Resource Efficient Over-the-Air Fronthaul Signaling for Uplink Cell-Free Massive MIMO Systems

We propose a novel resource efficient analog over-the-air (OTA) computation framework to address the demanding requirements of the uplink (UL) fronthaul between the access points (APs) and the central processing unit (CPU) in cell-free massive multiple-input multiple-output (MIMO) systems. We discuss the drawbacks of the wired and wireless fronthaul solutions, and show that our proposed mechanism is efficient and scalable as the number of APs increases. We present the transmit precoding and two-phase power assignment strategies at the APs to coherently combine the signals OTA in a spectrally efficient manner. We derive the statistics of the APs locally available signals which enable us to to obtain the analytical expressions for the Bayesian and classical estimators of the OTA combined signals. We empirically evaluate the normalized mean square error (NMSE), symbol error rate (SER), and the coded bit error rate (BER) of our developed solution and benchmark against the state-of-the-art wired fronthaul based systemComment: 6 pages, 4 figures, Submitted to IEEE International Conference on Communications (ICC), 202

Finite Element Study on the Effect of Geometrical Parameters on the Mechanical Behavior of 3D Reentrant Auxetic Honeycombs.

Abstract Auxetic materials are a special case of cellular materials, which exhibit a negative Poisson’s ratio. This in fact is the reason behind their peculiar behavior i.e. lateral shrinkage under longitudinal compression and vice versa. Since these materials do not obey the laws of “normal” materials and go beyond common sense, they are still an emerging class which can be put to use for various purposes like self-locking reinforcing fibers in composites, controlled release media, self-healing films, piezoelectric sensors, and also be used in biomedical engineering. Their stress-strain behavior, Poisson’s ratio and impact energy absorption are controlled by bulk material as well as the unit cell geometry. Among many forms of auxetic structures available, we have chosen a three-dimensional reentrant auxetic honeycomb unit cell. The unit cell geometrical parameters were taken from literature. In this study, we try to understand the effects of strut angle through finite element simulations while keeping the bulk material, unit cell size, strut thickness and number of repetitions constant. A total of three different angles were tested, based on which we conclude that as angle increases, the Poisson’s ratio increases and Energy absorption is maximum at 30 deg

UTILIZING THE UNUSED GLASS SUBSTANCE IN STAND-IN OF CEMENT

Glass can be used in lots of forms in day-to-day existence. It's limited life time after utilize it is either stock stacked or delivered to landfills. Since glass is non-biodegradable, landfills don't offer an atmosphere friendly solution. Hence, there's strong have to utilize waste glasses. Many efforts happen to be designed to use waste glass in concrete industry like a substitute of coarse aggregate, fine aggregate and cement. Its performance like a coarse aggregate substitute has been discovered to become non-acceptable due to strength regression and expansion because of alkali-silica reaction. The study implies that there's strength loss because of fine aggregate substitution also. The purpose of the current work ended up being to use glass powder like a substitute of cement to evaluate the pozzolanic activity of proper glass powder in concrete and compare its performance along with other pozzolanic materials like silica fume and fly ash. A number of tests were conducted to review the result of 15% and 30% substitute of cement by silica fume, fly ash and glass powder on compressive durability and strength by means of capillary absorption. The particle size effect was evaluated by utilizing glass powder of size 150µm-100µm and glass powder of size under 100µm. The current study implies that waste glass, if ground finer than 100µm shows a pozzolanic behavior. It responds to lime at initial phase of hydration developing extra CSH gel therefore developing denser cement matrix. The first use of alkalis by glass particles mitigate alkali-silica reaction hence increase reliability of concrete

Generative AI for Consumer Electronics: Enhancing User Experience with Cognitive and Semantic Computing

Generative Artificial Intelligence(GAI) models such as ChatGPT , DALL-E , and the recently introduced Gemini have attracted considerable interest in both business and academia because of their capacity to produce material in response to human inputs. Cognitive computing is a broader field of machine learning that encompasses GAI, which particularly emphasizes systems capable of creating content, such as images, text, or sound, while semantic computing acts as a fundamental element of GAI, furnishing the comprehension of context and significance essential for GAI systems to generate content akin to human-like standards. GAI is becoming a game-changing technology for consumer electronics industry with a variety of applications that improve user experiences and product development. GAI can revolutionise architectural visualisation by facilitating quick prototyping and the investigation of cutting-edge design ideas. By creating unique compositions and graphics for a variety of applications, it also empowers media production and music composition. Our research identifies several applications of GAI in the consumer electronics industry. We analyze how GAI is utilized in augmented reality (AR) applications, optimizing user interactions and immersive experiences. Moreover, we explore the integration of GAI in voice assistants and virtual avatars, enhancing images, natural language understanding and delivering more personalized interactions. We present a novel case study on a Generative Artificial Intelligence-based Framework for answering consumer electronics queries. We have developed and presented the system using various GAI-based tools and integrations. The paper also discusses the challenges in implementing GAI in consumer electronics, such as ethical considerations, data privacy, compatibility with existing systems, and the need for continuous updates and improvements

