by jsendak | Jan 31, 2024 | Computer Science
New Method for Designing Analog Circuits in Deep Sub-micron CMOS Technology
In the field of semiconductor technology, designing analog circuits for deep sub-micron CMOS fabrication processes has always presented challenges. In this work, a new method is proposed that aims to streamline and expedite the design process, without relying on simulation software.
The key idea behind this method is the utilization of regression algorithms in conjunction with the transistor circuit model. By leveraging this approach, the sizing of a transistor in 0.18 um technology becomes faster and more efficient.
Addressing Nonlinear Parameters in Nano-scale Transistors
When it comes to nano-scale transistors, it becomes increasingly difficult to predict the behavior of key parameters such as threshold voltage, output resistance, and the product of mobility and oxide capacitance. Traditionally, circuit simulators have been relied upon to determine the values of these parameters.
However, this reliance on simulation software significantly increases design time. To overcome this challenge, the proposed method employs regression analysis to predict the values of these parameters, obviating the need for extensive simulations.
Performance Validation with Current Feedback Instrumentational Amplifier (CFIA)
To gauge the effectiveness of this new method, a Current Feedback Instrumentational Amplifier (CFIA) is designed and implemented using the proposed approach. The results are highly encouraging.
The accuracy achieved in predicting the desired value of W, a key parameter in transistor sizing, exceeds 90%. Moreover, this method reduces design time by over 97% when compared to conventional methods that rely on circuit simulations.
Impressive Circuit Performance Results
The designed circuit using this novel method exhibits impressive performance characteristics. It consumes a mere 5.76 uW of power, which is exceptionally low. Additionally, it boasts a Common Mode Rejection Ratio (CMRR) of 35.83 dB and achieves a gain of 8.17 V/V.
Overall, the application of this new method for designing analog circuits in deep sub-micron CMOS technology shows great promise. Its ability to accurately predict transistor parameters and significantly reduce design time makes it a valuable addition to the semiconductor industry.
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by jsendak | Jan 30, 2024 | Computer Science
Analyzing individual emotions during group conversation is crucial in
developing intelligent agents capable of natural human-machine interaction.
While reliable emotion recognition techniques depend on different modalities
(text, audio, video), the inherent heterogeneity between these modalities and
the dynamic cross-modal interactions influenced by an individual’s unique
behavioral patterns make the task of emotion recognition very challenging. This
difficulty is compounded in group settings, where the emotion and its temporal
evolution are not only influenced by the individual but also by external
contexts like audience reaction and context of the ongoing conversation. To
meet this challenge, we propose a Multimodal Attention Network that captures
cross-modal interactions at various levels of spatial abstraction by jointly
learning its interactive bunch of mode-specific Peripheral and Central
networks. The proposed MAN injects cross-modal attention via its Peripheral
key-value pairs within each layer of a mode-specific Central query network. The
resulting cross-attended mode-specific descriptors are then combined using an
Adaptive Fusion technique that enables the model to integrate the
discriminative and complementary mode-specific data patterns within an
instance-specific multimodal descriptor. Given a dialogue represented by a
sequence of utterances, the proposed AMuSE model condenses both spatial and
temporal features into two dense descriptors: speaker-level and
utterance-level. This helps not only in delivering better classification
performance (3-5% improvement in Weighted-F1 and 5-7% improvement in Accuracy)
in large-scale public datasets but also helps the users in understanding the
reasoning behind each emotion prediction made by the model via its Multimodal
Explainability Visualization module.
Analyzing individual emotions during group conversations is a crucial aspect of developing intelligent agents capable of natural human-machine interaction. This article highlights the challenges in emotion recognition techniques due to the heterogeneity between different modalities such as text, audio, and video. The dynamics of cross-modal interactions influenced by an individual’s behavioral patterns further complicates the task.
In the field of multimedia information systems, understanding and recognizing emotions in various modalities is essential for creating effective user interfaces and personalized experiences. By developing a Multimodal Attention Network (MAN), the researchers propose a solution to capture cross-modal interactions at different levels of spatial abstraction. This multi-disciplinary approach combines techniques from computer vision, natural language processing, and signal processing to overcome the challenges posed by the heterogeneity and dynamics of emotions.
The MAN model incorporates both peripheral and central networks to inject cross-modal attention. This allows the model to consider the influence of external factors like audience reactions and the context of ongoing conversations in group settings. By integrating discriminative and complementary mode-specific data patterns, the model can generate instance-specific multimodal descriptors, condensing spatial and temporal features into speaker-level and utterance-level representations.
