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Type of neural network architecture designed to process sequences of input data, such as natural language text or time series data. They have been widely adopted in the field of artificial intelligence due to their ability to capture long-term dependencies in data and their strong performance on a wide range of tasks. Transformers have been used in a wide range of applications in artificial intelligence, including natural language processing, speech recognition, computer vision, and recommendation systems. They have achieved state-of-the-art performance on many benchmarks and are a key technology in the field of deep learning. The key features of transformers include: Self-attention mechanism: Transformers use a self-attention mechanism to process sequences of input data. This mechanism allows the network to focus on the most relevant parts of the input sequence at each step, capturing long-term dependencies and improving performance. Multi-head attention: Transformers use multi-head attention to process different parts of the input sequence in parallel. This allows the network to capture complex patterns in the data and improve performance. Positional encoding: Transformers use positional encoding to provide the network with information about the position of each element in the input sequence. This allows the network to capture the order of the elements and improve performance. Feedforward networks: Transformers include feedforward networks that process the output of the self-attention mechanism. These networks allow the network to capture non-linear relationships between the elements of the input sequence and improve performance. Pre-training: Transformers are often pre-trained on large amounts of data using unsupervised learning techniques such as masked language modeling or next sentence prediction. This pre-training allows the network to learn general features of the data that can be fine-tuned for specific tasks.

5G/6G Network Planning

Site selection: AI can be used to analyze data from various sources, such as satellite imagery, social media, and transportation data, to identify optimal sites for network equipment, such as base stations and antennas. AI can analyze factors such as population density, traffic patterns, and terrain to identify areas that require additional coverage or capacity. Network design: AI can be used to optimize the network design by analyzing large amounts of data and identifying patterns and correlations. AI can be used to optimize network parameters such as power settings, bandwidth allocation, and network topology to improve network performance and efficiency. Resource allocation: AI can be used to optimize the allocation of network resources, such as bandwidth and spectrum, to improve network performance and reduce congestion. AI can analyze network traffic patterns and predict demand to ensure that resources are allocated efficiently and effectively. Fault detection: AI can be used to detect faults and anomalies in the network, such as equipment failures or performance degradation. AI can analyze large amounts of data in real-time to identify patterns and deviations from normal behavior, allowing network operators to take proactive measures to address issues before they impact network performance. Feature engineering: Feature engineering is a critical part of data science in AI, as it involves selecting and transforming the most relevant features of the data to use in AI models. Predictive maintenance: AI can be used to predict equipment failures and perform proactive maintenance to reduce downtime and improve network reliability. AI can analyze data from network equipment to identify signs of equipment failure, enabling network operators to take action before equipment failure occurs.


Leveraging the power of the XGBoost library to build accurate and efficient machine learning models using: Scalability: XGBoost can handle large datasets and can be distributed across multiple cores and machines to speed up the training process. Regularization: XGBoost includes L1 and L2 regularization to prevent overfitting and improve model generalization. Tree pruning: XGBoost uses a technique called "pruning" to remove unnecessary branches from decision trees, which reduces the model's complexity and improves performance. Cross-validation: XGBoost includes built-in cross-validation functionality to help optimize hyperparameters and prevent overfitting. Compatibility: XGBoost can be used with a variety of programming languages, including Python, R, Java, and C++. XGBoost is effective for complex datasets with many features and can be fine-tuned to achieve state-of-the-art performance in many machine learning tasks.

Deployment experts

We oversee the successful implementation of software applications, systems, and services within an organization. Planning and strategizing the deployment process: work with stakeholders to understand their requirements and ensure that the deployment process is well planned and documented. This may involve creating a project plan, defining timelines, and identifying risks and dependencies. Coordinating with teams: work with different teams, including development, testing, and operations teams, to ensure that the deployment process is executed smoothly. Collaborate with them to define deployment procedures, validate the readiness of the systems, and troubleshoot any issues that arise. Managing deployment infrastructure: ensure that the infrastructure required for deployment, such as servers, databases, and network connections, is set up and maintained correctly. Work with the IT team to ensure that the infrastructure is secure, scalable, and reliable. Ensuring compliance and security: ensure that the deployed systems comply with the relevant regulations and security standards. This may involve implementing security measures such as firewalls, encryption, and access controls, and conducting security audits to identify vulnerabilities. Monitoring and optimizing performance: monitor the performance of the deployed systems and optimize them for better performance, reliability, and scalability. This may involve analyzing logs, metrics, and user feedback to identify performance bottlenecks and working with the development team to implement optimizations.

