiled unparalleled prowess in conquering cross-modal retrieval tasks—its indomitable prowess thoroughly tested with three entrancing datasets: video-text, video-audio, and image-audio. Fathom this—an enchanting interplay occurred as researchers fed the model enchanting audio queries, as the model astutely matched the mellifluous spoken words with resplendent video clips, artfully portraying corresponding actions. Manifestly, this extraordinary technique outshone other mundane machine-learning methods, liberating users with insights into the very essence behind the model's magnificent decision-making.

 

Potential Applications:

Venture into the wondrous realm of possibilities—this revolutionary AI system, springing forth from this illustrious research, holds the key to unleashing robots' potential to learn from the world, akin to their human counterparts. As the AI system adroitly discerns shared concepts resonating across video, audio, and text, machines metamorphose into beacons of holistic data comprehension, adorning them with unparalleled efficiency and intuitional acumen, ripe for diverse applications across the grand tapestry of existence.

 

Thus Speak Authors/Experts:

These ingenious researchers trumpet the harmonious symphony between machine and human cognition—a symphony orchestrated by the alignment of mesmerizing visual and auditory modalities. With bated breath, they unveil a mesmerizing AI system, where the magnificent extraction and interweaving of essential concepts empower robots to apprehend and embrace their environment with human-like profundity.

 

Conclusion:

Behold, the masterpiece unveiled by the exceptional minds at MIT, a masterpiece transcending the boundaries of AI. This profound research bears witness to an AI system that not only imbibes and comprehends shared concepts embracing distinct modalities but also embodies the essence of human perception. Thus, this revelatory advancement ignites the spark that ignites the AI domain, enkindling new avenues toward crafting versatile and ingenious AI systems that mirror the very essence of human brilliance.

 

 

Image Gallery

Researchers at the Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed an artificial intelligence (AI) technique that allows machines to learn concepts shared between different modalities such as videos, audio clips, and images. The AI system can learn that a baby crying in a video is related to the spoken word “crying” in an audio clip, for example, and use this knowledge to identify and label actions in a video.

MIT researchers developed a machine learning technique that learns to represent data in a way that captures concepts which are shared between visual and audio modalities. Their model can identify where certain action is taking place in a video and label it. Credit: Courtesy of the researchers. Edited by MIT News

All Images Credit: from References/Resources sites [Internet]

 

Hashtag/Keyword/Labels:

#AI #MachineLearning #MIT #CrossModalLearning #RepresentationLearning #AIResearch #ArtificialIntelligence

 

References/Resources:

 

1.       https://news.mit.edu/2022/ai-video-audio-text-connections-0504

2.       https://scitechdaily.com/revolutionary-ai-system-learns-concepts-shared-across-video-audio-and-text/

3.       Research Paper: "Cross-Modal Discrete Representation Learning" by Alexander H. Liu et al.

4.       Website: scitechdaily.com/revolutionary-ai-system-learns-concepts-shared-across-video-audio-and-text/

 

For more such blog posts visit Index page or click InnovationBuzz label.

 

…till next post, bye-bye and take-care.

Pioneering Innovation Unravels the Enigma of Quantum Qubit Creation

 

 

About Topic In Short:
	Who: Institute of Industrial Science, The University of Tokyo; Authors: Atsushi Kobayashi, Shunya Kihira, Takahito Takeda, Masaki Kobayashi, Takayuki Harada, Kohei Ueno, and Hiroshi Fujioka.
	What: Researchers from The University of Tokyo demonstrated the growth of nanoscale layers of niobium nitride (NbNx) on top of an aluminum nitride (AlN) layer, enabling the integration of quantum qubits with existing microelectronics.
	How: The scientists investigated the impact of temperature on the crystal structures and electrical properties of the NbNx thin films grown on AlN template substrates, allowing for the formation of a highly crystalline layer at the interface. This facilitates the integration of superconductors into semiconductor optoelectronic devices.

