Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of an Inertial Sensor to Quantify Postural Stability Assessments in Young Healthy Individuals and Individuals with Chronic Ankle Instability

Measures of postural stability are utilized in clinical and research settings and are important for prevention and rehabilitation of musculoskeletal injuries. Force-plates are often used to quantify postural stability in research settings, however due to cost and size are not readily available in clinical settings. Clinical tests of postural stability require minimal equipment and are easily implemented, but are restricted due to reliability and sensitivity. Low-cost inertial sensors may be an effective alternative to force-plates for objective postural stability assessment. However, there is limited research determining which measures and assessments are most reliable, valid and discriminatory in populations with postural stability deficits related to musculoskeletal injury. For sensor-based postural stability assessments to be implemented in clinical settings, they must be reliable, valid, and discriminatory in desired target populations. The purposes of this dissertation were to (i) establish the reliability of accelerometry measures of postural stability, (ii) establish the concurrent validity of accelerometry measures compared to force plate measures of postural stability and their ability to detect differences in task difficulty, and (iii) determine the ability of accelerometry measures to discriminate postural stability deficits in individuals with chronic ankle instability (CAI). A total of 50 young, active individuals (25 control, 25 CAI) were recruited to address the study aims. Ten accelerometry measures were extracted from a waist-worn sensor during each of ten postural stability tasks of varying difficulty (eight static, two dynamic). Force-plate data were collected concurrently. Several accelerometry measures of static and dynamic postural stability were found to be reliable within session and across three sessions in control and CAI groups. Within subject variability improved when at least three static or six dynamic trials were averaged. Static postural stability accelerometry measures showed weak (r<0.5) to strong (r≤0.75) associations with force-plate measures, while dynamic postural stability associations ranged from weak to moderate (0.5≤r<0.75). Accelerometry measures were sensitive to task difficulty and postural stability deficits in individuals with CAI. Overall, a subset of the accelerometer instrumented assessments provided reliable and valid, objective measures of postural stability. Integration with a mobile device will provide clinicians a low-cost, objective solution for postural stability assessment.
E-Health applications over 5G networks: challenges and state of the art

Nowadays, the evolution of 5G technologies has a great effect on different application varying from automotive to real-time health care applications. Wireless Body Area Network (WBAN) is considered as the prime technology utilized for health care systems. This article presents an outline of the intelligent employ of 5G technologies in a WBAN health care application. As well as it gives advantages and disadvantages of the integration of WBAN health care system along with 5G and the architecture of WBAN based on 5G technology. Additionally, the role of mm-wave in the communication among WBAN and the role of 5G in communicating WBAN to the outer world have been discussed. Finally, various 5G health care application has been illustrated.
Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector

Bidirectional Long Short-Term Memory Network (Bi-LSTM) has shown promising performance in sentiment classification task. It processes inputs as sequence of information. Due to this behavior, sentiment predictions by Bi-LSTM were influenced by words sequence and the first or last phrases of the texts tend to have stronger features than other phrases. Meanwhile, in the problem scope of Indonesian sentiment analysis, phrases that express the sentiment of a document might not appear in the first or last part of the document that can lead to incorrect sentiment classification. To this end, we propose the using of an existing document representation method called paragraph vector as additional input features for Bi-LSTM. This vector provides information context of the document for each sequence processing. The paragraph vector is simply concatenated to each word vector of the document. This representation also helps to differentiate ambiguous Indonesian words. Bi-LSTM and paragraph vector were previously used as separate methods. Combining the two methods has shown a significant performance improvement of Indonesian sentiment analysis model. Several case studies on testing data showed that the proposed method can handle the sentiment phrases position problem encountered by Bi-LSTM.
26.9 A 0.19×0.17mm2 Wireless Neural Recording IC for Motor Prediction with Near-Infrared-Based Power and Data Telemetry