The impacts of this research are significant not only in terms of classification performance improvement but also in enhancing user understanding of emotion predictions. The proposed AMuSE model includes a Multimodal Explainability Visualization module, which provides explanations for each emotion prediction. This brings transparency to the decision-making process of the model, enabling users to comprehend the reasoning behind the emotions detected.
These concepts are closely related to the wider field of multimedia information systems and have implications for various applications such as virtual reality, augmented reality, and artificial reality. These technologies can benefit from improved emotion recognition techniques to create more immersive and engaging experiences. By understanding users’ emotions, these systems can adapt and respond accordingly, enhancing user satisfaction.
In conclusion, the proposed Multimodal Attention Network and AMuSE model contribute to the development of intelligent agents capable of understanding and responding to human emotions during group conversations. The multi-disciplinary nature of this research, combining knowledge from various domains, is crucial in tackling the challenges posed by heterogeneous and dynamic emotion recognition. This article demonstrates the potential impact of these concepts on the wider field of multimedia information systems and related technologies like animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jan 30, 2024 | Computer Science
Can All NP Problems Be Solved in P Time? Analyzing the Complexity Classes
In the realm of computer science, the distinction between complexity classes P and NP has been the subject of extensive research and debate. The raison d’être of this article is to answer a fundamental question: Can all problems belonging to the complexity class NP be solved in polynomial time, thereby falling into the complexity class P?
To investigate this question, we will dive into a specific decision problem and examine its properties. By evaluating its characteristics and analyzing its behavior, we can draw insightful conclusions about the relationship between the P and NP complexity classes.
Defining Complexity Classes P and NP
Before delving into the analysis, let us first establish a clear understanding of complexity classes P and NP.
The class P encompasses problems that can be solved in polynomial time on a deterministic Turing machine. In simpler terms, these are problems for which there exists an algorithm that can find the solution efficiently.
On the other hand, the class NP consists of problems for which solutions can be verified efficiently but not necessarily found in polynomial time. In this case, a non-deterministic algorithm can efficiently verify whether a given solution is correct.
The Decision Problem: Classifying Complexity
Our decision problem for analysis aims to determine whether a problem belongs to complexity class P or NP. By scrutinizing this problem in detail, we can gain insights into the wider question of whether all NP problems are solvable in polynomial time.
Upon thorough examination, it becomes evident that this particular problem falls into the class NP. A non-deterministic algorithm can efficiently verify whether a given solution is correct by examining its properties and evaluating its validity. However, finding the right answer to this problem in polynomial time remains elusive.
This crucial distinction highlights the existence of at least one problem that lies within the class NP but remains outside the class P. Therefore, we reach the significant conclusion that not all NP problems can be solved in polynomial time.
The Future of P versus NP
The P versus NP problem has long been a fascinating enigma in the field of computer science. While this article has successfully demonstrated the existence of problems that fall into class NP but not class P, it leaves us with many unanswered questions.
Future research and exploration in this area are crucial to understanding the boundaries and limitations of computational efficiency. Researchers will continue to explore alternative approaches and algorithms in an attempt to bridge the gap between the classes P and NP.
Ultimately, unraveling the complexity classes P and NP is fundamental not only for theoretical computer science but also for practical applications such as optimization, cryptography, and artificial intelligence. The quest to determine whether P equals NP or not will undoubtedly persist as one of the most captivating puzzles in the realm of computer science.
Expert Commentary: The analysis presented in this article offers valuable insights into the complexities of problem-solving. By showcasing a specific decision problem that belongs to class NP but not class P, we gain a deeper understanding of the limitations of polynomial-time solvability. This discovery reinforces the need for continued research and innovation to tackle NP problems efficiently. Looking ahead, advancements in computational algorithms and theoretical frameworks may hold the key to unlocking more efficient approaches to problem-solving.
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by jsendak | Jan 29, 2024 | Computer Science
While speech interaction finds widespread utility within the Extended Reality
(XR) domain, conventional vocal speech keyword spotting systems continue to
grapple with formidable challenges, including suboptimal performance in noisy
environments, impracticality in situations requiring silence, and
susceptibility to inadvertent activations when others speak nearby. These
challenges, however, can potentially be surmounted through the cost-effective
fusion of voice and lip movement information. Consequently, we propose a novel
vocal-echoic dual-modal keyword spotting system designed for XR headsets. We
devise two different modal fusion approches and conduct experiments to test the
system’s performance across diverse scenarios. The results show that our
dual-modal system not only consistently outperforms its single-modal
counterparts, demonstrating higher precision in both typical and noisy
environments, but also excels in accurately identifying silent utterances.