Boiling and Condensation

This chapter contains a brief overview of both boiling and condensation heat transfer phenomena. Boiling and condensation are the two convective heat transfer phenomena that involve phase change from liquid to vapour and vapour to liquid, respectively. The chapter starts with the basis of heat transfer with an emphasis on the boiling and condensation phenomenon. Next, the overview of the boiling phenomenon and its different classifications like pool, flow, and subcooled and saturated boiling are discussed in detail. Different boiling regimes (natural convection boiling, nucleate boiling, transition boiling and film boiling) with the observed heat transfer rate in the case of pool boiling are mentioned in detail using the boiling curve. The heat transfer aspect and basics of condensation with types (drop and film-wise condensation) and application are also presented. The derivation for the calculation of the rate of heat transfer during film condensation with the correlations for heat transfer coefficient on vertical, horizontal and inclined plates is explained. Some numerical for the calculation of the rate of heat transfer and heat transfer coefficient for condensation phenomena has been also been mentioned. Apart from a basic overview, this chapter also includes information about the advanced heat transfer enhancement techniques available for boiling and condensation

Luminescence study of Erbium doped CaZrO3 Phosphor

Erbium(Er)-2.0 wt%, 2.5 wt% doped CaZrO3 phosphors were prepared by using Solid State Reaction method. The Luminescent properties, Crystal structures and Crystal sizes of CaZrO3 were Studied using Photo Luminescence, X-ray diffraction (XRD), Scanning Electron Microscope (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). The Photo Luminescence spectra show peaks in green region. The crystallite size lying in nano range.     &nbsp

Application of multiple linear regression and machine learning algorithms to elucidate the association of poor glycemic control and hyperhomocysteinemia with microalbuminuria

Microalbuminuria is an early biomarker of general vascular dysfunction and a predictor of risk for cardiovascular and renal diseases. It is also considered as a marker of insulin resistance in both diabetic and non-diabetic patients. The rationale of this study was to elucidate threshold values of fasting blood glucose (FBS) and glycosylated hemoglobin (HbA1c) that are associated with microalbuminuria. In the parallel association of microalbuminuria with hyperhomocysteinemia was investigated. Machine learning algorithm and multiple linear regression were applied to study the association of poor glycemic control on microalbuminuria and hyperhomocysteinemia. In non-diabetic subjects with FBS <102 mg/dL and HbA1c <6.3%; and in diabetic subjects with good glycemic control (FBS: 102-118 mg/dL; HbA1c: 6.3-7.0%), urinary microalbumin levels were <40µg/mg creatinine. Poor glycemic control (FBS >172 mg/dL and HbA1c >9.0%) was associated with microalbumin >40µg/mg creatinine. Age, gender, HbA1c and FBS were shown to explain variability in urinary microalbumin to the extent of 54.4% as shown by multiple linear regression model. Analysis of variance (ANOVA) revealed higher levels of FBS (F: 39.77, P <0.0001), HbA1c (F: 64.31, P <0.0001) and total plasma homocysteine (F: 3.69, P =0.04) in microalbuminuria and clinical microalbuminuria groups when compared to subjects with normal microalbumin levels. Diabetic patients with poor glycemic index had a more B12 deficiency. Poor glycemic index and hyperhomocysteinemia were associated with clinical microalbuminuria

Beyond Reality: The Pivotal Role of Generative AI in the Metaverse

Imagine stepping into a virtual world that's as rich, dynamic, and interactive as our physical one. This is the promise of the Metaverse, and it's being brought to life by the transformative power of Generative Artificial Intelligence (AI). This paper offers a comprehensive exploration of how generative AI technologies are shaping the Metaverse, transforming it into a dynamic, immersive, and interactive virtual world. We delve into the applications of text generation models like ChatGPT and GPT-3, which are enhancing conversational interfaces with AI-generated characters. We explore the role of image generation models such as DALL-E and MidJourney in creating visually stunning and diverse content. We also examine the potential of 3D model generation technologies like Point-E and Lumirithmic in creating realistic virtual objects that enrich the Metaverse experience. But the journey doesn't stop there. We also address the challenges and ethical considerations of implementing these technologies in the Metaverse, offering insights into the balance between user control and AI automation. This paper is not just a study, but a guide to the future of the Metaverse, offering readers a roadmap to harnessing the power of generative AI in creating immersive virtual worlds.Comment: 8 pages, 4 figure