Neural RRT TSP methods

The Neural RRT TSP method has applications in various fields, including logistics, transportation, and manufacturing, where the TSP is a common problem that arises in route planning, scheduling, and resource allocation. The Neural RRT TSP method has several advantages over other TSP algorithms, including: Scalability: The algorithm can handle large TSP instances with hundreds or even thousands of cities. Flexibility: The algorithm can be easily extended to handle different TSP variants, such as the asymmetric TSP or the multiple TSP. Efficiency: The algorithm can converge to near-optimal solutions in a relatively short amount of time. Robustness: The algorithm can handle noisy or incomplete input data and still produce good solutions.


Spatial analysis: GIS can be used to analyze spatial data, such as maps and satellite imagery, while AI can be used to perform advanced analysis on this data. For example, AI can be used to identify patterns in satellite imagery data that may be difficult to detect by the human eye, such as land-use changes, vegetation density, or infrastructure damage after natural disasters. Predictive modeling: GIS and AI can be used together to create predictive models that can help organizations to make informed decisions. For example, GIS can be used to analyze spatial data to identify patterns and trends, while AI can be used to create models that can predict future outcomes based on historical data. These models can be used in a variety of industries, such as urban planning, environmental management, and transportation planning. Natural language processing: Natural language processing (NLP) is a branch of AI that is used to analyze and interpret human language. NLP can be used in GIS to analyze data from social media, news articles, and other sources to gain insights into public opinion and sentiment regarding specific locations or issues. This can be useful for a variety of purposes, such as emergency management, market research, or urban planning. Autonomous vehicles: GIS and AI can be used together to enable autonomous vehicles to navigate and operate in the real world. GIS can be used to provide detailed maps and location data, while AI can be used to provide advanced perception and decision-making capabilities. This combination of technologies is critical for enabling the widespread adoption of autonomous vehicles in industries such as transportation, logistics, and delivery. Data exploration and analysis: This involves using data visualization tools and analytics techniques to gain insights into the data, identify patterns, and uncover anomalies. It helps data scientists and AI engineers to develop more accurate and effective AI models.

Principal Python experts

We lead the development of high-quality, scalable, and maintainable software projects using Python. Responsible for ensuring that the software meets the organization's requirements and that it is delivered on time and to a high standard. Designing software architecture: work with stakeholders to understand their requirements and design the software architecture that meets those requirements. This may involve identifying appropriate Python libraries, frameworks, and tools to be used in the project, as well as defining the high-level structure of the project. Leading development efforts: lead the development team in implementing the software architecture and ensuring that the project is delivered on time and to a high standard. This may involve writing Python code myself, as well as providing guidance and feedback to other developers. Conducting code reviews: review the Python code produced by other developers, providing feedback on its quality, performance, and maintainability. I will ensure that the code follows best practices, adheres to established coding standards, and is well-documented. Identifying and solving technical problems: will be responsible for identifying technical problems that arise during development and implementing solutions to these problems. This may involve troubleshooting code issues, resolving system configuration problems, or addressing performance bottlenecks. Mentoring and training other developers: mentor and train other developers on Python best practices, coding standards, and software development processes. Help them to improve their skills and grow as developers, providing guidance and feedback along the way.