Brain machine interfaces using neural recording systems [1]–[4] can enable motor prediction [5]–[6] for accurate arm and hand control in paralyzed or severely injured individuals. However, implantable systems have historically used wires for data communication and power, increasing risks of tissue damage, infection, and cerebrospinal fluid leakage, rendering these devices unsuitable for long-term implantation (Fig. 26.9.1, top). Recently, several wireless and miniaturized neural recording implants with various power and data transmission methods were proposed. References [7], [8] propose an electrocorticography (ECoG) recording system with near-field RF power transfer and bilateral communication, but the 0.5W Tx exceeds maximum exposure limits by 10x [8]. Ultrasonic telemetry can safely send more power than RF; however it requires mm-scale dimensions (0.8mm3 in [9]) due to bulky ultrasound transducers. On the other hand, near infrared (NIR) light can provide power transfer and data downlink via a photovoltaic cell (PV), and a data uplink via a light-emitting diode (LED). Dimensions can be scaled to 100s of microns [10], with [11] demonstrating a 0.0297mm2 neural recording system using a 50mW/mm2 light source $(< 1/6^{th}$ of safety limit for the brain). However, this system is limited to a single channel, and since it only has a surface electrode, it can record only surface potentials (face-down, potentially blocking the light channel) or must itself be injected into brain tissue, creating significant tissue damage and danger of bleeding. In this paper, we propose $0.74\mu \mathrm{W}, 0.19\times 0.17\mathrm{mm}^{2}1\mathrm{C}$ designed for a wireless neural recording probe. It computes so-called spiking band power (SBP) [5], [12] on-chip to save 920x power while maintaining accurate finger position and velocity decoding.
Energy Cost Optimization of Hybrid Renewables Based V2G Microgrid Considering Multi Objective Function by Using Artificial Bee Colony Optimization

The worldwide demand for reduction of CO2 pollution, with more penetration of renewable energy sources and an increased number of electric vehicles (EVs), demonstrates the importance of economic dispatch (ED) with taking into account the reduction of CO2 emission. ED is a classical problem in which EVs impose more penetration as a dynamic load, and its impact as vehicle-to-grid (V2G) is the possible future trend with cost minimization. Based on the integration of EVs and hybrid renewable sources concerning both economic dispatch and pollution minimization, the multi-objective function is converted into a single comprehensive objective by using the judgment matrix methodology. In this paper, the investigation involves the minimization of the cost of all three objectives viz. operation cost, pollution cost, and carbon emissions with ED by incorporating V2G technology. The algorithms which include particle swarm optimization, as well as artificial bee colony, are applied under various operation and control strategies. The proposed models are verified and analyzed with different case studies. In terms of operation economics, the simulation results validate the superior performance of EVs based microgrid (MG) model in the coordinated charging and discharging mode. Further, the comparison of both algorithms shows better results with the ABC algorithm in terms of cost minimization of all objectives. ABC is better in V2G based microgrid with coordinated charging and discharging mode while its performance is significant during a large number of EVs (i.e., 700 EVs). Moreover, the load shedding scenarios are integrated which enables the MG system to operate in dual mode (i.e., seamless transition). In this paper, the main contribution involves penetration of EVs as dynamic load and its V2G impact in a coordinated or uncoordinated way, application of ABC algorithm for this particular load problem with improved results, and inclusion of short-term load shedding scenarios.
A Two-Port SRAM Cell with Improved Write-Ability and Reduced Leakage Current for Signal Processing Applications

In this paper, a novel two-port seven-transistor (7T) static random access memory (SRAM) cell incorporates assist circuitries is proposed. Among them, the read/write control circuit is employed to process the memory cell failures. During a write operation, this circuit is activated to connect a diode-connected transistor to the source of the drive transistor located close to the write bit line (WBL). Hence, it can provide an efficient solution to the writing ‘1’ issue to improve write operations in this manner. Furthermore, during a read operation, a two-phase reading mechanism is employed to enhance the reading speed and thus avoid unwanted power dissipation. Besides, a pre-charging circuit is designed to pull up the bit line of a selected column to the voltage level VDD before the read or write operation. Finally, with the standby start-up circuit design, the SRAM cell can quickly switch to the standby mode, and thus reduce leakage current in standby mode. Simulation results for the proposed cell design confirm that the proposed cell provides an efficient solution to the writing ‘1’ issue and leakage current reduction.
A Novel Rate Control Method for Still Image Coding