Furthermore, we have successfully applied the system in real-time
demonstrations, achieving promising results. The code is available at
https://github.com/caizhuojiang/VE-KWS.
Enhancing Speech Interaction in Extended Reality with a Vocal-Echoic Dual-Modal Keyword Spotting System
In the field of Extended Reality (XR), speech interaction plays a crucial role in providing a natural and intuitive user experience. However, traditional vocal speech keyword spotting systems face several challenges that hinder their performance and usability in XR environments. These challenges include suboptimal performance in noisy surroundings, impracticality in situations where silence is required, and susceptibility to inadvertent activations when others speak nearby.
To overcome these limitations, a novel solution has been proposed – a vocal-echoic dual-modal keyword spotting system designed specifically for XR headsets. By combining voice and lip movement information, this system aims to achieve more accurate and reliable speech recognition in diverse scenarios.
The multi-disciplinary nature of this concept becomes apparent when we consider the various components involved. On one hand, there is the domain of multimedia information systems, which deals with the processing and analysis of different types of media, including speech and visual data. On the other hand, we have Animations, Artificial Reality, Augmented Reality, and Virtual Realities, all of which contribute to the immersive XR experience.
In this research, two different modal fusion approaches were devised and tested through experiments. The results demonstrate that the vocal-echoic dual-modal system consistently outperforms its single-modal counterparts. It exhibits higher precision in typical and noisy environments while also excelling in accurately identifying silent utterances.
One notable aspect of this system is its real-time applicability. Real-time demonstrations have been successfully conducted, showcasing the system’s potential for practical use. This opens up possibilities for integrating the vocal-echoic dual-modal keyword spotting system into XR applications, enabling more seamless and reliable speech interaction.
The availability of the code on GitHub (https://github.com/caizhuojiang/VE-KWS) further enhances the research’s accessibility and promotes collaboration and further innovation in the field.
In conclusion, the development of a vocal-echoic dual-modal keyword spotting system for XR headsets holds significant promise in enhancing speech interaction within Extended Reality. The fusion of voice and lip movement information addresses the challenges faced by traditional vocal speech keyword spotting systems, leading to improved performance and usability. As the field of XR continues to evolve, advancements in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities will play a crucial role in shaping the future of immersive user experiences.
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by jsendak | Jan 29, 2024 | Computer Science
Expert Commentary on Consumer Behavior Towards Food Delivery Apps
This paper provides valuable insights into consumer behavior towards food delivery apps, focusing on attributes that play a crucial role in shaping consumer satisfaction and app usage. By analyzing the impact of restaurant variety, food packaging quality, and application design and user interface, the study highlights key factors that businesses need to address to enhance customer satisfaction, boost app engagement, and foster long-term customer loyalty.
Impact of Restaurant Variety
The study reveals that a diverse range of restaurants positively influences consumer satisfaction, leading to increased app usage. This finding emphasizes the importance of offering a wide selection of restaurants on food delivery apps, as it caters to the varied preferences of consumers. By partnering with a diverse range of establishments, food delivery apps can attract more users and provide them with a better experience, thus increasing customer satisfaction and app engagement.
Role of Food Packaging Quality
Interestingly, the study finds that the quality of food packaging does not significantly impact overall satisfaction. However, it is important to note that this does not mean businesses can neglect this aspect altogether. While it may not be a distinguishing factor for consumers, poor food packaging quality can still lead to negative experiences and impact customer satisfaction in the long run. Therefore, it is essential for food delivery apps and participating restaurants to prioritize packaging quality to ensure a consistently positive customer experience.
Significance of Application Design and User Interface
The study underscores the crucial role of application design and user interface in shaping consumer behavior. Both factors significantly influence overall satisfaction, with user-friendly interfaces attracting more users and promoting frequent app usage. Businesses operating food delivery apps should invest in intuitive and visually appealing designs, ensuring that the user interface is seamless and easy to navigate. By prioritizing application design and user interface, food delivery apps can enhance customer satisfaction, encourage app engagement, and ultimately foster long-term customer loyalty.