  

In a groundbreaking experiment undertaken by ingenious researchers at The University of Tokyo's Institute of Industrial Science, a newfound methodology has surfaced that might spearhead a paradigm shift in quantum qubit fabrication. Quantum computing, harnessing the enigmatic attributes of quantum mechanics, harbors the capacity to resolve intricate quandaries at breakneck speeds. Nonetheless, its widespread adoption has been impeded by the intricacies entailed in constructing quantum frameworks from scratch. The scientists, however, have showcased an approach to harmonize quantum qubits with traditional microelectronics, employing a superconducting component, niobium nitride, which crystallizes as a layer atop a nitride-semiconductor substrate. This leap forward holds immense potential for streamlining the process of quantum device creation, culminating in seamless compatibility with prevailing computer technologies.

 

The Genesis of Innovation:

Unveiled on the 14th of December, 2022, the comprehensive study unravels the research undertaken by the brilliant minds at The University of Tokyo. Their primary focus revolved around cultivating nanoscale strata of niobium nitride (NbNx) directly atop an aluminum nitride (AlN) layer. Niobium nitride's superconductivity materializes at exceedingly low temperatures, rendering it eminently suited for fabricating superconducting qubits, the cornerstone of quantum computing. By configuring niobium nitride into a structure referred to as a Josephson junction, the researchers managed to actualize a superconducting qubit.

 

Noteworthy Discoveries and Their Ramifications:

The research cadre extensively explored the effects of temperature on the crystal structures and electrical traits of NbNx thin films, cultivated on AlN template substrates. Their investigations led them to uncover that a slight lattice mismatch between aluminum nitride and niobium nitride facilitated the formation of exceptionally crystalline layers at the interface. This seminal revelation paves the way for precise amalgamation of superconductors into semiconductor optoelectronic devices, forging a path to the development of quantum and conventional logic units on a singular chip.

 

By fashioning superconducting layers of mere nanometers in thickness, replete with high crystallinity, these innovative quantum devices can function as perceptive detectors of individual photons or electrons. Such groundbreaking strides carry far-reaching implications for the realm of quantum computing and quantum communication, heralding significant enhancements in the efficiency and performance of forthcoming quantum devices.

 

The Voices of Pundits and Experts:

As underscored by the preeminent and corresponding author, Atsushi Kobayashi, "The structural resemblance between aluminum nitride and niobium nitride streamlines the integration of superconductors into semiconductor optoelectronic devices." This articulate statement accentuates the profound import of the research findings and underscores the potential impact on the advancement of quantum computing technologies.

 

A Conclusive Note:

The scientists at The University of Tokyo have achieved a prodigious feat through their groundbreaking research, unriddling a pioneering technique streamlining quantum qubit fabrication. By blending superconducting components with traditional microelectronics, this revelation lays the groundwork for the seamless assimilation of quantum computing within the existing computer infrastructure. This newfound ability to forge quantum devices with heightened efficiency and seamless compatibility represents an epoch-making stride towards realizing practical quantum computing systems. The relentless momentum of quantum research portends a future replete with fascinating possibilities as quantum computing pervades and enriches our quotidian existence.

  

Image Gallery

Researchers at The University of Tokyo grow a nanoscale layer of a superconducting material on top of a nitride-semiconductor substrate, which may help facilitate the integration of quantum qubits with existing microelectronics. Credit: Institute of Industrial Science, The University of Tokyo

All Images Credit: from References/Resources sites [Internet]

 

Hashtag/Keyword/Labels:

#QuantumComputing #Superconductivity #UniversityOfTokyo #Nanotechnology #MaterialsScience

 

References/Resources:

1.       https://phys.org/news/2022-09-quantum-qubits-conventional-devices.html

2.       https://www.techexplorist.com/upgrading-computer-quantum/

3.       https://scitechdaily.com/upgrading-your-computer-to-quantum/

4.       https://www.discountmags.com/magazine/electronics-for-you-february-2023-digital-m/in-this-issue/hiRRo1jyO1675419030104

 

For more such blog posts visit Index page or click InnovationBuzz label. 

…till next post, bye-bye and take-care.