Graph CNN

Graph CNNs use graph convolutions to aggregate information from neighboring nodes in the graph. The convolutional operation is defined as a weighted sum of the feature vectors of a node and its neighboring nodes, where the weights are learned during the training process. This operation is performed recursively for all nodes in the graph, allowing the network to learn hierarchical representations of the input data. Graph CNNs are a powerful neural network architecture that can be used to process complex graph-structured data. They have a wide range of applications, including social network analysis, recommendation systems, bioinformatics, and computer vision. Some key features of Graph CNNs include: Graph representation: Graph CNNs can represent complex data structures as graphs, enabling them to process non-Euclidean data and capture local and global dependencies between nodes. Convolutional operation: Graph CNNs use a convolutional operation to aggregate information from neighboring nodes in the graph. This allows the network to learn hierarchical representations of the input data. Pooling: Graph CNNs use pooling operations to downsample the graph representation while preserving important features. This helps to reduce the computational cost of the network. Non-linearity: Graph CNNs include non-linear activation functions, such as ReLU or sigmoid, to introduce non-linearities and enable the network to learn complex relationships between nodes. Interpretability: Graph CNNs can provide interpretable results by highlighting important nodes or subgraphs in the graph.

AI Deep Learning GPU

Graphics Processing Units (GPUs) are a powerful tool for accelerating deep learning workloads. Here are some key points about AI deep learning with GPUs: Faster Training: GPUs can train deep learning models much faster than CPUs due to their parallel computing architecture. This allows deep learning models to be trained on larger datasets and more complex architectures. Improved Performance: GPUs can improve the performance of deep learning models by allowing them to handle more data and perform more computations in a shorter amount of time. This can lead to better accuracy and more efficient model training. Cloud Computing: Cloud computing services such as Amazon Web Services and Google Cloud Platform offer GPU instances for deep learning workloads. This allows users to scale up their deep learning projects without needing to invest in expensive hardware. GPU Selection: Not all GPUs are created equal. When selecting a GPU for deep learning, consider factors such as memory capacity, number of processing cores, and memory bandwidth. Tensor Processing Units (TPUs): TPUs are a type of hardware accelerator developed by Google specifically for deep learning workloads. TPUs offer even faster training speeds and are designed to work with TensorFlow, Google's popular deep learning framework.

NP-hard games experts

Game theory is the study of mathematical models of strategic interactions among rational decision-makers. NP-hard problems, on the other hand, are a class of computational problems that are believed to be computationally intractable, meaning that there is no known algorithm that can solve them efficiently. There are many games in game theory that are known to be NP-hard. One of the most well-known examples is the game of Go, which has been proven to be PSPACE-complete, a complexity class that includes all problems that can be solved by a nondeterministic Turing machine using polynomial space. Other examples of NP-hard games include various types of combinatorial games, such as chess, checkers, and Othello, as well as games with imperfect information, such as poker and bridge. While it is generally difficult to solve NP-hard games exactly, there are various techniques that can be used to approximate solutions or find good strategies. These include heuristic algorithms, Monte Carlo methods, and reinforcement learning.


We specialize in the development of advanced driver assistance systems (ADAS) and autonomous driving (AD) software. Our expertise lies in crafting cutting-edge software solutions that enhance vehicle safety, improve driving experiences, and pave the way for autonomous mobility.

Hw design

Our expertise extends to hardware design, where we specialize in creating innovative and optimized solutions for various applications. With a deep understanding of hardware architecture and industry standards, we deliver cutting-edge designs that meet performance, efficiency, and reliability requirements. Our hardware design services encompass schematic design, PCB layout, component selection, and prototyping, enabling clients to achieve their desired product functionality and performance goals. Trust us to transform your concepts into tangible, high-quality hardware solutions.


We specialize in rapid prototyping services, enabling companies to transform their ideas into tangible prototypes quickly and efficiently. Using state-of-the-art technologies and manufacturing processes, we offer end-to-end prototyping solutions, from concept design to physical realization. Our expertise covers various prototyping methods, including 3D printing, CNC machining, and electronics prototyping. With our prototyping services, you can iterate, test, and validate your product designs, accelerating the product development cycle and bringing your ideas to life with precision and quality.

ECUs development

We specialize in the development of Electronic Control Units (ECUs) for various automotive applications. Our dedicated team of engineers and experts designs and develops high-performance ECUs that cater to the specific needs of our clients. Whether it's engine control, powertrain management, infotainment systems, or advanced driver assistance systems (ADAS), we deliver customized ECUs that ensure optimal functionality, reliability, and compatibility. With our ECU development services, we help companies stay at the forefront of automotive innovation, enabling them to create advanced and efficient vehicles that meet the demands of modern technology and safety standards.