The still image coding encoders, such as JPEG image and intra frame in video coding, are highly required a specific frame size control method to support the various application. In this paper, a novel rate control method is proposed to control the frame size for different still image encoders. The gradient-based complexity is considered to construct a general Rate-Quantization (R-Q) model at block level. Then the specific quantization parameters (QP) can be generated for a single frame/image encoding with the R-Q model. Experimental results show that the proposed method achieves up to 94.5% and 99.75% bit match accuracy on JPEG encoder and x264 encoder, respectively.
Pseudonym Authentication on Network Layer in Information-Centric Networks*

The current data-centric security architecture for the Information Centric Network (ICN) does not provide efficient user authentication mechanisms, thus causing undesirable issues such as interest flooding a ttacks (IFA). I no rder to address them, this paper proposes a pseudonym authentication scheme on network layer based on the system of identity-based cryptography (IBC). In the proposed approach, each consumer needs to be authenticated before sending Interest packets into the network. After successful authentication, the consumer will be assigned a temporary nickname to be used in later data request. During the procedure, IBC is adopted to protect real names of consumers and verify the interest origin. As a result, this approach can complete user authentication and protect his real identity simultaneously. The proposed approach is case studied in IFA depression and expected results are achieved.
13.4 A 22nm 1Mb 1024b-Read and Near-Memory-Computing Dual-Mode STT-MRAM Macro with 42.6GB/s Read Bandwidth for Security-Aware Mobile Devices

Many security-aware mobile devices, using the secure hash algorithm (SHA) or the advanced encryption standard (AES) for data encryption, require short read-access time (tAC) and wide-IO from nonvolatile memory (NVM) for high-read bandwidth and SHA/AES shift/rotate functions. STT-MRAM is the major on-chip NVM for advanced process nodes [2]–[6]; however, it requires small-offset sense amplifiers (SAs) for robust reads, against a small TMR-ratio, at the expense of large area overhead and read energy (ERD). As Fig. 13.4. 1 shows, designing STT-MRAM macros for security-related applications imposes three main challenges. (1) Using a large number of SAs for wide parallel-IO readout to achieve a short tAC, but this results in a high peak current lPEAK and a large area overhead. Using fewer SAs for sequential wide-IO readout reduces lPEAK and area overhead, but imposes long tAC and a low read bandwidth (BWR). (2) MRAM macros with a high lPEAK degrade the supply (VDD) integrity of the chip, often leading to failure in noise-sensitive blocks on the same chip. (3) A conventional memory-logic-separated scheme imposes a long latency (2 cycles: wide-IO memory read + flip-flop (FF) shift/rotate) for NVM-based security logic operations. This paper presents a multibit current-mode SA (MB-CSA) for a high BWR with a short tAC and a low lPEAK. Also presented is a near-memory computing (NMC) unit with a 1-cycle access, to speed up computing for security applications. This work resulted in a 22nm 1 Mb STT-MRAM macro with dual-mode operations: wide-IO memory and NMC. The proposed 1 Mb macro demonstrates the largest number of data-out operations (1024b) with a tAC of 275ns using a 0.85V supply. In memory mode, this device outperformed all reported NVM macros in terms of BWR (42.67GB/s) and ERD(0.23pJ/b. This work also presents the first MRAM macro with NMC functionality, a 33.3% reduction in logic area, and only a 170ps latency, after NVM access, for 1 b shift/rotate operations.
Improved Bilingual Sentiment Analysis Lexicon Using Word-level Trigram

