Mobile Job Title: Mobile App Developer – Bluetooth + Camera + Offline Database (Prototype)

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in Android, Flutter, Mobile App Development, React Native, iPhone, SQLite As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@AwaisChaudhry

Hello, I can build this spectrometer prototype in Flutter, connecting to the ESP32-S3 through BLE, issuing the START_SCAN command, validating the 18-channel response, and handling connection states clearly. The workflow can combine the Raman spectrum captured through the camera with the incoming NIR data, then compare both against a local offline database using cosine similarity or Euclidean distance. I will structure the app for reliable offline operation: bundled CSV/reference data, local scan-history persistence, repeatable matching logic, and an uncomplicated Green genuine / Red fake result screen based on your mockups. Image Processing can be implemented initially around the Raman extraction method defined by your protocol and sample images, while leaving the architecture ready for later on-device ML improvements. I will also document the BLE message flow and matching thresholds so the prototype is easy to test with your hardware and refine from feedback. The provided protocol document, UI screenshots, and reference datasets should allow the first working flow to be developed efficiently. What exact Raman image format and extraction method should the prototype use, and how are the Green/Red matching thresholds defined for the reference database? Best regards, Muhammad Awais

@om26er

Hello, i have command on developing BLE scanner apps. i am mobile application developer with more than 8 years of experience in developing mobile apps together with backend services. i have developed many business solutions from scratch until store-ready applications. Till now, i have completed more than 550 projects successfully for different clients. i am very confident that i can provide you best quality software service, on time and within your budget. Let's chat to proceed.

@murtaza1122

Hi, I have esp32 with me and worked on several BLE apps. I have also deployed tflite and ncnn models on edge devices. Lets discuss in detail.

@c0909h1179

The tricky part of this prototype isn't the UI — it's making BLE reliable with an ESP32-S3 while matching 18 NIR channels plus a camera-captured Raman image against a local reference set fast enough to feel instant. I've shipped 80+ apps since 2008 across Flutter, React Native and native iOS/Android, and my real-time work (WebSocket platforms with state machines and recoverable execution flows) maps directly to handling flaky BLE connects, retries and partial packet reads. Approach: 1) implement the BLE layer against your protocol doc — scan, connect, START_SCAN, parse 18 floats, with reconnect handling; 2) import the NIST/OpenSpecy CSVs into Hive/SQLite and add normalized cosine-similarity plus Euclidean scoring with a tunable threshold; 3) wire camera capture, Green/Red result screen and local scan history from your mockups, delivered via TestFlight and an internal APK. Is a physical ESP32-S3 unit available for testing, or should I build a BLE simulator first?

@rahuliitm69

Hi Sir, I’d be happy to build this spectrometer prototype in Flutter. I’ve reviewed the BLE protocol you shared and understand the communication flow with the ESP32-S3, including the TMT_DEVICE advertising name, BLE service, command/data/status characteristics, and the defined scan sequence. The BLE implementation can follow your documented protocol directly: the app will connect to TMT_DEVICE, subscribe to the Data Characteristic before initiating a scan, send the 0x01 START_SCAN command, receive the 21-byte notification, validate the 0xAA start byte and XOR checksum, and extract the 18 NIR channel values along with battery information. I’ll also handle the 5-second timeout and device status/error conditions defined in the protocol. After receiving the spectral data, I can implement the camera flow for Raman spectrum capture and the offline comparison against your provided reference database, using cosine similarity or Euclidean distance as appropriate. The scan history will be stored locally, allowing the prototype to operate without an internet connection. The overall flow will follow the sequence specified in your document, from BLE connection and scanning through Raman capture, database comparison, and the final Green/Red result. Best, Rahul

@DrSyafiq

The brief is clear; I’d prove the critical path first, then expand from working evidence. For a Mobile Job Title: Mobile App Developer – Bluetooth + Camera + Offline Database (Prototype), the hard part is balancing sensor scanning accuracy, battery life, Bluetooth/SIM fallback, and OTA-ready embedded reliability. My approach would be to validate the sensor matrix and power budget first, then design the acquisition PCB, embedded firmware, connectivity fallback, and OTA/admin controls in staged prototypes. Quick question: Do you already have a preferred OTP provider and retry/fraud policy, or should that be designed with the delivery? Also, do you already have the API and screen designs, or should the first milestone define the end-to-end mobile flow? Best, Dr. Syafiq

@Hammadhassan21

Howdy, The real challenge here is making BLE communication, 18 channel data capture, Raman imaging and offline matching feel like one seamless scan instead of four separate technical features. I can build the prototype around your ESP32 S3 protocol and spectral CSV database, with local scan history and a clear genuine/fake result flow that works even without internet. I can use Flutter or React Native with BLE communication and SQLite/Hive for the offline data layer. The matching engine can use cosine similarity or Euclidean distance against the reference data, while keeping the image processing layer modular for future TensorFlow Lite or Raman extraction integration. I can also review your existing reference and supplied UI mockups and BLE protocol document so the implementation stays aligned with the intended workflow. I can show you similar mobile/BLE work in chat and, after reviewing the protocol and sample data, confirm the implementation plan within your 3 to 6 week target. Portfolio: https://www.freelancer.com/u/Hammadhassan21 Best regards, Hammad Hassan