Perception Integration and verification

We specialize in perception integration and verification services, ensuring the seamless integration and reliable performance of perception systems in automotive applications. Our expert team works closely with clients to integrate perception sensors, such as cameras, lidars, and radars, into the overall vehicle architecture. Through rigorous testing and verification processes, we validate the accuracy, robustness, and functionality of perception systems. Our comprehensive approach includes real-world testing, simulation-based verification, and algorithm validation to ensure that the perception system meets the required specifications and performs optimally in various driving scenarios.

Linux, QNX, Android

We have extensive expertise in Linux, QNX, and Android operating systems, offering comprehensive solutions tailored to your specific requirements. Whether you need a Linux-based embedded system, a real-time QNX application, or an innovative Android application, our team is well-equipped to deliver high-quality solutions that meet your objectives.


We excel in comprehensive testing services, ensuring the reliability, functionality, and performance of software systems. Our dedicated testing approach encompasses rigorous quality assurance measures, including functional testing, performance testing, security testing, and compatibility testing. With our testing expertise, we help companies achieve robust and high-performing software solutions, ensuring optimal performance and user satisfaction.

Custom Android Emulator of IVI

We offer customized Android emulator solutions for In-Vehicle Infotainment (IVI) systems. Our tailored Android emulators provide a simulated environment that replicates the functionality and user experience of IVI systems, allowing developers to test and validate their applications without the need for physical hardware. With our custom Android emulator, you can efficiently develop, debug, and optimize IVI applications, ensuring seamless integration and optimal performance in automotive environments.

Development autonomous driving platform

We are at the forefront of autonomous driving platform development, providing innovative solutions that enable the realization of autonomous mobility. Our expertise lies in designing and developing cutting-edge software and hardware platforms specifically tailored for autonomous driving applications. Our platform encompasses a comprehensive suite of components, including perception systems, sensor fusion, decision-making algorithms, and control systems, all working seamlessly together to enable safe and efficient autonomous driving. With our autonomous driving platform, companies can accelerate their development process, reduce time-to-market, and unlock the potential of self-driving technologies.

Autosar, DMS, OMS

In the realm of Autosar, we provide expertise in implementing the Autosar standard for automotive software architecture, enabling seamless integration and interoperability across various electronic control units (ECUs) within a vehicle. Our team is skilled in developing Autosar-compliant software components and modules, ensuring efficient communication and coordination between different ECUs for enhanced functionality and system performance. With our Driver Monitoring Systems (DMS), we offer cutting-edge solutions that utilize advanced sensing technologies, such as cameras and sensors, to monitor driver behavior, attention, and alertness. Our DMS solutions enable real-time detection of drowsiness, distraction, and other critical factors, contributing to improved driver safety and reducing the risk of accidents. We provide Object Management Systems (OMS) solutions that facilitate efficient data exchange and communication between various vehicle components, subsystems, and external entities. Our OMS solutions ensure seamless integration and coordination of data, enabling effective data sharing, synchronization, and control in automotive systems.


Digital Signal Processing (DSP) is a key area of our expertise. We specialize in developing DSP solutions for various applications, particularly in the field of audio, communications, and image processing. Our experienced team of engineers is proficient in designing and implementing algorithms, optimizing signal processing techniques, and utilizing advanced DSP hardware and software platforms. Through our DSP solutions, we enable real-time signal analysis, enhancement, and manipulation, delivering superior audio quality, efficient data transmission, and advanced image processing capabilities. Our expertise spans areas such as audio and speech processing, digital communications, image and video processing, and sensor signal processing.

Vehicle integration

We specialize in vehicle integration services, facilitating the seamless integration of various systems and components within a vehicle. Our vehicle integration services enable the harmonious collaboration of various systems and components, leading to enhanced performance, functionality, and user experience. Whether it's electrical, network, software, or mechanical integration, our team possesses the expertise to deliver seamless integration solutions tailored to your specific vehicle requirements.