In social networks, Senegalese citizen freely express their opinions using French and Wolof Languages, which often leads to a lot of spelling mistakes in their writing. Furthermore, the complexity of Wolof language in orthography and French language in grammar and conjugation makes the analysis of Senegalese citizen’s opinions more challenging. FWLSA-score [1], has been recently proposed as an effective solution for overcoming the complexities of this bilingual sentiment analysis. This solution was based on word-level similarity. But, it does not deal perfectly with verbs’ conjugation or some words’ declination in both languages. This paper proposes an improvement of FWLSA-score [1] to address these issues. The new contribution is based on the word-level trigrams (list of consecutive three letters). Experimental results conducted on the same data [2], show that it successfully predicts sentiment polarity with an accuracy of 96.83% (implying an increase of 5.93% compared to the previous work).
5.1 A 240×192 Pixel 10fps 70klux 225m-Range Automotive LiDAR SoC Using a 40ch 0.0036mm2 Voltage/Time Dual-Data-Converter-Based AFE

A safe and reliable self-driving system is a key enabling technology for a society without traffic jams or accidents; LiDAR plays an essential role for such systems. To ensure higher levels of safety and comfort, early detection of small objects (e.g., debris/children) is crucial. To achieve this, state-of-the-art LiDARs [1-3] must attain even more finely scaled pixel resolution: for example, a 0.1-degree angle resolution (a 2x finer resolution than [3]) is required to detect a 20×20cm2 object 100m away. However, hybrid LiDAR systems [3] require a pair of TDC/ADC AFEs per pixel to obtain both precise short-range (SR) distance measurement (DM) and 200m long-range (LR) DM. Scaling the pixel resolution will significantly enlarge the SoC area and explode its cost.
10.3 A 12nm CMOS RF Transceiver Supporting 4G/5G UL MIMO

With the explosion of online streaming, artificial intelligence and mobile computing, higher wireless data-rates are required, which in 5G NR are achieved by adopting massive MIMO and Carrier Aggregation (CA) with a wide channel bandwidth (BW) up to 200MHz. Together with the requirement for LTE+NR Dual Connectivity (EN-DC), the transceiver (XCVR) and RF front-end complexity increases dramatically requiring new techniques to maintain a small and cost-effective RF implementation. In this work, a 12nm CMOS 2/3/4/5G multi-RAT (Radio Access Technology) XCVR is presented, capable of supporting up to 6 inter-band CA DL with flexible 4×4 MIMO port selection and 2 inter-band CA UL with coherent 2×2 MIMO. In NR 200MHz 4×4 MIMO 256-QAM mode a throughput up to 5Gb/s can be achieved.
AIK-Means: A Novel Clustering Method for Single-Cell RNA Sequencing Data

Single-cell RNA sequencing data provides abundant information for biologist to understand unknown life mystery. Instead of microarray data from bulk cells with their mean values, single cell expression data is independent between genes which enables specific research. For large gene expression data sets analysis, an important step is clustering genes or cells into several modules according to biological functions or biology processes. Although there are many clustering methods have been proposed even some of them specified at single cell expression data sets, low accuracy and robustness are still problems to be solved. In addition, initial parameter selection will impact the method effect. Here, we present AIK-means, an integrated clustering algorithm based on k-means aiming to improve clustering accuracy with high stability. The proposed method can also choose parameter cluster k and initial centers automatically by using few prior knowledge information. Compared to several relevant tools, AIK-means performed well in published single cell dataset.
Surface Defect Segmentation with Multi-column Patch-Wise U-net

Surface defect segmentation of the production plays an important role on the performance in industry. However, training the model by deep learning methods on industrial dataset with high resolution and few images is computationally impossible. Therefore, in this situation, we train a patch-wise fully convolutional neural network to perform similar to related image-wise methods. On this object segmentation task, we propose a method in inferring object segmentation by leveraging only ellipses annotation roughly indicating the defective area. This problem is viewed as a kind of weakly-supervised segmentation task, it can be solved by the approach that combines deep convolutional neural networks with the Multiple Instance Learning (MIL) framework. In view of this, we add the global pooling layer that called MIL-layer behind the last convolutional layer to compute the average class probability of all pixels in every patch. Finally, we combine three of this fully convolutional neural network with MIL-layer to become MIL-layer, which can utilize filters with receptive fields of different sizes. To address defect/non-defect class imbalance problem, we weight the loss contribution of false negative and false positive examples by our Weighted SoftMax loss function. The proposed network is evaluated on the detection of industrial inspection in the dataset of DAGM2007. Compared with some typical fully-supervised segmentation methods, our method achieves competitive accuracy (99.2%) but only requires easily ellipse annotations.
A More Accurate Delay Model based Task Scheduling in Cellular Edge Computing Systems