@gotucat

Hi, I reviewed the project and understand the main requirement: a mobile prototype must connect to an ESP32-S3 spectrometer, capture camera data, compare results offline, and show genuine or fake clearly. I can build the app with Flutter or React Native, implementing iPhone BLE communication, the START_SCAN command, reception of 18 spectral values, camera capture, and SQLite-based local storage. I’ll use the provided BLE protocol, mockup, and CSV reference data to apply cosine similarity or Euclidean distance, display Green or Red results, and save scan history for offline use. I’ll keep the prototype clean, responsive, and easy to test across the BLE and database workflow, with clear progress updates throughout the 3-6 week timeline. Let’s discuss here now.

@gutibs

Hi! This is something we can take on — the on-device signal processing angle makes it more interesting than a typical BLE prototype, and it's a good fit for what we do. Before I commit to scope, the piece I need to understand is the image side. Matching the 18-channel NIR data against your CSV reference database using cosine similarity is well-defined. The Raman spectrum image is less so: what the app needs to extract from that image — whether it's a simple pixel-column average along a known ROI, or something that has to handle variable alignment and lighting conditions — changes the complexity considerably. Do you have a reference implementation or algorithm in mind for that extraction step, or is that part still open? On how we'd build it: React Native with Expo is our recommendation — BLE library support, camera access, and SQLite all work reliably in that stack, and a single codebase covers iOS and Android. The matching logic runs fully offline against your NIST/OpenSpecy reference data, scan history is stored locally, and the BLE command flow (START_SCAN → receive 18 values → trigger camera) is straightforward to implement once we have your protocol doc. The UI mockups are useful — we'd work directly from those rather than interpret the layout ourselves. Once I understand how the image extraction is supposed to work, I can give you a realistic scope rather than pad for the unknown. Gustavo & the DoTheCode team

@pinkeegupta

Hi I have read your requirements and I am sure I will be able to help you. Please message me so that we will have detail technical discussion. I have 9+ years of combined experience in Mobile Application development, Website development, Desktop application development, 3rd party Artificial Intelligence api, AR/ VR, Chatbot, Blockchain- Cryptocurrency, CRM & ERP, Game Development and any other Software development. Please consider me and initiate a chat for further detailed discussion. Regards, Anju

@Hubready

Hello!, I am a US-based full stack/mobile developer with around 10 years of experience building Flutter/React Native apps, BLE integrations, offline databases, camera workflows, local scan history, and prototype-to-MVP mobile products. I read your project description carefully and understand the goal: a simple working prototype that connects to an ESP32-S3 over BLE, sends START_SCAN, receives 18-channel NIR data, captures a Raman spectrum image, compares both against an offline reference database, and shows a clear Green/Red result. For this build, I’d recommend Flutter with flutter_blue_plus for BLE, SQLite or Hive for local storage, camera package for image capture, and a deterministic matching module using cosine similarity/Euclidean distance first. Relevant work includes: BLE mobile app connected to ESP32 hardware Offline inspection/scoring app using local SQLite data Camera-based mobile workflow with saved history Prototype dashboard with simple pass/fail results Estimated timeline: 4–5 weeks. Preferred budget: $600 for a working prototype, depending on final mockups and CSV structure. Could you please clarify the following questions to help me better understand the project? 1. Is the ESP32 BLE firmware already complete and testable? 2. Should Raman image matching be basic prototype-level, or do you expect spectrum extraction from the image? 3. How many reference samples are in the offline CSV database? Regards, James Zappi

@gtssoftindia

Let's get started right now. Do you have a quick time to discuss in more brief about your project on a call? I assure you that you will get a secure, scalable, and 100% bug-free Android/iOS app where you will get all kinds of features. I am always looking for a long-term relationship, and I could be the one-stop solution in terms of design, development & QA, so you don't have to search for a new employee for every new project. I can spend at least 40 hours/week on your project (the project will be completed on time). I have been in this sector for like 5+ years now and am pretty experienced in creating apps with using the, Swift, React Native, Laravel, PHP, and Node.js. I am equipped to develop apps using these languages in all industries. Well-versed in in-app purchases, Machine learning, AI solutions, Dark Mode, Siri, Memoji, Real-time tracking, Multilinguality, Live Streaming, Photo/Video Editing, Voice Search, Push Notifications, User Authentication, User Profile Creation, Location, Chat, Map/social media/Payment Integration. Apart from that, I possess great familiarity with UI design principles, patterns, and best practices, Android Architecture Patterns (MVP, MVVM, MVI), REST (Retrofit, OkHttp, WebSockets), and Strong knowledge of Android SDK, different versions of Android. I look forward to the great opportunity to work with you. Best Regards