Implications for the Food Delivery App Market
Understanding and catering to consumer preferences in areas such as restaurant variety, food packaging quality, and application design and user interface can contribute to the success of food delivery apps in the competitive market. By addressing these attributes, businesses can differentiate themselves from competitors, gain a competitive edge, and attract a larger customer base. Moreover, by continuously analyzing consumer behavior and adapting to evolving preferences, food delivery apps can stay relevant and meet the changing needs of their users.
In conclusion, this study highlights the importance of considering attributes like restaurant variety, food packaging quality, and application design and user interface in the development and management of food delivery apps. By prioritizing these factors, businesses can enhance customer satisfaction, increase app engagement, and foster long-term customer loyalty in the competitive food delivery app market.
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by jsendak | Jan 28, 2024 | Computer Science
Effective Receptive field (ERF) plays an important role in transform coding,
which determines how much redundancy can be removed at most during transform
and how many spatial priors can be utilized to synthesize textures during
inverse transform. Existing methods rely on stacks of small kernels, whose ERF
remains not large enough instead, or heavy non-local attention mechanisms,
which limit the potential of high resolution image coding. To tackle this
issue, we propose Large Receptive Field Transform Coding with Adaptive Weights
for Learned Image Compression (LLIC). Specifically, for the first time in
learned image compression community, we introduce a few large kernel-based
depth-wise convolutions to reduce more redundancy while maintaining modest
complexity. Due to wide range of image diversity, we propose to enhance the
adaptability of convolutions via generating weights in a self-conditioned
manner. The large kernels cooperate with non-linear embedding and gate
mechanisms for better expressiveness and lighter point-wise interactions. We
also investigate improved training techniques to fully exploit the potential of
large kernels. In addition, to enhance the interactions among channels, we
propose the adaptive channel-wise bit allocation via generating channel
importance factor in a self-conditioned manner. To demonstrate the
effectiveness of proposed transform coding, we align the entropy model to
compare with existing transform methods and obtain models LLIC-STF, LLIC-ELIC,
LLIC-TCM. Extensive experiments demonstrate our proposed LLIC models have
significant improvements over corresponding baselines and achieve
state-of-the-art performances and better trade-off between performance and
complexity.
Large Receptive Field Transform Coding with Adaptive Weights for Learned Image Compression (LLIC)
This article introduces a new method for learned image compression called Large Receptive Field Transform Coding with Adaptive Weights (LLIC). It addresses the issue of effectively removing redundancy and utilizing spatial priors to synthesize textures during inverse transform.
Existing methods in learned image compression often rely on stacks of small kernels or heavy non-local attention mechanisms. However, these approaches either limit the potential for high-resolution image coding or do not have a large enough Effective Receptive Field (ERF). The authors propose a solution by introducing a few large kernel-based depth-wise convolutions to reduce more redundancy while maintaining reasonable complexity.
One notable aspect of LLIC is its multi-disciplinary nature, drawing on concepts from various fields such as transform coding, multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. This integration highlights the potential application of LLIC in these domains, as well as its potential impact on the wider field of multimedia information systems.
The authors also aim to enhance the adaptability of convolutions by generating weights in a self-conditioned manner, considering the wide range of image diversity. The large kernels in LLIC, combined with non-linear embedding and gate mechanisms, contribute to better expressiveness and lighter point-wise interactions.
To fully exploit the potential of large kernels, the authors investigate improved training techniques. This demonstrates their commitment to ensuring that LLIC achieves optimal performance and complexity trade-offs.
In addition to addressing the ERF issue, LLIC improves interactions among channels by proposing adaptive channel-wise bit allocation. This process involves generating channel importance factors in a self-conditioned manner, further enhancing the effectiveness of the proposed transform coding.
The authors evaluate the performance of LLIC by aligning the entropy model with existing transform methods. They obtain models called LLIC-STF, LLIC-ELIC, and LLIC-TCM. Extensive experiments demonstrate that these LLIC models outperform corresponding baselines and achieve state-of-the-art performance while maintaining a better trade-off between performance and complexity.
In summary, the introduction of LLIC in learned image compression represents a significant advancement in the field. By addressing the limitations of existing methods and incorporating large kernel-based convolutions, LLIC demonstrates the potential for improved image compression performance. Its multi-disciplinary nature also shows how concepts from various fields can be leveraged to further enhance multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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