This paper studies task scheduling for delay minimization in cellular edge computing systems. Existing works depend on the delay model implied by Little’s Law. However, that model could be inaccurate. This work designs scheduling algorithm without using Little’s Law. Firstly, a more accurate delay model for cellular edge computing systems is established. Then, a Lyapunov function of delay is defined based on which a novel task scheduling algorithm is proposed. Simulation results show that the proposed scheduling algorithm can further reduce the delay compared with the traditional scheduling algorithm. Specifically, the proposed scheduling algorithm can achieve maximum delay reduction of 55% for light-traffic users.
Hierarchical Clustering and Regression Classification based Review analysis on Movie based Applications

In recent year sentiment analysis plays an important role in the opinion mining where the research on the analysis is mainly focused on the extraction of opinions and their emotions towards a particular application in the web. This paper presents review analysis of movie from various movie based applications. This paper concentrates more on the fundamental aspects of sentimental analysis which includes different methods of analyzing the reviews including data extraction, clustering and classification. In this paper extraction of the reviews from various movie based applications like IMDB, Pay tm etc.,, can be taken and formed the Movie dataset, This Movie API consists of movie name, comments, reviews, rating, emoji’s etc., In this paper first data pre-processing techniques is applied to clean the dataset and then apply the hierarchical clustering for the attributes taken and then regression based classification on the attributes to form a cluster based on negative and the positive reviews provided by the different users and finally determining the accuracy using proposed hierarchical clustering and regression technique with existing decision tree technique and the accuracy of proposed technique more accuracy is achieved.
An Adaptive Task Scheduling System Based on Real-time Clustering and NetFlow Prediction

With more and more enterprise users begin to adapt cloud computing service as their target platform for important business activity, the Cloud Computing Platform (CCP) is facing severe loading and stability problems. As a result, an excellent task scheduling system is much needed. To build such a system, the widely used method is to design detailed plan manually based on each task. However, this method has many disadvantages. Firstly, people have to add new task and rearrange them every time new user comes, with too much time wasted. Secondly, changing environment like time, task numbers may also influence the final running results. In this paper, we creatively introduce an adaptive task scheduling system which is driven by history user data [1]. By collecting these data, we conduct clustering at intervals of time to achieve the latest user task size classification. We also predict the netflow amount for next few hours, based on historical time series. Later, we arrange the order of current tasks dynamically through a weighted scoring mechanism. Experiments result on the real-life dataset demonstrate the superiority of our proposed method over state-of-the-art method.
An AES-Based Secure Image Retrieval Scheme Using Random Mapping and BOW in Cloud Computing

With the rapid growth of the number of images, many content-based image retrieval methods have been extensively used in our daily life. In general, image retrieval services are very expensive in terms of computing and storage. Therefore, outsourcing services to the cloud server is a good choice for image owners. However, privacy protection can become a big issue for image owners because the cloud server can only be semi-trusted. In this paper, we propose a novel image retrieval scheme. It is a ciphertext image retrieval method based on random mapping features with the bag-of-words model. After encrypting the image with Advanced Encryption Standard and block permutation, the cloud server generates random templates and then extracts the local features. All local features are clustered by $k$ -means algorithm to form the visual word. The histogram of encrypted visual words is constructed in this way as the feature vector to represent each image. The similarity between images can be measured by the distance between feature vectors on the cloud server. Experiments and analysis prove the effect of the scheme.