@toriquldev123

Hello There! I’m Md Toriqul Islam, an experienced full-stack and mobile developer with 10+ years of experience, and I can start your spectrometer prototype immediately. I understand you need a cross-platform app connecting to an ESP32-S3 via BLE, sending START_SCAN, receiving 18-channel spectral data, capturing Raman images, comparing both datasets against an offline database, and displaying Green/Red results with local scan history. I can implement the BLE protocol, SQLite/Hive storage, similarity matching, camera workflow, clean UI, and reliable error handling. I am skilled in React Native, mobile development, BLE, REST APIs, SQLite, databases, and Git. Please share the BLE protocol and UI mockups to begin. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@msaqibimran

Hi, I’ve reviewed your project requirements and can develop a prototype app that connects to your ESP32-S3 device, captures Raman images, and performs the necessary data comparisons in the estimated timeframe of 5–6 weeks. To ensure we align perfectly, could you clarify how you envision handling the offline database? Also, what format do you expect for the spectral data, and will there be specific requirements for the image capture from the camera? My approach would involve integrating BLE functionality for real-time data communication, implementing an efficient offline database for quick comparisons, and designing a straightforward UI based on your mockups. I have experience with Flutter and offline databases, which will help streamline the development process. Looking forward to discussing further details to finalize the scope and pricing. Best regards, Mohammad

@Fawadkaleem

With over 6 years as a reputable mobile app developer, I've mastered the fields of Android and Flutter app development. My proficiency stretches from Firebase integration to stripe payments and marketplace-style payouts. As a technologist who has built multiple apps from inception to App Store approval, I have more than just a sight on how to build your prototype–I am well-versed in what it takes to get your app swiftly approved as well. My broad-ranging skills are an ideal match for your project's requirements. My solid grasp of Flutter and React Native is a major asset for this app development. My extensive experience with Bluetooth Low Energy (BLE) and offline databases (SQLite, Hive), along with my adequate comprehension of cosine similarity or Euclidean distance, equipped me properly to work on this project. Pitch: As an added advantage, I am experienced in TensorFlow Lite which can positively impact development. Additionally, my knowledge in image processing is an invaluable skill in this field but also complements perfectly with the need for Raman spectrum extraction expertise. These combined skills could push our prototype even further while still giving us room to evolve in the instance where you require advanced ML models deployed in-app or sophisticated image processing functionalities on the Raman spectrum images captured

@invoketech

HELLO, WE HAVE HANDLED SIMILAR MOBILE APPLICATIONS INVOLVING BLUETOOTH DEVICES, OFFLINE DATA AND REAL-TIME DEVICE COMMUNICATION, AND CAN SHARE RELEVANT EXAMPLES. I have 10+ years of experience in mobile and backend development, with strong experience in React Native, device integrations, local databases and API/data processing. I can keep the first version focused on the workflow you described, with local scan history and a clear genuine/fake result, while keeping the architecture ready for TensorFlow Lite or more advanced image processing later. WORKING FLOW: BLE Connection → Connect the React Native app with the ESP32-S3 and handle device communication reliably. Scan & Data Capture → Send START_SCAN, receive the 18 spectral values and capture the Raman spectrum image. Local Matching → Process both datasets and compare them against the provided offline reference database. Result & History → Show a simple Green/Red result and securely save each scan locally for future review. Testing & Delivery → Test the BLE flow, matching logic and offline behaviour on the target Android/iOS devices and provide the complete source code. Based on the provided BLE protocol, UI mockups and sample CSV data, I can deliver the working prototype within the requested 3–6 week window. Thanks

@crtvecode

I see you need a prototype app that connects to an ESP32-S3 device via BLE and processes spectral data to determine the authenticity of drugs and food. I can build this app using Flutter or React Native, making sure it captures those Raman spectrum images and compares the spectral data against your offline database. But I can have the prototype ready for you to test first, so you can see it in action before any payment or project award, this way you only move forward when you're completely satisfied with the results. I’m ready to start immediately and can go ahead and complete this within your 3–6 week timeline for $300, looking forward to working together! Rajavel - Flutter and React Native Developer with BLE expertise.

@Proswift

Hi, I have read the BLE protocol document and I completely understand it. Clip-on spectrometer prototype: BLE to ESP32‑S3 → START_SCAN → 18 NIR channels → camera Raman capture → offline match → Green/Red, plus local scan history. Stack: React Native (or Flutter if you prefer) + BLE + SQLite/Hive for the local spectral DB. Matching via cosine similarity / Euclidean distance on your NIST/OpenSpecy CSV data. TFLite / image processing can wait unless we need it for Raman extraction in v1. Relevant work: Android/RN apps with ESP32 BLE command/response, offline storage, and sensor-style data flows (IoT + hardware control). Timeline: 4–5 weeks for a working prototype from your mockups + BLE protocol + CSV DB. Budget: $300 (within your $300–600 range). Ready when you share the BLE doc and sample CSVs. Best regards